metric = load_metric("accuracy")\nmodel.eval()\nfor batch in eval_dataloader:\n batch = {k: v.to(device) for k, v in batch.items()}\n with torch.no_grad():\n outputs = model(**batch)\n\n logits = outputs.logits\n predictions = torch.argmax(logits, dim=-1)\n metric.add_batch(predictions=predictions, references=batch["labels"])\n\nmetric.compute()
Para mais exemplos de fine-tuning acesse:
\n🤗 Transformers Examples inclui scripts\npara treinas tarefas comuns de NLP em PyTorch e TensorFlow.
🤗 Transformers Notebooks contém vários notebooks sobre como aplicar o fine-tuning a um modelo\npara tarefas específicas no PyTorch e TensorFlow.
Uma AutoClass automaticamente infere a arquitetura do modelo e baixa configurações e pesos pré-treinados. Geralmente, nós recomendamos usar uma AutoClass para produzir um código independente de checkpoints. Mas usuários que querem mais contole sobre parâmetros específicos do modelo pode criar um modelo customizado 🤗 Transformers a partir de algumas classes bases. Isso pode ser particulamente útil para alguém que está interessado em estudar, treinar ou fazer experimentos com um modelo 🤗 Transformers. Nesse tutorial, será explicado como criar um modelo customizado sem uma AutoClass. Aprenda como:
A configuration refere-se a atributos específicos de um modelo. Cada configuração de modelo tem atributos diferentes; por exemplo, todos modelo de PLN possuem os atributos hidden_size, num_attention_heads, num_hidden_layers e vocab_size em comum. Esse atributos especificam o numero de ‘attention heads’ ou ‘hidden layers’ para construir um modelo.
Dê uma olhada a mais em DistilBERT acessando DistilBertConfig para observar esses atributos:
from transformers import DistilBertConfig\n\nconfig = DistilBertConfig()\nprint(config)\nDistilBertConfig {\n "activation": "gelu",\n "attention_dropout": 0.1,\n "dim": 768,\n "dropout": 0.1,\n "hidden_dim": 3072,\n "initializer_range": 0.02,\n "max_position_embeddings": 512,\n "model_type": "distilbert",\n "n_heads": 12,\n "n_layers": 6,\n "pad_token_id": 0,\n "qa_dropout": 0.1,\n "seq_classif_dropout": 0.2,\n "sinusoidal_pos_embds": false,\n "transformers_version": "4.16.2",\n "vocab_size": 30522\n}
DistilBertConfig mostra todos os atributos padrões usados para construir um DistilBertModel base. Todos atributos são customizáveis, o que cria espaço para experimentos. Por exemplo, você pode customizar um modelo padrão para:
activation.attention_dropout.my_config = DistilBertConfig(activation="relu", attention_dropout=0.4)\nprint(my_config)\nDistilBertConfig {\n "activation": "relu",\n "attention_dropout": 0.4,\n "dim": 768,\n "dropout": 0.1,\n "hidden_dim": 3072,\n "initializer_range": 0.02,\n "max_position_embeddings": 512,\n "model_type": "distilbert",\n "n_heads": 12,\n "n_layers": 6,\n "pad_token_id": 0,\n "qa_dropout": 0.1,\n "seq_classif_dropout": 0.2,\n "sinusoidal_pos_embds": false,\n "transformers_version": "4.16.2",\n "vocab_size": 30522\n}
Atributos de um modelo pré-treinado podem ser modificados na função from_pretrained():
my_config = DistilBertConfig.from_pretrained("distilbert-base-uncased", activation="relu", attention_dropout=0.4)
Uma vez que você está satisfeito com as configurações do seu modelo, você consegue salvar elas com save_pretrained(). Seu arquivo de configurações está salvo como um arquivo JSON no diretório especificado:
my_config.save_pretrained(save_directory="./your_model_save_path")
Para reusar o arquivo de configurações, carregue com from_pretrained():
my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
Você pode também salvar seu arquivo de configurações como um dicionário ou até mesmo com a diferença entre as seus atributos de configuração customizados e os atributos de configuração padrões! Olhe a documentação configuration para mais detalhes.
O próximo passo é criar um model. O modelo - também vagamente referido como arquitetura - define o que cada camada está fazendo e quais operações estão acontecendo. Atributos como num_hidden_layers das configurações são utilizados para definir a arquitetura. Todo modelo compartilha a classe base PreTrainedModel e alguns métodos em comum como redimensionar o tamanho dos embeddings de entrada e podar as ‘self-attention heads’. Além disso, todos os modelos também são subclasses de torch.nn.Module, tf.keras.Model ou flax.linen.Module. Isso significa que os modelos são compatíveis com cada respectivo uso de framework.
Carregar seus atributos de configuração customizados em um modelo:
\n\n\tfrom transformers import DistilBertModel\n\nmy_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")\nmodel = DistilBertModel(my_config)
Isso cria um modelo com valores aleatórios ao invés de pré-treinar os pesos. Você não irá conseguir usar usar esse modelo para nada útil ainda, até você treinar ele. Treino é um processo caro e demorado. Geralmente é melhor utilizar um modelo pré-treinado para obter melhores resultados mais rápido, enquanto usa apenas uma fração dos recursos necessários para treinar.
\nCriar um modelo pré-treinado com from_pretrained():
model = DistilBertModel.from_pretrained("distilbert-base-uncased")
Quando você carregar os pesos pré-treinados, a configuração padrão do modelo é automaticamente carregada se o modelo é provido pelo 🤗 Transformers. No entanto, você ainda consegue mudar - alguns ou todos - os atributos padrões de configuração do modelo com os seus próprio atributos, se você preferir:
\n\n\tmodel = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
Carregar os seus próprios atributos padrões de contiguração no modelo:
\n\n\tfrom transformers import TFDistilBertModel\n\nmy_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")\ntf_model = TFDistilBertModel(my_config)
Isso cria um modelo com valores aleatórios ao invés de pré-treinar os pesos. Você não irá conseguir usar usar esse modelo para nada útil ainda, até você treinar ele. Treino é um processo caro e demorado. Geralmente é melhor utilizar um modelo pré-treinado para obter melhores resultados mais rápido, enquanto usa apenas uma fração dos recursos necessários para treinar.
\nCriar um modelo pré-treinado com from_pretrained():
tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
Quando você carregar os pesos pré-treinados, a configuração padrão do modelo é automaticamente carregada se o modelo é provido pelo 🤗 Transformers. No entanto, você ainda consegue mudar - alguns ou todos - os atributos padrões de configuração do modelo com os seus próprio atributos, se você preferir:
\n\n\ttf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
Neste ponto, você tem um modelo básico do DistilBERT que gera os estados ocultos. Os estados ocultos são passados como entrada para a head do moelo para produzir a saída final. 🤗 Transformers fornece uma head de modelo diferente para cada tarefa desde que o modelo suporte essa tarefa (por exemplo, você não consegue utilizar o modelo DistilBERT para uma tarefa de ‘sequence-to-sequence’ como tradução).
\nPor exemplo, DistilBertForSequenceClassification é um modelo DistilBERT base com uma head de classificação de sequência. A head de calssificação de sequência é uma camada linear no topo das saídas agrupadas.
from transformers import DistilBertForSequenceClassification\n\nmodel = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
Reutilize facilmente esse ponto de parada para outra tarefe mudando para uma head de modelo diferente. Para uma tarefe de responder questões, você usaria a head do modelo DistilBertForQuestionAnswering. A head de responder questões é similar com a de classificação de sequências exceto o fato de que ela é uma camada no topo dos estados das saídas ocultas.
from transformers import DistilBertForQuestionAnswering\n\nmodel = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
Por exemplo, TFDistilBertForSequenceClassification é um modelo DistilBERT base com uma head de classificação de sequência. A head de calssificação de sequência é uma camada linear no topo das saídas agrupadas.
from transformers import TFDistilBertForSequenceClassification\n\ntf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
Reutilize facilmente esse ponto de parada para outra tarefe mudando para uma head de modelo diferente. Para uma tarefe de responder questões, você usaria a head do modelo TFDistilBertForQuestionAnswering. A head de responder questões é similar com a de classificação de sequências exceto o fato de que ela é uma camada no topo dos estados das saídas ocultas.
from transformers import TFDistilBertForQuestionAnswering\n\ntf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
A útlima classe base que você precisa antes de usar um modelo para dados textuais é a tokenizer para converter textos originais para tensores. Existem dois tipos de tokenizers que você pode usar com 🤗 Transformers:
\nPreTrainedTokenizer: uma implementação em Python de um tokenizer.PreTrainedTokenizerFast: um tokenizer da nossa biblioteca 🤗 Tokenizer baseada em Rust. Esse tipo de tokenizer é significantemente mais rapido - especialmente durante tokenization de codificação - devido a implementação em Rust. O tokenizer rápido tambem oferece métodos adicionais como offset mapping que mapeia tokens para suar palavras ou caracteres originais.Os dois tokenizers suporta métodos comuns como os de codificar e decodificar, adicionar novos tokens, e gerenciar tokens especiais.
\n\n\nNem todo modelo suporta um ‘fast tokenizer’. De uma olhada aqui table pra checar se um modelo suporta ‘fast tokenizer’.
Se você treinou seu prórpio tokenizer, você pode criar um a partir do seu arquivo vocabulary:
\n\n\tfrom transformers import DistilBertTokenizer\n\nmy_tokenizer = DistilBertTokenizer(vocab_file="my_vocab_file.txt", do_lower_case=False, padding_side="left")
É importante lembrar que o vocabulário de um tokenizer customizado será diferente de um vocabulário gerado pelo tokenizer de um modelo pré treinado. Você precisa usar o vocabulário de um modelo pré treinado se você estiver usando um modelo pré treinado, caso contrário as entradas não farão sentido. Criando um tokenizer com um vocabulário de um modelo pré treinado com a classe DistilBertTokenizer:
from transformers import DistilBertTokenizer\n\nslow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
Criando um ‘fast tokenizer’ com a classe DistilBertTokenizerFast:
from transformers import DistilBertTokenizerFast\n\nfast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
Pos padrão, AutoTokenizer tentará carregar um ‘fast tokenizer’. Você pode disabilitar esse comportamento colocando use_fast=False no from_pretrained.
Um extrator de features processa entradas de imagem ou áudio. Ele herda da classe base FeatureExtractionMixin, e pode também herdar da classe ImageFeatureExtractionMixin para processamento de features de imagem ou da classe SequenceFeatureExtractor para processamento de entradas de áudio.
Dependendo do que você está trabalhando em um audio ou uma tarefa de visão, crie um estrator de features associado com o modelo que você está usando. Por exemplo, crie um ViTFeatureExtractor padrão se você estiver usando ViT para classificação de imagens:
from transformers import ViTFeatureExtractor\n\nvit_extractor = ViTFeatureExtractor()\nprint(vit_extractor)\nViTFeatureExtractor {\n "do_normalize": true,\n "do_resize": true,\n "feature_extractor_type": "ViTFeatureExtractor",\n "image_mean": [\n 0.5,\n 0.5,\n 0.5\n ],\n "image_std": [\n 0.5,\n 0.5,\n 0.5\n ],\n "resample": 2,\n "size": 224\n}
Se você não estiver procurando por nenhuma customização, apenas use o método from_pretrained para carregar parâmetros do modelo de extrator de features padrão.
Modifique qualquer parâmetro dentre os ViTFeatureExtractor para criar seu extrator de features customizado.
from transformers import ViTFeatureExtractor\n\nmy_vit_extractor = ViTFeatureExtractor(resample="PIL.Image.BOX", do_normalize=False, image_mean=[0.3, 0.3, 0.3])\nprint(my_vit_extractor)\nViTFeatureExtractor {\n "do_normalize": false,\n "do_resize": true,\n "feature_extractor_type": "ViTFeatureExtractor",\n "image_mean": [\n 0.3,\n 0.3,\n 0.3\n ],\n "image_std": [\n 0.5,\n 0.5,\n 0.5\n ],\n "resample": "PIL.Image.BOX",\n "size": 224\n}
Para entradas de áutio, você pode criar um Wav2Vec2FeatureExtractor e customizar os parâmetros de uma forma similar:
from transformers import Wav2Vec2FeatureExtractor\n\nw2v2_extractor = Wav2Vec2FeatureExtractor()\nprint(w2v2_extractor)\nWav2Vec2FeatureExtractor {\n "do_normalize": true,\n "feature_extractor_type": "Wav2Vec2FeatureExtractor",\n "feature_size": 1,\n "padding_side": "right",\n "padding_value": 0.0,\n "return_attention_mask": false,\n "sampling_rate": 16000\n}
Para modelos que suportam tarefas multimodais, 🤗 Transformers oferece uma classe processadora que convenientemente cobre um extrator de features e tokenizer dentro de um único objeto. Por exemplo, vamos usar o Wav2Vec2Processor para uma tarefa de reconhecimento de fala automática (ASR). ASR transcreve áudio para texto, então você irá precisar de um extrator de um features e um tokenizer.
Crie um extrator de features para lidar com as entradas de áudio.
\n\n\tfrom transformers import Wav2Vec2FeatureExtractor\n\nfeature_extractor = Wav2Vec2FeatureExtractor(padding_value=1.0, do_normalize=True)
Crie um tokenizer para lidar com a entrada de textos:
\n\n\tfrom transformers import Wav2Vec2CTCTokenizer\n\ntokenizer = Wav2Vec2CTCTokenizer(vocab_file="my_vocab_file.txt")
Combine o extrator de features e o tokenizer no Wav2Vec2Processor:
from transformers import Wav2Vec2Processor\n\nprocessor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
Com duas classes básicas - configuração e modelo - e um preprocessamento de classe adicional (tokenizer, extrator de features, ou processador), você pode criar qualquer modelo que suportado por 🤗 Transformers. Qualquer uma dessas classes base são configuráveis, te permitindo usar os atributos específicos que você queira. Você pode facilmente preparar um modelo para treinamento ou modificar um modelo pré-treinado com poucas mudanças.
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\nEste guia mostrará como realizar o fine-tuning do DistilBERT no conjunto de dados IMDb para determinar se a crítica de filme é positiva ou negativa.
\n\n\nConsulte a página de tarefas de classificação de texto para obter mais informações sobre outras formas de classificação de texto e seus modelos, conjuntos de dados e métricas associados.
Carregue o conjunto de dados IMDb utilizando a biblioteca 🤗 Datasets:
\n\n\tfrom datasets import load_dataset\n\nimdb = load_dataset("imdb")
Em seguida, dê uma olhada em um exemplo:
\n\n\timdb["test"][0]\n{\n "label": 0,\n "text": "I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \\"Gene Roddenberry's Earth...\\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.",\n}
Existem dois campos neste dataset:
\ntext: uma string contendo o texto da crítica do filme.label: um valor que pode ser 0 para uma crítica negativa ou 1 para uma crítica positiva.Carregue o tokenizador do DistilBERT para processar o campo text:
from transformers import AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
Crie uma função de pré-processamento para tokenizar o campo text e truncar as sequências para que não sejam maiores que o comprimento máximo de entrada do DistilBERT:
def preprocess_function(examples):\n return tokenizer(examples["text"], truncation=True)
Use a função map do 🤗 Datasets para aplicar a função de pré-processamento em todo o conjunto de dados. Você pode acelerar a função map definindo batched=True para processar vários elementos do conjunto de dados de uma só vez:
tokenized_imdb = imdb.map(preprocess_function, batched=True)
Use o DataCollatorWithPadding para criar um batch de exemplos. Ele também preencherá dinamicamente seu texto até o comprimento do elemento mais longo em seu batch, para que os exemplos do batch tenham um comprimento uniforme. Embora seja possível preencher seu texto com a função tokenizer definindo padding=True, o preenchimento dinâmico utilizando um data collator é mais eficiente.
from transformers import DataCollatorWithPadding\n\ndata_collator = DataCollatorWithPadding(tokenizer=tokenizer)
from transformers import DataCollatorWithPadding\n\ndata_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
Carregue o DistilBERT com AutoModelForSequenceClassification junto com o número de rótulos esperados:
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n\nmodel = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
Se você não estiver familiarizado com o fine-tuning de um modelo com o Trainer, dê uma olhada no tutorial básico aqui!
Nesse ponto, restam apenas três passos:
\nTrainingArguments.Trainer junto com o modelo, conjunto de dados, tokenizador e o data collator.train() para executar o fine-tuning do seu modelo.training_args = TrainingArguments(\n output_dir="./results",\n learning_rate=2e-5,\n per_device_train_batch_size=16,\n per_device_eval_batch_size=16,\n num_train_epochs=5,\n weight_decay=0.01,\n)\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=tokenized_imdb["train"],\n eval_dataset=tokenized_imdb["test"],\n tokenizer=tokenizer,\n data_collator=data_collator,\n)\n\ntrainer.train()
O Trainer aplicará o preenchimento dinâmico por padrão quando você definir o argumento tokenizer dele. Nesse caso, você não precisa especificar um data collator explicitamente.
Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato tf.data.Dataset com to_tf_dataset. Nessa execução você deverá especificar as entradas e rótulos (no parâmetro columns), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:
tf_train_set = tokenized_imdb["train"].to_tf_dataset(\n columns=["attention_mask", "input_ids", "label"],\n shuffle=True,\n batch_size=16,\n collate_fn=data_collator,\n)\n\ntf_validation_set = tokenized_imdb["test"].to_tf_dataset(\n columns=["attention_mask", "input_ids", "label"],\n shuffle=False,\n batch_size=16,\n collate_fn=data_collator,\n)
Se você não estiver familiarizado com o fine-tuning de um modelo com o Keras, dê uma olhada no tutorial básico aqui!
Configure o otimizador e alguns hiperparâmetros de treinamento:
\n\n\tfrom transformers import create_optimizer\nimport tensorflow as tf\n\nbatch_size = 16\nnum_epochs = 5\nbatches_per_epoch = len(tokenized_imdb["train"]) // batch_size\ntotal_train_steps = int(batches_per_epoch * num_epochs)\noptimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
Carregue o DistilBERT com TFAutoModelForSequenceClassification junto com o número de rótulos esperados:
from transformers import TFAutoModelForSequenceClassification\n\nmodel = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
Configure o modelo para treinamento com o método compile:
import tensorflow as tf\n\nmodel.compile(optimizer=optimizer)
Chame o método fit para executar o fine-tuning do modelo:
model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)
Para obter um exemplo mais aprofundado de como executar o fine-tuning de um modelo para classificação de texto, dê uma olhada nesse notebook utilizando PyTorch ou nesse notebook utilizando TensorFlow.
A classificação de tokens atribui um rótulo a tokens individuais em uma frase. Uma das tarefas de classificação de tokens mais comuns é o Reconhecimento de Entidade Nomeada, também chamada de NER (sigla em inglês para Named Entity Recognition). O NER tenta encontrar um rótulo para cada entidade em uma frase, como uma pessoa, local ou organização.
\nEste guia mostrará como realizar o fine-tuning do DistilBERT no conjunto de dados WNUT 17 para detectar novas entidades.
\n\n\nConsulte a página de tarefas de classificação de tokens para obter mais informações sobre outras formas de classificação de tokens e seus modelos, conjuntos de dados e métricas associadas.
Carregue o conjunto de dados WNUT 17 da biblioteca 🤗 Datasets:
\n\n\tfrom datasets import load_dataset\n\nwnut = load_dataset("wnut_17")
E dê uma olhada em um exemplo:
\n\n\twnut["train"][0]\n{'id': '0',\n 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n 'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']\n}
Cada número em ner_tags representa uma entidade. Converta o número em um rótulo para obter mais informações:
label_list = wnut["train"].features[f"ner_tags"].feature.names\nlabel_list\n[\n "O",\n "B-corporation",\n "I-corporation",\n "B-creative-work",\n "I-creative-work",\n "B-group",\n "I-group",\n "B-location",\n "I-location",\n "B-person",\n "I-person",\n "B-product",\n "I-product",\n]
O ner_tag descreve uma entidade, como uma organização, local ou pessoa. A letra que prefixa cada ner_tag indica a posição do token da entidade:
B- indica o início de uma entidade.I- indica que um token está contido dentro da mesma entidade (por exemplo, o token State pode fazer parte de uma entidade como Empire State Building).0 indica que o token não corresponde a nenhuma entidade.Carregue o tokenizer do DistilBERT para processar os tokens:
from transformers import AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
Como a entrada já foi dividida em palavras, defina is_split_into_words=True para tokenizar as palavras em subpalavras:
tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)\ntokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])\ntokens\n['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
Ao adicionar os tokens especiais [CLS] e [SEP] e a tokenização de subpalavras uma incompatibilidade é gerada entre a entrada e os rótulos. Uma única palavra correspondente a um único rótulo pode ser dividida em duas subpalavras. Você precisará realinhar os tokens e os rótulos da seguinte forma:
word_ids.-100 aos tokens especiais [CLS] e [SEP] para que a função de loss do PyTorch ignore eles.-100 a outros subtokens da mesma palavra.Aqui está como você pode criar uma função para realinhar os tokens e rótulos e truncar sequências para não serem maiores que o comprimento máximo de entrada do DistilBERT:
\n\n\tdef tokenize_and_align_labels(examples):\n tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)\n\n labels = []\n for i, label in enumerate(examples[f"ner_tags"]):\n word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.\n previous_word_idx = None\n label_ids = []\n for word_idx in word_ids: # Set the special tokens to -100.\n if word_idx is None:\n label_ids.append(-100)\n elif word_idx != previous_word_idx: # Only label the first token of a given word.\n label_ids.append(label[word_idx])\n else:\n label_ids.append(-100)\n previous_word_idx = word_idx\n labels.append(label_ids)\n\n tokenized_inputs["labels"] = labels\n return tokenized_inputs
Use a função map do 🤗 Datasets para tokenizar e alinhar os rótulos em todo o conjunto de dados. Você pode acelerar a função map configurando batched=True para processar vários elementos do conjunto de dados de uma só vez:
tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
Use o DataCollatorForTokenClassification para criar um batch de exemplos. Ele também preencherá dinamicamente seu texto e rótulos para o comprimento do elemento mais longo em seu batch, para que tenham um comprimento uniforme. Embora seja possível preencher seu texto na função tokenizer configurando padding=True, o preenchimento dinâmico é mais eficiente.
from transformers import DataCollatorForTokenClassification\n\ndata_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
from transformers import DataCollatorForTokenClassification\n\ndata_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")
Carregue o DistilBERT com o AutoModelForTokenClassification junto com o número de rótulos esperados:
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer\n\nmodel = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=14)
Se você não estiver familiarizado com o fine-tuning de um modelo com o Trainer, dê uma olhada no tutorial básico aqui!
Nesse ponto, restam apenas três passos:
\nTrainingArguments.Trainer junto com o modelo, conjunto de dados, tokenizador e o data collator.train() para executar o fine-tuning do seu modelo.training_args = TrainingArguments(\n output_dir="./results",\n evaluation_strategy="epoch",\n learning_rate=2e-5,\n per_device_train_batch_size=16,\n per_device_eval_batch_size=16,\n num_train_epochs=3,\n weight_decay=0.01,\n)\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=tokenized_wnut["train"],\n eval_dataset=tokenized_wnut["test"],\n tokenizer=tokenizer,\n data_collator=data_collator,\n)\n\ntrainer.train()
Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato tf.data.Dataset com to_tf_dataset. Nessa execução você deverá especificar as entradas e rótulos (no parâmetro columns), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:
tf_train_set = tokenized_wnut["train"].to_tf_dataset(\n columns=["attention_mask", "input_ids", "labels"],\n shuffle=True,\n batch_size=16,\n collate_fn=data_collator,\n)\n\ntf_validation_set = tokenized_wnut["validation"].to_tf_dataset(\n columns=["attention_mask", "input_ids", "labels"],\n shuffle=False,\n batch_size=16,\n collate_fn=data_collator,\n)
Se você não estiver familiarizado com o fine-tuning de um modelo com o Keras, dê uma olhada no tutorial básico aqui!
Configure o otimizador e alguns hiperparâmetros de treinamento:
\n\n\tfrom transformers import create_optimizer\n\nbatch_size = 16\nnum_train_epochs = 3\nnum_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs\noptimizer, lr_schedule = create_optimizer(\n init_lr=2e-5,\n num_train_steps=num_train_steps,\n weight_decay_rate=0.01,\n num_warmup_steps=0,\n)
Carregue o DistilBERT com o TFAutoModelForTokenClassification junto com o número de rótulos esperados:
from transformers import TFAutoModelForTokenClassification\n\nmodel = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
Configure o modelo para treinamento com o método compile:
import tensorflow as tf\n\nmodel.compile(optimizer=optimizer)
Chame o método fit para executar o fine-tuning do modelo:
model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)
Para obter um exemplo mais aprofundado de como executar o fine-tuning de um modelo para classificação de tokens, dê uma olhada nesse notebook utilizando PyTorch ou nesse notebook utilizando TensorFlow.
Installieren Sie 🤗 Transformers für die Deep-Learning-Bibliothek, mit der Sie arbeiten, richten Sie Ihren Cache ein und konfigurieren Sie 🤗 Transformers optional für den Offline-Betrieb.
\n🤗 Transformers wurde unter Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, und Flax getestet. Folgen Sie den Installationsanweisungen unten für die von Ihnen verwendete Deep-Learning-Bibliothek:
\nSie sollten 🤗 Transformers in einer virtuellen Umgebung installieren. Wenn Sie mit virtuellen Python-Umgebungen nicht vertraut sind, werfen Sie einen Blick auf diese Anleitung. Eine virtuelle Umgebung macht es einfacher, verschiedene Projekte zu verwalten und Kompatibilitätsprobleme zwischen Abhängigkeiten zu vermeiden.
\nBeginnen wir mit der Erstellung einer virtuellen Umgebung in Ihrem Projektverzeichnis:
\n\n\tpython -m venv .envAktivieren wir die virtuelle Umgebung. Unter Linux und MacOs:
\n\n\tsource .env/bin/activate
Aktivieren wir die virtuelle Umgebung unter Windows
\n\n\t.env/Scripts/activateJetzt können wir die 🤗 Transformers mit dem folgenden Befehl installieren:
\n\n\tpip install transformers
Bei reiner CPU-Unterstützung können wir 🤗 Transformers und eine Deep-Learning-Bibliothek bequem in einer Zeile installieren. Installieren wir zum Beispiel 🤗 Transformers und PyTorch mit:
\n\n\tpip install transformers[torch]
🤗 Transformers und TensorFlow 2.0:
\n\n\tpip install transformers[tf-cpu]
🤗 Transformers und Flax:
\n\n\tpip install transformers[flax]
Überprüfen wir abschließend, ob 🤗 Transformers ordnungsgemäß installiert wurde, indem wir den folgenden Befehl ausführen. Es wird ein vortrainiertes Modell heruntergeladen:
\n\n\tpython -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"Dann wird die Kategorie und die Wahrscheinlichkeit ausgegeben:
\n\n\t[{'label': 'POSITIVE', 'score': 0.9998704791069031}]Installieren wir 🤗 Transformers aus dem Quellcode mit dem folgenden Befehl:
\n\n\tpip install git+https://github.com/huggingface/transformers
Dieser Befehl installiert die aktuelle main Version und nicht die neueste stable Version. Die main-Version ist nützlich, um mit den neuesten Entwicklungen Schritt zu halten. Zum Beispiel, wenn ein Fehler seit der letzten offiziellen Version behoben wurde, aber eine neue Version noch nicht veröffentlicht wurde. Das bedeutet jedoch, dass die “Hauptversion” nicht immer stabil ist. Wir bemühen uns, die Hauptversion einsatzbereit zu halten, und die meisten Probleme werden normalerweise innerhalb weniger Stunden oder eines Tages behoben. Wenn Sie auf ein Problem stoßen, öffnen Sie bitte ein [Issue] (https://github.com/huggingface/transformers/issues), damit wir es noch schneller beheben können!
Überprüfen wir, ob 🤗 Transformers richtig installiert wurde, indem Sie den folgenden Befehl ausführen:
\n\n\tpython -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"Sie benötigen eine bearbeitbare Installation, wenn Sie:
\nKlonen Sie das Repository und installieren 🤗 Transformers mit den folgenden Befehlen:
\n\n\tgit clone https://github.com/huggingface/transformers.git\ncd transformers\npip install -e .
Diese Befehle verknüpfen den Ordner, in den Sie das Repository geklont haben, mit den Pfaden Ihrer Python-Bibliotheken. Python wird nun in dem Ordner suchen, in den Sie geklont haben, zusätzlich zu den normalen Bibliothekspfaden. Wenn zum Beispiel Ihre Python-Pakete normalerweise in ~/anaconda3/envs/main/lib/python3.7/site-packages/ installiert sind, wird Python auch den Ordner durchsuchen, in den Sie geklont haben: ~/transformers/.
Sie müssen den Ordner transformers behalten, wenn Sie die Bibliothek weiter verwenden wollen.
Jetzt können Sie Ihren Klon mit dem folgenden Befehl ganz einfach auf die neueste Version von 🤗 Transformers aktualisieren:
\n\n\tcd ~/transformers/\ngit pullIhre Python-Umgebung wird beim nächsten Ausführen die main-Version von 🤗 Transformers finden.
Installation von dem conda Kanal huggingface:
conda install -c huggingface transformers
Vorgefertigte Modelle werden heruntergeladen und lokal zwischengespeichert unter: ~/.cache/huggingface/hub. Dies ist das Standardverzeichnis, das durch die Shell-Umgebungsvariable “TRANSFORMERS_CACHE” vorgegeben ist. Unter Windows wird das Standardverzeichnis durch C:\\Benutzer\\Benutzername\\.cache\\huggingface\\hub angegeben. Sie können die unten aufgeführten Shell-Umgebungsvariablen - in der Reihenfolge ihrer Priorität - ändern, um ein anderes Cache-Verzeichnis anzugeben:
HUGGINGFACE_HUB_CACHE oder TRANSFORMERS_CACHE.HF_HOME.XDG_CACHE_HOME + /huggingface.Transformers verwendet die Shell-Umgebungsvariablen PYTORCH_TRANSFORMERS_CACHE oder PYTORCH_PRETRAINED_BERT_CACHE, wenn Sie von einer früheren Iteration dieser Bibliothek kommen und diese Umgebungsvariablen gesetzt haben, sofern Sie nicht die Shell-Umgebungsvariable TRANSFORMERS_CACHE angeben.
Transformers ist in der Lage, in einer Firewall- oder Offline-Umgebung zu laufen, indem es nur lokale Dateien verwendet. Setzen Sie die Umgebungsvariable TRANSFORMERS_OFFLINE=1, um dieses Verhalten zu aktivieren.
Fügen sie 🤗 Datasets zu Ihrem Offline-Trainingsworkflow hinzufügen, indem Sie die Umgebungsvariable HF_DATASETS_OFFLINE=1 setzen.
So würden Sie beispielsweise ein Programm in einem normalen Netzwerk mit einer Firewall für externe Instanzen mit dem folgenden Befehl ausführen:
\n\n\tpython examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
Führen Sie das gleiche Programm in einer Offline-Instanz mit aus:
\n\n\tHF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \\\npython examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
Das Skript sollte nun laufen, ohne sich aufzuhängen oder eine Zeitüberschreitung abzuwarten, da es weiß, dass es nur nach lokalen Dateien suchen soll.
\nEine andere Möglichkeit, 🤗 Transformers offline zu verwenden, besteht darin, die Dateien im Voraus herunterzuladen und dann auf ihren lokalen Pfad zu verweisen, wenn Sie sie offline verwenden müssen. Es gibt drei Möglichkeiten, dies zu tun:
\nLaden Sie eine Datei über die Benutzeroberfläche des Model Hub herunter, indem Sie auf das ↓-Symbol klicken.
\n![\"download-icon\"](\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/download-icon.png\"){kind=icon}
Verwenden Sie den [PreTrainedModel.from_pretrained] und [PreTrainedModel.save_pretrained] Workflow:
\nLaden Sie Ihre Dateien im Voraus mit PreTrainedModel.from_pretrained() herunter:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n\ntokenizer = AutoTokenizer.from_pretrained("bigscience/T0_3B")\nmodel = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0_3B")
Speichern Sie Ihre Dateien in einem bestimmten Verzeichnis mit PreTrainedModel.save_pretrained():
tokenizer.save_pretrained("./your/path/bigscience_t0")\nmodel.save_pretrained("./your/path/bigscience_t0")
Wenn Sie nun offline sind, laden Sie Ihre Dateien mit PreTrainedModel.from_pretrained() aus dem bestimmten Verzeichnis:
tokenizer = AutoTokenizer.from_pretrained("./your/path/bigscience_t0")\nmodel = AutoModel.from_pretrained("./your/path/bigscience_t0")
Programmatisches Herunterladen von Dateien mit der huggingface_hub Bibliothek:
\nInstallieren Sie die “huggingface_hub”-Bibliothek in Ihrer virtuellen Umgebung:
\n\n\tpython -m pip install huggingface_hub
Verwenden Sie die Funktion hf_hub_download, um eine Datei in einen bestimmten Pfad herunterzuladen. Der folgende Befehl lädt zum Beispiel die Datei “config.json” aus dem Modell T0 in den gewünschten Pfad herunter:
from huggingface_hub import hf_hub_download\n\nhf_hub_download(repo_id="bigscience/T0_3B", filename="config.json", cache_dir="./your/path/bigscience_t0")
Sobald Ihre Datei heruntergeladen und lokal zwischengespeichert ist, geben Sie den lokalen Pfad an, um sie zu laden und zu verwenden:
\n\n\tfrom transformers import AutoConfig\n\nconfig = AutoConfig.from_pretrained("./your/path/bigscience_t0/config.json")
Weitere Informationen zum Herunterladen von Dateien, die auf dem Hub gespeichert sind, finden Sie im Abschnitt [Wie man Dateien vom Hub herunterlädt] (https://huggingface.co/docs/hub/how-to-downstream).
Bei so vielen verschiedenen Transformator-Architekturen kann es eine Herausforderung sein, eine für Ihren Checkpoint zu erstellen. Als Teil der 🤗 Transformers Kernphilosophie, die Bibliothek leicht, einfach und flexibel nutzbar zu machen, leitet eine AutoClass automatisch die richtige Architektur aus einem gegebenen Checkpoint ab und lädt sie. Mit der Methode from_pretrained() kann man schnell ein vortrainiertes Modell für eine beliebige Architektur laden, so dass man keine Zeit und Ressourcen aufwenden muss, um ein Modell von Grund auf zu trainieren. Die Erstellung dieser Art von Checkpoint-agnostischem Code bedeutet, dass Ihr Code, wenn er für einen Checkpoint funktioniert, auch mit einem anderen Checkpoint funktionieren wird - solange er für eine ähnliche Aufgabe trainiert wurde - selbst wenn die Architektur unterschiedlich ist.
Denken Sie daran, dass sich die Architektur auf das Skelett des Modells bezieht und die Checkpoints die Gewichte für eine bestimmte Architektur sind. Zum Beispiel ist BERT eine Architektur, während bert-base-uncased ein Checkpoint ist. Modell ist ein allgemeiner Begriff, der entweder Architektur oder Prüfpunkt bedeuten kann.
In dieser Anleitung lernen Sie, wie man:
\nNahezu jede NLP-Aufgabe beginnt mit einem Tokenizer. Ein Tokenizer wandelt Ihre Eingabe in ein Format um, das vom Modell verarbeitet werden kann.
\nLaden Sie einen Tokenizer mit AutoTokenizer.from_pretrained():
from transformers import AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
Dann tokenisieren Sie Ihre Eingabe wie unten gezeigt:
\n\n\tsequence = "In a hole in the ground there lived a hobbit."\nprint(tokenizer(sequence))\n{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102], \n 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \n 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Für Audio- und Bildverarbeitungsaufgaben verarbeitet ein Merkmalsextraktor das Audiosignal oder Bild in das richtige Eingabeformat.
\nLaden Sie einen Merkmalsextraktor mit AutoFeatureExtractor.from_pretrained():
from transformers import AutoFeatureExtractor\n\nfeature_extractor = AutoFeatureExtractor.from_pretrained(\n "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"\n)
Multimodale Aufgaben erfordern einen Prozessor, der zwei Arten von Vorverarbeitungswerkzeugen kombiniert. Das Modell LayoutLMV2 beispielsweise benötigt einen Feature-Extraktor für Bilder und einen Tokenizer für Text; ein Prozessor kombiniert beide.
\nLaden Sie einen Prozessor mit AutoProcessor.from_pretrained():
from transformers import AutoProcessor\n\nprocessor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
Mit den AutoModelFor-Klassen können Sie schließlich ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe hier für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit AutoModelForSequenceClassification.from_pretrained():
from transformers import AutoModelForSequenceClassification\n\nmodel = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
\n\n\tfrom transformers import AutoModelForTokenClassification\n\nmodel = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
Für PyTorch-Modelle verwendet die Methode from_pretrained() torch.load(), die intern pickle verwendet und als unsicher bekannt ist. Generell sollte man niemals ein Modell laden, das aus einer nicht vertrauenswürdigen Quelle stammen könnte, oder das manipuliert worden sein könnte. Dieses Sicherheitsrisiko wird für öffentliche Modelle, die auf dem Hugging Face Hub gehostet werden, teilweise gemildert, da diese bei jeder Übertragung auf Malware gescannt werden. Siehe die Hub-Dokumentation für Best Practices wie signierte Commit-Verifizierung mit GPG.
TensorFlow- und Flax-Checkpoints sind nicht betroffen und können in PyTorch-Architekturen mit den Kwargs from_tf und from_flax für die Methode from_pretrained geladen werden, um dieses Problem zu umgehen.
Im Allgemeinen empfehlen wir die Verwendung der Klasse “AutoTokenizer” und der Klasse “AutoModelFor”, um trainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
Mit den Klassen TFAutoModelFor schließlich können Sie ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe hier für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit TFAutoModelForSequenceClassification.from_pretrained():
from transformers import TFAutoModelForSequenceClassification\n\nmodel = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
\n\n\tfrom transformers import TFAutoModelForTokenClassification\n\nmodel = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
Im Allgemeinen empfehlen wir, die Klasse “AutoTokenizer” und die Klasse “TFAutoModelFor” zu verwenden, um vortrainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
\n\t\t\t\t\tDie pipeline() macht es einfach, jedes beliebige Modell aus dem Hub für die Inferenz auf jede Sprache, Computer Vision, Sprache und multimodale Aufgaben zu verwenden. Selbst wenn Sie keine Erfahrung mit einer bestimmten Modalität haben oder nicht mit dem zugrundeliegenden Code hinter den Modellen vertraut sind, können Sie sie mit der pipeline() für Inferenzen verwenden! In diesem Beispiel lernen Sie, wie:
pipeline() für Inferenz zu verwenden.pipeline() für Audio-, Vision- und multimodale Aufgaben zu verwenden.Eine vollständige Liste der unterstützten Aufgaben und verfügbaren Parameter finden Sie in der pipeline()-Dokumentation.
Obwohl jede Aufgabe eine zugehörige pipeline() hat, ist es einfacher, die allgemeine pipeline()-Abstraktion zu verwenden, die alle aufgabenspezifischen Pipelines enthält. Die pipeline() lädt automatisch ein Standardmodell und eine Vorverarbeitungsklasse, die für Ihre Aufgabe inferenzfähig ist.
pipeline() und geben Sie eine Inferenzaufgabe an:from transformers import pipeline\n\ngenerator = pipeline(task="text-generation")
pipeline():generator(\n "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"\n) # doctest: +SKIP\n[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
Wenn Sie mehr als eine Eingabe haben, übergeben Sie die Eingabe als Liste:
\n\n\tgenerator(\n [\n "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",\n "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",\n ]\n) # doctest: +SKIP
Alle zusätzlichen Parameter für Ihre Aufgabe können auch in die pipeline() aufgenommen werden. Die Aufgabe Text-Generierung hat eine generate()-Methode mit mehreren Parametern zur Steuerung der Ausgabe. Wenn Sie zum Beispiel mehr als eine Ausgabe erzeugen wollen, setzen Sie den Parameter num_return_sequences:
generator(\n "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",\n num_return_sequences=2,\n) # doctest: +SKIP
Die pipeline() akzeptiert jedes Modell aus dem [Hub] (https://huggingface.co/models). Auf dem Hub gibt es Tags, mit denen Sie nach einem Modell filtern können, das Sie für Ihre Aufgabe verwenden möchten. Sobald Sie ein passendes Modell ausgewählt haben, laden Sie es mit der entsprechenden AutoModelFor und AutoTokenizer Klasse. Laden Sie zum Beispiel die Klasse AutoModelForCausalLM für eine kausale Sprachmodellierungsaufgabe:
from transformers import AutoTokenizer, AutoModelForCausalLM\n\ntokenizer = AutoTokenizer.from_pretrained("distilgpt2")\nmodel = AutoModelForCausalLM.from_pretrained("distilgpt2")
Erstellen Sie eine pipeline() für Ihre Aufgabe, und geben Sie das Modell und den Tokenizer an, die Sie geladen haben:
from transformers import pipeline\n\ngenerator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
Übergeben Sie Ihren Eingabetext an die pipeline() , um einen Text zu erzeugen:
generator(\n "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"\n) # doctest: +SKIP\n[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
Die pipeline() unterstützt auch Audioaufgaben wie Audioklassifizierung und automatische Spracherkennung.
Lassen Sie uns zum Beispiel die Emotion in diesem Audioclip klassifizieren:
\n\n\tfrom datasets import load_dataset\nimport torch\n\ntorch.manual_seed(42)\nds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")\naudio_file = ds[0]["audio"]["path"]
Finden Sie ein Audioklassifikation Modell auf dem Model Hub für Emotionserkennung und laden Sie es in die pipeline():
from transformers import pipeline\n\naudio_classifier = pipeline(\n task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"\n)
Übergeben Sie die Audiodatei an die pipeline():
preds = audio_classifier(audio_file)\npreds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]\npreds\n[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]
Die Verwendung einer pipeline() für Bildverarbeitungsaufgaben ist praktisch identisch.
Geben Sie Ihre Aufgabe an und übergeben Sie Ihr Bild an den Klassifikator. Das Bild kann ein Link oder ein lokaler Pfad zu dem Bild sein. Zum Beispiel: Welche Katzenart ist unten abgebildet?
\n![\"pipeline-cat-chonk\"](\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg\")
from transformers import pipeline\n\nvision_classifier = pipeline(task="image-classification")\npreds = vision_classifier(\n images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"\n)\npreds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]\npreds\n[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
Die pipeline() unterstützt mehr als eine Modalität. Eine Aufgabe zur Beantwortung visueller Fragen (VQA) kombiniert zum Beispiel Text und Bild. Verwenden Sie einen beliebigen Bildlink und eine Frage, die Sie zu dem Bild stellen möchten. Das Bild kann eine URL oder ein lokaler Pfad zu dem Bild sein.
Wenn Sie zum Beispiel das gleiche Bild wie in der obigen Vision-Pipeline verwenden:
\n\n\timage = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"\nquestion = "Where is the cat?"
Erstellen Sie eine Pipeline für “vqa” und übergeben Sie ihr das Bild und die Frage:
\n\n\tfrom transformers import pipeline\n\nvqa = pipeline(task="vqa")\npreds = vqa(image=image, question=question)\npreds = [{"score": round(pred["score"], 4), "answer": pred["answer"]} for pred in preds]\npreds\n[{'score': 0.9112, 'answer': 'snow'}, {'score': 0.8796, 'answer': 'in snow'}, {'score': 0.6717, 'answer': 'outside'}, {'score': 0.0291, 'answer': 'on ground'}, {'score': 0.027, 'answer': 'ground'}]
Mit 🤗 Transformers können Sie sofort loslegen! Verwenden Sie die pipeline() für schnelle Inferenz und laden Sie schnell ein vortrainiertes Modell und einen Tokenizer mit einer AutoClass, um Ihre Text-, Bild- oder Audioaufgabe zu lösen.
Alle in der Dokumentation vorgestellten Codebeispiele haben oben links einen Umschalter für PyTorch und TensorFlow. Wenn\nnicht, wird erwartet, dass der Code für beide Backends ohne Änderungen funktioniert.
pipeline() ist der einfachste Weg, ein vortrainiertes Modell für eine bestimmte Aufgabe zu verwenden.
Die pipeline() unterstützt viele gängige Aufgaben:
Text:
\nBild:
\nAudio:
\nFür mehr Details über die pipeline() und assoziierte Aufgaben, schauen Sie in die Dokumentation hier.
Im folgenden Beispiel werden Sie die pipeline() für die Stimmungsanalyse verwenden.
Installieren Sie die folgenden Abhängigkeiten, falls Sie dies nicht bereits getan haben:
\npip install torch
pip install tensorflow
Importieren sie die pipeline() und spezifizieren sie die Aufgabe, welche sie lösen möchten:
from transformers import pipeline\n\nclassifier = pipeline("sentiment-analysis")
Die Pipeline lädt ein standardmäßiges [vortrainiertes Modell] (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) und einen Tokenizer für die Stimmungs-Analyse herunter und speichert sie. Jetzt können Sie den “Klassifikator” auf Ihren Zieltext anwenden:
\n\n\tclassifier("We are very happy to show you the 🤗 Transformers library.")\n[{'label': 'POSITIVE', 'score': 0.9998}]
For more than one sentence, pass a list of sentences to the pipeline() which returns a list of dictionaries:
results = classifier(["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."])\nfor result in results:\n print(f"label: {result['label']}, with score: {round(result['score'], 4)}")\nlabel: POSITIVE, with score: 0.9998\nlabel: NEGATIVE, with score: 0.5309
Die pipeline() kann auch über einen ganzen Datensatz iterieren. Starten wir mit der Installation der 🤗 Datasets Bibliothek:
pip install datasets
Erstellen wir eine pipeline() mit der Aufgabe die wir lösen und dem Modell welches wir nutzen möchten.
import torch\nfrom transformers import pipeline\n\nspeech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
Als nächstes laden wir den Datensatz (siehe 🤗 Datasets Quick Start für mehr Details) welches wir nutzen möchten. Zum Beispiel laden wir den MInDS-14 Datensatz:
\n\n\tfrom datasets import load_dataset, Audio\n\ndataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
Wir müssen sicherstellen, dass die Abtastrate des Datensatzes der Abtastrate entspricht, mit der facebook/wav2vec2-base-960h trainiert wurde.
dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))
Audiodateien werden automatisch geladen und neu abgetastet, wenn die Spalte “audio” aufgerufen wird.\nExtrahieren wir die rohen Wellenform-Arrays der ersten 4 Beispiele und übergeben wir sie als Liste an die Pipeline:
\n\n\tresult = speech_recognizer(dataset[:4]["audio"])\nprint([d["text"] for d in result])\n['I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT', "FODING HOW I'D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE", "I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE AP SO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS", 'HOW DO I THURN A JOIN A COUNT']
Bei einem größeren Datensatz mit vielen Eingaben (wie bei Sprache oder Bildverarbeitung) sollten Sie einen Generator anstelle einer Liste übergeben, der alle Eingaben in den Speicher lädt. Weitere Informationen finden Sie in der Pipeline-Dokumentation.
\nDie pipeline() kann jedes Modell aus dem [Model Hub] (https://huggingface.co/models) verwenden, wodurch es einfach ist, die pipeline() für andere Anwendungsfälle anzupassen. Wenn Sie beispielsweise ein Modell wünschen, das französischen Text verarbeiten kann, verwenden Sie die Tags im Model Hub, um nach einem geeigneten Modell zu filtern. Das oberste gefilterte Ergebnis liefert ein mehrsprachiges BERT-Modell, das auf die Stimmungsanalyse abgestimmt ist. Großartig, verwenden wir dieses Modell!
model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
Use the AutoModelForSequenceClassification and AutoTokenizer to load the pretrained model and it’s associated tokenizer (more on an AutoClass below):
from transformers import AutoTokenizer, AutoModelForSequenceClassification\n\nmodel = AutoModelForSequenceClassification.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)
Use the TFAutoModelForSequenceClassification and AutoTokenizer to load the pretrained model and it’s associated tokenizer (more on an TFAutoClass below):
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n\nmodel = TFAutoModelForSequenceClassification.from_pretrained(model_name)\ntokenizer = AutoTokenizer.from_pretrained(model_name)
Dann können Sie das Modell und den Tokenizer in der pipeline() angeben und den Klassifikator auf Ihren Zieltext anwenden:
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)\nclassifier("Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.")\n[{'label': '5 stars', 'score': 0.7273}]
Wenn Sie kein Modell für Ihren Anwendungsfall finden können, müssen Sie ein vortrainiertes Modell auf Ihren Daten feinabstimmen. Schauen Sie sich unser Feinabstimmungs-Tutorial an, um zu erfahren, wie das geht. Und schließlich, nachdem Sie Ihr trainiertes Modell verfeinert haben, sollten Sie es mit der Community im Model Hub teilen (siehe Tutorial hier), um NLP für alle zu demokratisieren! 🤗
\nUnter der Haube arbeiten die Klassen AutoModelForSequenceClassification und AutoTokenizer zusammen, um die pipeline() zu betreiben. Eine AutoClass ist eine Abkürzung, die automatisch die Architektur eines trainierten Modells aus dessen Namen oder Pfad abruft. Sie müssen nur die passende AutoClass für Ihre Aufgabe und den zugehörigen Tokenizer mit AutoTokenizer auswählen.
Kehren wir zu unserem Beispiel zurück und sehen wir uns an, wie Sie die AutoClass verwenden können, um die Ergebnisse der pipeline() zu replizieren.
Ein Tokenizer ist für die Vorverarbeitung von Text in ein für das Modell verständliches Format zuständig. Zunächst zerlegt der Tokenisierer den Text in Wörter, die Token genannt werden. Es gibt mehrere Regeln für den Tokenisierungsprozess, z. B. wie und auf welcher Ebene ein Wort aufgespalten wird (weitere Informationen über Tokenisierung hier). Das Wichtigste ist jedoch, dass Sie den Tokenizer mit demselben Modellnamen instanziieren müssen, um sicherzustellen, dass Sie dieselben Tokenisierungsregeln verwenden, mit denen ein Modell zuvor trainiert wurde.\nLaden sie einen Tokenizer mit AutoTokenizer:
from transformers import AutoTokenizer\n\nmodel_name = "nlptown/bert-base-multilingual-uncased-sentiment"\ntokenizer = AutoTokenizer.from_pretrained(model_name)
Anschließend wandelt der Tokenizer die Token in Zahlen um, um einen Tensor als Eingabe für das Modell zu konstruieren. Dieser wird als Vokabular des Modells bezeichnet.
\nÜbergeben Sie Ihren Text an den Tokenizer:
\n\n\tencoding = tokenizer("We are very happy to show you the 🤗 Transformers library.")\nprint(encoding)\n{'input_ids': [101, 11312, 10320, 12495, 19308, 10114, 11391, 10855, 10103, 100, 58263, 13299, 119, 102],\n 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Der Tokenizer gibt ein Wörterbuch zurück, das Folgendes enthält:
\nGenau wie die pipeline() akzeptiert der Tokenizer eine Liste von Eingaben. Darüber hinaus kann der Tokenizer den Text auch auffüllen und kürzen, um einen Stapel mit einheitlicher Länge zurückzugeben:
pt_batch = tokenizer(\n ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],\n padding=True,\n truncation=True,\n max_length=512,\n return_tensors="pt",\n)
tf_batch = tokenizer(\n ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],\n padding=True,\n truncation=True,\n max_length=512,\n return_tensors="tf",\n)
Lesen Sie das Tutorial preprocessing für weitere Details zur Tokenisierung.
\n🤗 Transformers bietet eine einfache und einheitliche Möglichkeit, vortrainierte Instanzen zu laden. Das bedeutet, dass Sie ein AutoModel laden können, wie Sie einen AutoTokenizer laden würden. Der einzige Unterschied ist die Auswahl des richtigen AutoModel für die Aufgabe. Da Sie eine Text- oder Sequenzklassifizierung vornehmen, laden Sie AutoModelForSequenceClassification:
from transformers import AutoModelForSequenceClassification\n\nmodel_name = "nlptown/bert-base-multilingual-uncased-sentiment"\npt_model = AutoModelForSequenceClassification.from_pretrained(model_name)
In der Aufgabenzusammenfassung steht, welche [AutoModel]-Klasse für welche Aufgabe zu verwenden ist.
Jetzt können Sie Ihren vorverarbeiteten Stapel von Eingaben direkt an das Modell übergeben. Sie müssen nur das Wörterbuch entpacken, indem Sie ** hinzufügen:
pt_outputs = pt_model(**pt_batch)Das Modell gibt die endgültigen Aktivierungen in dem Attribut “logits” aus. Wenden Sie die Softmax-Funktion auf die “logits” an, um die Wahrscheinlichkeiten zu erhalten:
\n\n\tfrom torch import nn\n\npt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)\nprint(pt_predictions)\ntensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],\n [0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=<SoftmaxBackward0>)
🤗 Transformers bietet eine einfache und einheitliche Methode zum Laden von vortrainierten Instanzen. Das bedeutet, dass Sie ein TFAutoModel genauso laden können, wie Sie einen AutoTokenizer laden würden. Der einzige Unterschied ist die Auswahl des richtigen TFAutoModel für die Aufgabe. Da Sie Text - oder Sequenz - Klassifizierung machen, laden Sie TFAutoModelForSequenceClassification:
from transformers import TFAutoModelForSequenceClassification\n\nmodel_name = "nlptown/bert-base-multilingual-uncased-sentiment"\ntf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
In der Aufgabenzusammenfassung steht, welche [AutoModel]-Klasse für welche Aufgabe zu verwenden ist.
Jetzt können Sie Ihren vorverarbeiteten Stapel von Eingaben direkt an das Modell übergeben, indem Sie die Wörterbuchschlüssel direkt an die Tensoren übergeben:
\n\n\ttf_outputs = tf_model(tf_batch)Das Modell gibt die endgültigen Aktivierungen in dem Attribut “logits” aus. Wenden Sie die Softmax-Funktion auf die “logits” an, um die Wahrscheinlichkeiten zu erhalten:
\n\n\timport tensorflow as tf\n\ntf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)\ntf_predictions
Alle 🤗 Transformers-Modelle (PyTorch oder TensorFlow) geben die Tensoren vor der endgültigen Aktivierungsfunktion\nFunktion (wie Softmax) aus, da die endgültige Aktivierungsfunktion oft mit dem Verlusten verschmolzen ist.
Modelle sind ein standardmäßiges torch.nn.Module oder ein tf.keras.Model, sodass Sie sie in Ihrer üblichen Trainingsschleife verwenden können. Um jedoch die Dinge einfacher zu machen, bietet 🤗 Transformers eine Trainer-Klasse für PyTorch, die Funktionalität für verteiltes Training, gemischte Präzision und mehr bietet. Für TensorFlow können Sie die Methode fit aus Keras verwenden. Siehe das training tutorial für weitere Details.
Transformers-Modellausgaben sind spezielle Datenklassen, so dass ihre Attribute in einer IDE automatisch vervollständigt werden.\nDie Modellausgänge verhalten sich auch wie ein Tupel oder ein Wörterbuch (z.B. können Sie mit einem Integer, einem Slice oder einem String indexieren), wobei die Attribute, die “None” sind, ignoriert werden.
Sobald Ihr Modell feinabgestimmt ist, können Sie es mit seinem Tokenizer speichern, indem Sie PreTrainedModel.save_pretrained() verwenden:
pt_save_directory = "./pt_save_pretrained"\ntokenizer.save_pretrained(pt_save_directory)\npt_model.save_pretrained(pt_save_directory)
Wenn Sie bereit sind, das Modell erneut zu verwenden, laden Sie es mit PreTrainedModel.from_pretrained():
pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained")
Sobald Ihr Modell feinabgestimmt ist, können Sie es mit seinem Tokenizer unter Verwendung von TFPreTrainedModel.save_pretrained() speichern:
tf_save_directory = "./tf_save_pretrained"\ntokenizer.save_pretrained(tf_save_directory)\ntf_model.save_pretrained(tf_save_directory)
Wenn Sie bereit sind, das Modell wieder zu verwenden, laden Sie es mit TFPreTrainedModel.from_pretrained():
tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained")
Ein besonders cooles 🤗 Transformers-Feature ist die Möglichkeit, ein Modell zu speichern und es entweder als PyTorch- oder TensorFlow-Modell wieder zu laden. Der Parameter “from_pt” oder “from_tf” kann das Modell von einem Framework in das andere konvertieren:
\nfrom transformers import AutoModel\n\ntokenizer = AutoTokenizer.from_pretrained(tf_save_directory)\npt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
from transformers import TFAutoModel\n\ntokenizer = AutoTokenizer.from_pretrained(pt_save_directory)\ntf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
Sie können die Konfigurationsklasse des Modells ändern, um zu bestimmen, wie ein Modell aufgebaut ist. Die Konfiguration legt die Attribute eines Modells fest, z. B. die Anzahl der verborgenen Schichten oder der Aufmerksamkeitsköpfe. Wenn Sie ein Modell aus einer benutzerdefinierten Konfigurationsklasse initialisieren, beginnen Sie bei Null. Die Modellattribute werden zufällig initialisiert, und Sie müssen das Modell trainieren, bevor Sie es verwenden können, um aussagekräftige Ergebnisse zu erhalten.
\nBeginnen Sie mit dem Import von AutoConfig und laden Sie dann das trainierte Modell, das Sie ändern möchten. Innerhalb von AutoConfig.from_pretrained() können Sie das Attribut angeben, das Sie ändern möchten, z. B. die Anzahl der Aufmerksamkeitsköpfe:
from transformers import AutoConfig\n\nmy_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)
Create a model from your custom configuration with AutoModel.from_config():
from transformers import AutoModel\n\nmy_model = AutoModel.from_config(my_config)
Create a model from your custom configuration with TFAutoModel.from_config():
from transformers import TFAutoModel\n\nmy_model = TFAutoModel.from_config(my_config)
Weitere Informationen zur Erstellung von benutzerdefinierten Konfigurationen finden Sie in der Anleitung Erstellen einer benutzerdefinierten Architektur.
\nNachdem Sie nun die 🤗 Transformers-Kurztour abgeschlossen haben, schauen Sie sich unsere Anleitungen an und erfahren Sie, wie Sie spezifischere Dinge tun können, wie das Schreiben eines benutzerdefinierten Modells, die Feinabstimmung eines Modells für eine Aufgabe und wie man ein Modell mit einem Skript trainiert. Wenn Sie mehr über die Kernkonzepte von 🤗 Transformers erfahren möchten, nehmen Sie sich eine Tasse Kaffee und werfen Sie einen Blick auf unsere konzeptionellen Leitfäden!
\n\n\n\t\t\n"},"__index_level_0__":{"kind":"number","value":496,"string":"496"}}},{"rowIdx":497,"cells":{"index":{"kind":"number","value":0,"string":"0"},"repo_id":{"kind":"string","value":"hf_public_repos/doc-build-dev/transformers/pr_18789"},"file_path":{"kind":"string","value":"hf_public_repos/doc-build-dev/transformers/pr_18789/de/index.html"},"content":{"kind":"string","value":"\n\t\n\t\n\t\n\t\n\t\n\t\n\t \n\n\n\n\n\n\nMaschinelles Lernen auf dem neuesten Stand der Technik für PyTorch, TensorFlow und JAX.
\n🤗 Transformers bietet APIs zum einfachen Herunterladen und Trainieren von vortrainierten Modellen auf dem neuesten Stand der Technik. Die Verwendung von vortrainierten Modellen kann Rechenkosten sparen und den CO2-Fußabdruck reduzieren und Zeit sparen, die für das Training eines Modells von Grund auf benötigt wird. Die Modelle können für verschiedene Modalitäten verwendet werden, wie z. B.:
\nUnsere Bibliothek unterstützt die nahtlose Integration von drei der beliebtesten Deep-Learning-Bibliotheken: PyTorch, TensorFlow und JAX. Trainieren Sie Ihr Modell in drei Codezeilen in einem Framework und laden Sie es zur Inferenz mit einem anderen.
\nJede 🤗 Transformers-Architektur ist in einem eigenständigen Python-Modul definiert, so dass sie leicht für Forschung und Experimente angepasst werden kann.
\n![\"HuggingFace](\"https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png\")
\nDie Dokumentation ist in fünf Teile gegliedert:
\nGET STARTED enthält eine kurze Tour und Installationsanweisungen, um mit 🤗 Transformers loszulegen.
TUTORIALS sind ein hervorragender Ausgangspunkt, wenn Sie neu in unserer Bibliothek sind. Dieser Abschnitt hilft Ihnen, die grundlegenden Fähigkeiten zu erlangen, die Sie benötigen, um mit 🤗 Transformers zu arbeiten.
HOW-TO GUIDES zeigen Ihnen, wie Sie ein bestimmtes Ziel erreichen können, z. B. die Feinabstimmung eines vortrainierten Modells für die Sprachmodellierung oder die Erstellung eines benutzerdefinierten Modellkopfs.
KONZEPTUELLE ANLEITUNGEN bietet weitere Diskussionen und Erklärungen zu den zugrunde liegenden Konzepten und Ideen hinter Modellen, Aufgaben und der Designphilosophie von 🤗 Transformers.
API beschreibt jede Klasse und Funktion, gruppiert in:
\nDie Bibliothek enthält derzeit JAX-, PyTorch- und TensorFlow-Implementierungen, vortrainierte Modellgewichte, Nutzungsskripte und Konvertierungsprogramme für die folgenden Modelle.
\nDie folgende Tabelle zeigt die derzeitige Unterstützung in der Bibliothek für jedes dieser Modelle, unabhängig davon, ob sie einen Python\nTokenizer haben (als “langsam” bezeichnet), ein “schneller” Tokenizer, der von der 🤗 Tokenizers Bibliothek unterstützt wird, ob sie Unterstützung in Jax (via\nFlax), PyTorch, und/oder TensorFlow haben.
\n\n| Model | \nTokenizer slow | \nTokenizer fast | \nPyTorch support | \nTensorFlow support | \nFlax Support |
|---|---|---|---|---|---|
| ALBERT | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| BART | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| BEiT | \n❌ | \n❌ | \n✅ | \n❌ | \n✅ |
| BERT | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| Bert Generation | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| BigBird | \n✅ | \n✅ | \n✅ | \n❌ | \n✅ |
| BigBird-Pegasus | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Blenderbot | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| BlenderbotSmall | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| BLOOM | \n❌ | \n✅ | \n✅ | \n❌ | \n❌ |
| CamemBERT | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| CANINE | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| CLIP | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| CodeGen | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| ConvBERT | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| ConvNeXT | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| CTRL | \n✅ | \n❌ | \n✅ | \n✅ | \n❌ |
| CvT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Data2VecAudio | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Data2VecText | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Data2VecVision | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| DeBERTa | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| DeBERTa-v2 | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| Decision Transformer | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| DeiT | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| DETR | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| DistilBERT | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| DPR | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| DPT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| ELECTRA | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| Encoder decoder | \n❌ | \n❌ | \n✅ | \n✅ | \n✅ |
| FairSeq Machine-Translation | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| FlauBERT | \n✅ | \n❌ | \n✅ | \n✅ | \n❌ |
| FLAVA | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| FNet | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| Funnel Transformer | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| GLPN | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| GPT Neo | \n❌ | \n❌ | \n✅ | \n❌ | \n✅ |
| GPT NeoX | \n❌ | \n✅ | \n✅ | \n❌ | \n❌ |
| GPT-J | \n❌ | \n❌ | \n✅ | \n✅ | \n✅ |
| GroupViT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Hubert | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| I-BERT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| ImageGPT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| LayoutLM | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| LayoutLMv2 | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| LayoutLMv3 | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| LED | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| LeViT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Longformer | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| LongT5 | \n❌ | \n❌ | \n✅ | \n❌ | \n✅ |
| LUKE | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| LXMERT | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| M-CTC-T | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| M2M100 | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| Marian | \n✅ | \n❌ | \n✅ | \n✅ | \n✅ |
| MaskFormer | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| mBART | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| Megatron-BERT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| MobileBERT | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| MobileViT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| MPNet | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| MT5 | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| MVP | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| Nezha | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Nyströmformer | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| OpenAI GPT | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| OpenAI GPT-2 | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| OPT | \n❌ | \n❌ | \n✅ | \n✅ | \n✅ |
| OWL-ViT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Pegasus | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| Perceiver | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| PLBart | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| PoolFormer | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| ProphetNet | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| QDQBert | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| RAG | \n✅ | \n❌ | \n✅ | \n✅ | \n❌ |
| REALM | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| Reformer | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| RegNet | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| RemBERT | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| ResNet | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| RetriBERT | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| RoBERTa | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| RoFormer | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| SegFormer | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
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| Speech2Text2 | \n✅ | \n❌ | \n❌ | \n❌ | \n❌ |
| Splinter | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| SqueezeBERT | \n✅ | \n✅ | \n✅ | \n❌ | \n❌ |
| Swin Transformer | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| Swin Transformer V2 | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| T5 | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
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| Trajectory Transformer | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| Transformer-XL | \n✅ | \n❌ | \n✅ | \n✅ | \n❌ |
| TrOCR | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| UniSpeech | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| UniSpeechSat | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| VAN | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| VideoMAE | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
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| Vision Encoder decoder | \n❌ | \n❌ | \n✅ | \n✅ | \n✅ |
| VisionTextDualEncoder | \n❌ | \n❌ | \n✅ | \n❌ | \n✅ |
| VisualBERT | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| ViT | \n❌ | \n❌ | \n✅ | \n✅ | \n✅ |
| ViTMAE | \n❌ | \n❌ | \n✅ | \n✅ | \n❌ |
| Wav2Vec2 | \n✅ | \n❌ | \n✅ | \n✅ | \n✅ |
| Wav2Vec2-Conformer | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| WavLM | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| XGLM | \n✅ | \n✅ | \n✅ | \n❌ | \n✅ |
| XLM | \n✅ | \n❌ | \n✅ | \n✅ | \n❌ |
| XLM-ProphetNet | \n✅ | \n❌ | \n✅ | \n❌ | \n❌ |
| XLM-RoBERTa | \n✅ | \n✅ | \n✅ | \n✅ | \n✅ |
| XLM-RoBERTa-XL | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| XLNet | \n✅ | \n✅ | \n✅ | \n✅ | \n❌ |
| YOLOS | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
| YOSO | \n❌ | \n❌ | \n✅ | \n❌ | \n❌ |
Die letzten beiden Tutorials haben gezeigt, wie man ein Modell mit PyTorch, Keras und 🤗 Accelerate für verteilte Setups feinabstimmen kann. Der nächste Schritt besteht darin, Ihr Modell mit der Community zu teilen! Bei Hugging Face glauben wir an den offenen Austausch von Wissen und Ressourcen, um künstliche Intelligenz für alle zu demokratisieren. Wir ermutigen Sie, Ihr Modell mit der Community zu teilen, um anderen zu helfen, Zeit und Ressourcen zu sparen.
\nIn diesem Tutorial lernen Sie zwei Methoden kennen, wie Sie ein trainiertes oder verfeinertes Modell auf dem Model Hub teilen können:
\nUm ein Modell mit der Öffentlichkeit zu teilen, benötigen Sie ein Konto auf huggingface.co. Sie können auch einer bestehenden Organisation beitreten oder eine neue Organisation gründen.
Jedes Repository im Model Hub verhält sich wie ein typisches GitHub-Repository. Unsere Repositorys bieten Versionierung, Commit-Historie und die Möglichkeit, Unterschiede zu visualisieren.
\nDie integrierte Versionierung des Model Hub basiert auf Git und git-lfs. Mit anderen Worten: Sie können ein Modell als ein Repository behandeln, was eine bessere Zugriffskontrolle und Skalierbarkeit ermöglicht. Die Versionskontrolle ermöglicht Revisionen, eine Methode zum Anheften einer bestimmten Version eines Modells mit einem Commit-Hash, Tag oder Branch.
\nFolglich können Sie eine bestimmte Modellversion mit dem Parameter “Revision” laden:
\n\n\tmodel = AutoModel.from_pretrained(\n "julien-c/EsperBERTo-small", revision="v2.0.1" # tag name, or branch name, or commit hash\n)
Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können die Commit-Historie sowie die Unterschiede einsehen:
\n![\"vis_diff\"](\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vis_diff.png\")
Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig ~/.cache/) gespeichert:
huggingface-cli login
Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die huggingface_hub Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub.
pip install huggingface_hub
Verwenden Sie dann notebook_login, um sich beim Hub anzumelden, und folgen Sie dem Link hier, um ein Token für die Anmeldung zu generieren:
from huggingface_hub import notebook_login\n\nnotebook_login()
Um sicherzustellen, dass Ihr Modell von jemandem verwendet werden kann, der mit einem anderen Framework arbeitet, empfehlen wir Ihnen, Ihr Modell sowohl mit PyTorch- als auch mit TensorFlow-Checkpoints zu konvertieren und hochzuladen. Während Benutzer immer noch in der Lage sind, Ihr Modell von einem anderen Framework zu laden, wenn Sie diesen Schritt überspringen, wird es langsamer sein, weil 🤗 Transformers den Checkpoint on-the-fly konvertieren müssen.
\nDie Konvertierung eines Checkpoints für ein anderes Framework ist einfach. Stellen Sie sicher, dass Sie PyTorch und TensorFlow installiert haben (siehe hier für Installationsanweisungen), und finden Sie dann das spezifische Modell für Ihre Aufgabe in dem anderen Framework.
\nGeben Sie from_tf=True an, um einen Prüfpunkt von TensorFlow nach PyTorch zu konvertieren:
pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)\npt_model.save_pretrained("path/to/awesome-name-you-picked")
Geben Sie from_pt=True an, um einen Prüfpunkt von PyTorch nach TensorFlow zu konvertieren:
tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
Dann können Sie Ihr neues TensorFlow-Modell mit seinem neuen Checkpoint speichern:
\n\n\ttf_model.save_pretrained("path/to/awesome-name-you-picked")
Wenn ein Modell in Flax verfügbar ist, können Sie auch einen Kontrollpunkt von PyTorch nach Flax konvertieren:
\n\n\tflax_model = FlaxDistilBertForSequenceClassification.from_pretrained(\n "path/to/awesome-name-you-picked", from_pt=True\n)
Die Weitergabe eines Modells an den Hub ist so einfach wie das Hinzufügen eines zusätzlichen Parameters oder Rückrufs. Erinnern Sie sich an das Feinabstimmungs-Tutorial, in der Klasse TrainingArguments geben Sie Hyperparameter und zusätzliche Trainingsoptionen an. Eine dieser Trainingsoptionen beinhaltet die Möglichkeit, ein Modell direkt an den Hub zu pushen. Setzen Sie push_to_hub=True in Ihrer TrainingArguments:
training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True)
Übergeben Sie Ihre Trainingsargumente wie gewohnt an Trainer:
trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=small_train_dataset,\n eval_dataset=small_eval_dataset,\n compute_metrics=compute_metrics,\n)
Nach der Feinabstimmung Ihres Modells rufen Sie push_to_hub() auf Trainer auf, um das trainierte Modell an den Hub zu übertragen. Transformers fügt sogar automatisch Trainings-Hyperparameter, Trainingsergebnisse und Framework-Versionen zu Ihrer Modellkarte hinzu!
trainer.push_to_hub()Geben Sie ein Modell mit PushToHubCallback an den Hub weiter. In der PushToHubCallback Funktion, fügen Sie hinzu:
hub_model_id, die Ihr Hub-Benutzername und Modellname ist.from transformers.keras.callbacks import PushToHubCallback\n\npush_to_hub_callback = PushToHubCallback(\n output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model"\n)
Fügen Sie den Callback zu fit hinzu, und 🤗 Transformers wird das trainierte Modell an den Hub weiterleiten:
model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback)
push_to_hub.\n\tSie können push_to_hub auch direkt für Ihr Modell aufrufen, um es in den Hub hochzuladen.
Geben Sie den Namen Ihres Modells in “push_to_hub” an:
\n\n\tpt_model.push_to_hub("my-awesome-model")
Dadurch wird ein Repository unter Ihrem Benutzernamen mit dem Modellnamen my-awesome-model erstellt. Benutzer können nun Ihr Modell mit der Funktion from_pretrained laden:
from transformers import AutoModel\n\nmodel = AutoModel.from_pretrained("your_username/my-awesome-model")
Wenn Sie zu einer Organisation gehören und Ihr Modell stattdessen unter dem Namen der Organisation pushen wollen, fügen Sie diesen einfach zur repo_id hinzu:
pt_model.push_to_hub("my-awesome-org/my-awesome-model")
Die Funktion “push_to_hub” kann auch verwendet werden, um andere Dateien zu einem Modell-Repository hinzuzufügen. Zum Beispiel kann man einen Tokenizer zu einem Modell-Repository hinzufügen:
\n\n\ttokenizer.push_to_hub("my-awesome-model")
Oder vielleicht möchten Sie die TensorFlow-Version Ihres fein abgestimmten PyTorch-Modells hinzufügen:
\n\n\ttf_model.push_to_hub("my-awesome-model")
Wenn Sie nun zu Ihrem Hugging Face-Profil navigieren, sollten Sie Ihr neu erstelltes Modell-Repository sehen. Wenn Sie auf die Registerkarte Dateien klicken, werden alle Dateien angezeigt, die Sie in das Repository hochgeladen haben.
\nWeitere Einzelheiten zum Erstellen und Hochladen von Dateien in ein Repository finden Sie in der Hub-Dokumentation hier.
\nBenutzer, die einen no-code Ansatz bevorzugen, können ein Modell über das Webinterface des Hubs hochladen. Besuchen Sie huggingface.co/new um ein neues Repository zu erstellen:
\n![\"new_model_repo\"](\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_model_repo.png\")
Fügen Sie von hier aus einige Informationen über Ihr Modell hinzu:
\nKlicken Sie nun auf die Registerkarte Dateien und klicken Sie auf die Schaltfläche Datei hinzufügen, um eine neue Datei in Ihr Repository hochzuladen. Ziehen Sie dann eine Datei per Drag-and-Drop hoch und fügen Sie eine Übergabemeldung hinzu.
\n![\"upload_file\"](\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/upload_file.png\")
Um sicherzustellen, dass die Benutzer die Fähigkeiten, Grenzen, möglichen Verzerrungen und ethischen Aspekte Ihres Modells verstehen, fügen Sie bitte eine Modellkarte zu Ihrem Repository hinzu. Die Modellkarte wird in der Datei README.md definiert. Sie können eine Modellkarte hinzufügen, indem Sie:
Werfen Sie einen Blick auf die DistilBert model card als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei “README.md” einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation hier.
\n\n\n\t\t\n"},"__index_level_0__":{"kind":"number","value":498,"string":"498"}}},{"rowIdx":499,"cells":{"index":{"kind":"number","value":0,"string":"0"},"repo_id":{"kind":"string","value":"hf_public_repos/doc-build-dev/transformers/pr_18789"},"file_path":{"kind":"string","value":"hf_public_repos/doc-build-dev/transformers/pr_18789/de/accelerate.html"},"content":{"kind":"string","value":"\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t \n\n\n\n\n\n\nDa die Modelle immer größer werden, hat sich die Parallelität als Strategie zum Trainieren größerer Modelle auf begrenzter Hardware und zur Beschleunigung der Trainingsgeschwindigkeit um mehrere Größenordnungen erwiesen. Bei Hugging Face haben wir die Bibliothek 🤗 Accelerate entwickelt, um Nutzern zu helfen, ein 🤗 Transformers-Modell auf jeder Art von verteiltem Setup zu trainieren, egal ob es sich um mehrere GPUs auf einer Maschine oder mehrere GPUs auf mehreren Maschinen handelt. In diesem Tutorial lernen Sie, wie Sie Ihre native PyTorch-Trainingsschleife anpassen, um das Training in einer verteilten Umgebung zu ermöglichen.
\nBeginnen Sie mit der Installation von 🤗 Accelerate:
\n\n\tpip install accelerate
Dann importieren und erstellen Sie ein Accelerator-Objekt. Der Accelerator wird automatisch Ihre Art der verteilten Einrichtung erkennen und alle notwendigen Komponenten für das Training initialisieren. Sie müssen Ihr Modell nicht explizit auf einem Gerät platzieren.
from accelerate import Accelerator\n\naccelerator = Accelerator()
Der nächste Schritt ist die Übergabe aller relevanten Trainingsobjekte an die Methode prepare. Dazu gehören Ihre Trainings- und Evaluierungs-DataLoader, ein Modell und ein Optimierer:
train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(\n train_dataloader, eval_dataloader, model, optimizer\n)
Die letzte Ergänzung besteht darin, das typische loss.backward() in der Trainingsschleife durch die 🤗 Accelerate-Methode backward zu ersetzen:
for epoch in range(num_epochs):\n for batch in train_dataloader:\n outputs = model(**batch)\n loss = outputs.loss\n accelerator.backward(loss)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n progress_bar.update(1)
Wie Sie im folgenden Code sehen können, müssen Sie nur vier zusätzliche Codezeilen zu Ihrer Trainingsschleife hinzufügen, um verteiltes Training zu ermöglichen!
\n\n\t+ from accelerate import Accelerator\n from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler\n\n+ accelerator = Accelerator()\n\n model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n optimizer = AdamW(model.parameters(), lr=3e-5)\n\n- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")\n- model.to(device)\n\n+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(\n+ train_dataloader, eval_dataloader, model, optimizer\n+ )\n\n num_epochs = 3\n num_training_steps = num_epochs * len(train_dataloader)\n lr_scheduler = get_scheduler(\n "linear",\n optimizer=optimizer,\n num_warmup_steps=0,\n num_training_steps=num_training_steps\n )\n\n progress_bar = tqdm(range(num_training_steps))\n\n model.train()\n for epoch in range(num_epochs):\n for batch in train_dataloader:\n- batch = {k: v.to(device) for k, v in batch.items()}\n outputs = model(**batch)\n loss = outputs.loss\n- loss.backward()\n+ accelerator.backward(loss)\n\n optimizer.step()\n lr_scheduler.step()\n optimizer.zero_grad()\n progress_bar.update(1)
Sobald Sie die entsprechenden Codezeilen hinzugefügt haben, starten Sie Ihr Training in einem Skript oder einem Notebook wie Colaboratory.
\nWenn Sie Ihr Training mit einem Skript durchführen, führen Sie den folgenden Befehl aus, um eine Konfigurationsdatei zu erstellen und zu speichern:
\n\n\taccelerate config
Dann starten Sie Ihr Training mit:
\n\n\taccelerate launch train.py
🤗 Accelerate kann auch in einem Notebook laufen, wenn Sie planen, die TPUs von Colaboratory zu verwenden. Verpacken Sie den gesamten Code, der für das Training verantwortlich ist, in eine Funktion und übergeben Sie diese an notebook_launcher:
from accelerate import notebook_launcher\n\nnotebook_launcher(training_function)
Weitere Informationen über 🤗 Accelerate und seine umfangreichen Funktionen finden Sie in der Dokumentation.
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typing.Optional[float] = None"},{name:"bad_words_ids",val:": typing.Optional[typing.Iterable[int]] = None"},{name:"force_words_ids",val:": typing.Union[typing.Iterable[int], typing.Iterable[typing.Iterable[int]], NoneType] = None"},{name:"bos_token_id",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"length_penalty",val:": typing.Optional[float] = None"},{name:"no_repeat_ngram_size",val:": typing.Optional[int] = None"},{name:"encoder_no_repeat_ngram_size",val:": typing.Optional[int] = None"},{name:"num_return_sequences",val:": typing.Optional[int] = None"},{name:"max_time",val:": typing.Optional[float] = None"},{name:"max_new_tokens",val:": typing.Optional[int] = None"},{name:"decoder_start_token_id",val:": typing.Optional[int] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"num_beam_groups",val:": typing.Optional[int] = None"},{name:"diversity_penalty",val:": typing.Optional[float] = None"},{name:"prefix_allowed_tokens_fn",val:": typing.Union[typing.Callable[[int, torch.Tensor], typing.List[int]], NoneType] = None"},{name:"logits_processor",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = []"},{name:"stopping_criteria",val:": typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = []"},{name:"constraints",val:": typing.Optional[typing.List[transformers.generation_beam_constraints.Constraint]] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_scores",val:": typing.Optional[bool] = None"},{name:"return_dict_in_generate",val:": typing.Optional[bool] = None"},{name:"forced_bos_token_id",val:": typing.Optional[int] = None"},{name:"forced_eos_token_id",val:": typing.Optional[int] = None"},{name:"remove_invalid_values",val:": typing.Optional[bool] = None"},{name:"synced_gpus",val:": typing.Optional[bool] = False"},{name:"exponential_decay_length_penalty",val:": typing.Union[typing.Tuple[typing.Union[int, float]], NoneType] = None"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L832",parametersDescription:[{anchor:"transformers.generation_utils.GenerationMixin.generate.inputs",description:`<strong>inputs</strong> (<code>torch.Tensor</code> of varying shape depending on the modality, <em>optional</em>) —
The sequence used as a prompt for the generation or as model inputs to the encoder. If <code>None</code> the
method initializes it with <code>bos_token_id</code> and a batch size of 1. For decoder-only models <code>inputs</code>
should of in the format of <code>input_ids</code>. For encoder-decoder models <em>inputs</em> can represent any of
<code>input_ids</code>, <code>input_values</code>, <code>input_features</code>, or <code>pixel_values</code>.`,name:"inputs"},{anchor:"transformers.generation_utils.GenerationMixin.generate.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to <code>model.config.max_length</code>) —
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_utils.GenerationMixin.generate.max_new_tokens",description:`<strong>max_new_tokens</strong> (<code>int</code>, <em>optional</em>, defaults to None) —
The maximum numbers of tokens to generate, ignore the current number of tokens. Use either
<code>max_new_tokens</code> or <code>max_length</code> but not both, they serve the same purpose.`,name:"max_new_tokens"},{anchor:"transformers.generation_utils.GenerationMixin.generate.min_length",description:`<strong>min_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) —
The minimum length of the sequence to be generated.`,name:"min_length"},{anchor:"transformers.generation_utils.GenerationMixin.generate.do_sample",description:`<strong>do_sample</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to use sampling ; use greedy decoding otherwise.`,name:"do_sample"},{anchor:"transformers.generation_utils.GenerationMixin.generate.early_stopping",description:`<strong>early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.`,name:"early_stopping"},{anchor:"transformers.generation_utils.GenerationMixin.generate.num_beams",description:`<strong>num_beams</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of beams for beam search. 1 means no beam search.`,name:"num_beams"},{anchor:"transformers.generation_utils.GenerationMixin.generate.temperature",description:`<strong>temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The value used to module the next token probabilities.`,name:"temperature"},{anchor:"transformers.generation_utils.GenerationMixin.generate.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 50) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.generation_utils.GenerationMixin.generate.top_p",description:`<strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If set to float < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher
are kept for generation.`,name:"top_p"},{anchor:"transformers.generation_utils.GenerationMixin.generate.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"},{anchor:"transformers.generation_utils.GenerationMixin.generate.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.generate.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>beginning-of-sequence</em> token.`,name:"bos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.generate.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.generate.length_penalty",description:`<strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the
model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer
sequences.`,name:"length_penalty"},{anchor:"transformers.generation_utils.GenerationMixin.generate.no_repeat_ngram_size",description:`<strong>no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to int > 0, all ngrams of that size can only occur once.`,name:"no_repeat_ngram_size"},{anchor:"transformers.generation_utils.GenerationMixin.generate.encoder_no_repeat_ngram_size",description:`<strong>encoder_no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to int > 0, all ngrams of that size that occur in the <code>encoder_input_ids</code> cannot occur in the
<code>decoder_input_ids</code>.`,name:"encoder_no_repeat_ngram_size"},{anchor:"transformers.generation_utils.GenerationMixin.generate.bad_words_ids(List[List[int]],",description:`<strong>bad_words_ids(<code>List[List[int]]</code>,</strong> <em>optional</em>) —
List of token ids that are not allowed to be generated. In order to get the token ids of the words that
should not appear in the generated text, use <code>tokenizer(bad_words, add_prefix_space=True, add_special_tokens=False).input_ids</code>.`,name:"bad_words_ids(List[List[int]],"},{anchor:"transformers.generation_utils.GenerationMixin.generate.force_words_ids(List[List[int]]",description:`<strong>force_words_ids(<code>List[List[int]]</code></strong> or <code>List[List[List[int]]]</code>, <em>optional</em>) —
List of token ids that must be generated. If given a <code>List[List[int]]</code>, this is treated as a simple
list of words that must be included, the opposite to <code>bad_words_ids</code>. If given <code>List[List[List[int]]]</code>,
this triggers a <a href="https://github.com/huggingface/transformers/issues/14081" rel="nofollow">disjunctive constraint</a>,
where one can allow different forms of each word.`,name:"force_words_ids(List[List[int]]"},{anchor:"transformers.generation_utils.GenerationMixin.generate.num_return_sequences(int,",description:`<strong>num_return_sequences(<code>int</code>,</strong> <em>optional</em>, defaults to 1) —
The number of independently computed returned sequences for each element in the batch.`,name:"num_return_sequences(int,"},{anchor:"transformers.generation_utils.GenerationMixin.generate.max_time(float,",description:`<strong>max_time(<code>float</code>,</strong> <em>optional</em>, defaults to None) —
The maximum amount of time you allow the computation to run for in seconds. generation will still
finish the current pass after allocated time has been passed.`,name:"max_time(float,"},{anchor:"transformers.generation_utils.GenerationMixin.generate.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
Mask to avoid performing attention on padding token indices. Mask values are in <code>[0, 1]</code>, 1 for tokens
that are not masked, and 0 for masked tokens. If not provided, will default to a tensor the same shape
as <code>input_ids</code> that masks the pad token. <a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.generation_utils.GenerationMixin.generate.decoder_start_token_id",description:`<strong>decoder_start_token_id</strong> (<code>int</code>, <em>optional</em>) —
If an encoder-decoder model starts decoding with a different token than <em>bos</em>, the id of that token.
use_cache — (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.`,name:"decoder_start_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.generate.num_beam_groups",description:`<strong>num_beam_groups</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of
beams. <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.`,name:"num_beam_groups"},{anchor:"transformers.generation_utils.GenerationMixin.generate.diversity_penalty",description:`<strong>diversity_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
This value is subtracted from a beam’s score if it generates a token same as any beam from other group
at a particular time. Note that <code>diversity_penalty</code> is only effective if <code>group beam search</code> is
enabled.
prefix_allowed_tokens_fn — (<code>Callable[[int, torch.Tensor], List[int]]</code>, <em>optional</em>):
If provided, this function constraints the beam search to allowed tokens only at each step. If not
provided no constraint is applied. This function takes 2 arguments: the batch ID <code>batch_id</code> and
<code>input_ids</code>. It has to return a list with the allowed tokens for the next generation step conditioned
on the batch ID <code>batch_id</code> and the previously generated tokens <code>inputs_ids</code>. This argument is useful
for constrained generation conditioned on the prefix, as described in <a href="https://arxiv.org/abs/2010.00904" rel="nofollow">Autoregressive Entity
Retrieval</a>.`,name:"diversity_penalty"},{anchor:"transformers.generation_utils.GenerationMixin.generate.logits_processor",description:`<strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
Custom logits processors that complement the default logits processors built from arguments and a
model’s config. If a logit processor is passed that is already created with the arguments or a model’s
config an error is thrown. This feature is intended for advanced users.`,name:"logits_processor"},{anchor:"transformers.generation_utils.GenerationMixin.generate.stopping_criteria",description:`<strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
Custom stopping criteria that complement the default stopping criteria built from arguments and a
model’s config. If a stopping criteria is passed that is already created with the arguments or a
model’s config an error is thrown. This feature is intended for advanced users.`,name:"stopping_criteria"},{anchor:"transformers.generation_utils.GenerationMixin.generate.constraints",description:`<strong>constraints</strong> (<code>List[Constraint]</code>, <em>optional</em>) —
Custom constraints that can be added to the generation to ensure that the output will contain the use
of certain tokens as defined by <code>Constraint</code> objects, in the most sensible way possible.`,name:"constraints"},{anchor:"transformers.generation_utils.GenerationMixin.generate.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_utils.GenerationMixin.generate.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_utils.GenerationMixin.generate.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_utils.GenerationMixin.generate.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_utils.GenerationMixin.generate.forced_bos_token_id",description:`<strong>forced_bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the first generated token after the <code>decoder_start_token_id</code>. Useful
for multilingual models like <a href="../model_doc/mbart">mBART</a> where the first generated token needs to be
the target language token.`,name:"forced_bos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.generate.forced_eos_token_id",description:`<strong>forced_eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.`,name:"forced_eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.generate.remove_invalid_values",description:`<strong>remove_invalid_values</strong> (<code>bool</code>, <em>optional</em>) —
Whether to remove possible <em>nan</em> and <em>inf</em> outputs of the model to prevent the generation method to
crash. Note that using <code>remove_invalid_values</code> can slow down generation.`,name:"remove_invalid_values"},{anchor:"transformers.generation_utils.GenerationMixin.generate.synced_gpus",description:`<strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)`,name:"synced_gpus"},{anchor:"transformers.generation_utils.GenerationMixin.generate.exponential_decay_length_penalty",description:`<strong>exponential_decay_length_penalty</strong> (<code>tuple(int, float)</code>, <em>optional</em>) —
This Tuple adds an exponentially increasing length penalty, after a certain amount of tokens have been
generated. The tuple shall consist of: <code>(start_index, decay_factor)</code> where <code>start_index</code> indicates
where penalty starts and <code>decay_factor</code> represents the factor of exponential decay</p>
<p>model<em>kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If the model
is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs
should be prefixed with *decoder</em>*.`,name:"exponential_decay_length_penalty"}],returnDescription:`
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> (if
<code>return_dict_in_generate=True</code> or when <code>config.return_dict_in_generate=True</code>) or a <code>torch.FloatTensor</code>.</p>
<p>If the model is <em>not</em> an encoder-decoder model (<code>model.config.is_encoder_decoder=False</code>), the possible
<a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> types are:</p>
<ul>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchDecoderOnlyOutput"
>GreedySearchDecoderOnlyOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleDecoderOnlyOutput"
>SampleDecoderOnlyOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchDecoderOnlyOutput"
>BeamSearchDecoderOnlyOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleDecoderOnlyOutput"
>BeamSampleDecoderOnlyOutput</a></li>
</ul>
<p>If the model is an encoder-decoder model (<code>model.config.is_encoder_decoder=True</code>), the possible
<a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> types are:</p>
<ul>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchEncoderDecoderOutput"
>GreedySearchEncoderDecoderOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleEncoderDecoderOutput"
>SampleEncoderDecoderOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleEncoderDecoderOutput"
>BeamSampleEncoderDecoderOutput</a></li>
</ul>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> or <code>torch.LongTensor</code></p>
`}}),Xe=new Rm({props:{warning:"{true}",$$slots:{default:[Xm]},$$scope:{ctx:it}}}),ut=new ye({props:{code:`from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
prompt = "Today I believe we can finally"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
# generate up to 30 tokens
outputs = model.generate(input_ids, do_sample=False, max_length=30)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"Today I believe we can finally"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># generate up to 30 tokens</span>
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids, do_sample=<span class="hljs-literal">False</span>, max_length=<span class="hljs-number">30</span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Today I believe we can finally get to the point where we can make a difference in the lives of the people of the United States of America.\\n'</span>]`}}),ht=new ye({props:{code:`from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
prompt = "Today I believe we can finally"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
# sample up to 30 tokens
torch.manual_seed(0)
outputs = model.generate(input_ids, do_sample=True, max_length=30)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"Today I believe we can finally"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># sample up to 30 tokens</span>
<span class="hljs-meta">>>> </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids, do_sample=<span class="hljs-literal">True</span>, max_length=<span class="hljs-number">30</span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Today I believe we can finally get rid of discrimination," said Rep. Mark Pocan (D-Wis.).\\n\\n"Just look at the'</span>]`}}),ft=new ye({props:{code:`from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
sentence = "Paris is one of the densest populated areas in Europe."
input_ids = tokenizer(sentence, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"Helsinki-NLP/opus-mt-en-de"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"Helsinki-NLP/opus-mt-en-de"</span>)
<span class="hljs-meta">>>> </span>sentence = <span class="hljs-string">"Paris is one of the densest populated areas in Europe."</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(sentence, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Paris ist eines der dichtesten besiedelten Gebiete Europas.'</span>]`}}),bt=new be({props:{name:"greedy_search",anchor:"transformers.generation_utils.GenerationMixin.greedy_search",parameters:[{name:"input_ids",val:": LongTensor"},{name:"logits_processor",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"stopping_criteria",val:": typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_scores",val:": typing.Optional[bool] = None"},{name:"return_dict_in_generate",val:": typing.Optional[bool] = None"},{name:"synced_gpus",val:": typing.Optional[bool] = False"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L1489",parametersDescription:[{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.`,name:"input_ids"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.logits_processor",description:`<strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.`,name:"logits_processor"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.stopping_criteria",description:`<strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.`,name:"stopping_criteria"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_utils.GenerationMixin.greedy_search.synced_gpus",description:`<strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific keyword arguments will be forwarded to the <code>forward</code> function of the model.
If model is an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.`,name:"synced_gpus"}],returnDescription:`
<p><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchDecoderOnlyOutput"
>GreedySearchDecoderOnlyOutput</a>, <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchEncoderDecoderOutput"
>GreedySearchEncoderDecoderOutput</a>
or <code>torch.LongTensor</code>: A <code>torch.LongTensor</code> containing the generated tokens (default behaviour) or a
<a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchDecoderOnlyOutput"
>GreedySearchDecoderOnlyOutput</a> if <code>model.config.is_encoder_decoder=False</code> and
<code>return_dict_in_generate=True</code> or a <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchEncoderDecoderOutput"
>GreedySearchEncoderDecoderOutput</a> if
<code>model.config.is_encoder_decoder=True</code>.</p>
`}}),kt=new ye({props:{code:`from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
LogitsProcessorList,
MinLengthLogitsProcessor,
StoppingCriteriaList,
MaxLengthCriteria,
)
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
# set pad_token_id to eos_token_id because GPT2 does not have a EOS token
model.config.pad_token_id = model.config.eos_token_id
input_prompt = "It might be possible to"
input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
# instantiate logits processors
logits_processor = LogitsProcessorList(
[
MinLengthLogitsProcessor(10, eos_token_id=model.config.eos_token_id),
]
)
stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
outputs = model.greedy_search(
input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForCausalLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> StoppingCriteriaList,
<span class="hljs-meta">... </span> MaxLengthCriteria,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># set pad_token_id to eos_token_id because GPT2 does not have a EOS token</span>
<span class="hljs-meta">>>> </span>model.config.pad_token_id = model.config.eos_token_id
<span class="hljs-meta">>>> </span>input_prompt = <span class="hljs-string">"It might be possible to"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(input_prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">10</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=<span class="hljs-number">20</span>)])
<span class="hljs-meta">>>> </span>outputs = model.greedy_search(
<span class="hljs-meta">... </span> input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">"It might be possible to get a better understanding of the nature of the problem, but it's not"</span>]`}}),vt=new be({props:{name:"sample",anchor:"transformers.generation_utils.GenerationMixin.sample",parameters:[{name:"input_ids",val:": LongTensor"},{name:"logits_processor",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"stopping_criteria",val:": typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None"},{name:"logits_warper",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_scores",val:": typing.Optional[bool] = None"},{name:"return_dict_in_generate",val:": typing.Optional[bool] = None"},{name:"synced_gpus",val:": typing.Optional[bool] = False"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L1721",parametersDescription:[{anchor:"transformers.generation_utils.GenerationMixin.sample.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.`,name:"input_ids"},{anchor:"transformers.generation_utils.GenerationMixin.sample.logits_processor",description:`<strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.`,name:"logits_processor"},{anchor:"transformers.generation_utils.GenerationMixin.sample.stopping_criteria",description:`<strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.`,name:"stopping_criteria"},{anchor:"transformers.generation_utils.GenerationMixin.sample.logits_warper",description:`<strong>logits_warper</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.`,name:"logits_warper"},{anchor:"transformers.generation_utils.GenerationMixin.sample.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_utils.GenerationMixin.sample.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.sample.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.sample.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_utils.GenerationMixin.sample.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_utils.GenerationMixin.sample.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_utils.GenerationMixin.sample.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_utils.GenerationMixin.sample.synced_gpus",description:`<strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.`,name:"synced_gpus"}],returnDescription:`
<p><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleDecoderOnlyOutput"
>SampleDecoderOnlyOutput</a>, <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleEncoderDecoderOutput"
>SampleEncoderDecoderOutput</a> or
<code>torch.LongTensor</code>: A <code>torch.LongTensor</code> containing the generated tokens (default behaviour) or a
<a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleDecoderOnlyOutput"
>SampleDecoderOnlyOutput</a> if <code>model.config.is_encoder_decoder=False</code> and
<code>return_dict_in_generate=True</code> or a <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleEncoderDecoderOutput"
>SampleEncoderDecoderOutput</a> if
<code>model.config.is_encoder_decoder=True</code>.</p>
`}}),jt=new ye({props:{code:`from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
LogitsProcessorList,
MinLengthLogitsProcessor,
TopKLogitsWarper,
TemperatureLogitsWarper,
StoppingCriteriaList,
MaxLengthCriteria,
)
import torch
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
# set pad_token_id to eos_token_id because GPT2 does not have a EOS token
model.config.pad_token_id = model.config.eos_token_id
input_prompt = "Today is a beautiful day, and"
input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
# instantiate logits processors
logits_processor = LogitsProcessorList(
[
MinLengthLogitsProcessor(15, eos_token_id=model.config.eos_token_id),
]
)
# instantiate logits processors
logits_warper = LogitsProcessorList(
[
TopKLogitsWarper(50),
TemperatureLogitsWarper(0.7),
]
)
stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
torch.manual_seed(0)
outputs = model.sample(
input_ids,
logits_processor=logits_processor,
logits_warper=logits_warper,
stopping_criteria=stopping_criteria,
)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForCausalLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> TopKLogitsWarper,
<span class="hljs-meta">... </span> TemperatureLogitsWarper,
<span class="hljs-meta">... </span> StoppingCriteriaList,
<span class="hljs-meta">... </span> MaxLengthCriteria,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># set pad_token_id to eos_token_id because GPT2 does not have a EOS token</span>
<span class="hljs-meta">>>> </span>model.config.pad_token_id = model.config.eos_token_id
<span class="hljs-meta">>>> </span>input_prompt = <span class="hljs-string">"Today is a beautiful day, and"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(input_prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">15</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_warper = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> TopKLogitsWarper(<span class="hljs-number">50</span>),
<span class="hljs-meta">... </span> TemperatureLogitsWarper(<span class="hljs-number">0.7</span>),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=<span class="hljs-number">20</span>)])
<span class="hljs-meta">>>> </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">>>> </span>outputs = model.sample(
<span class="hljs-meta">... </span> input_ids,
<span class="hljs-meta">... </span> logits_processor=logits_processor,
<span class="hljs-meta">... </span> logits_warper=logits_warper,
<span class="hljs-meta">... </span> stopping_criteria=stopping_criteria,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Today is a beautiful day, and a wonderful day.\\n\\nI was lucky enough to meet the'</span>]`}}),Mt=new be({props:{name:"beam_search",anchor:"transformers.generation_utils.GenerationMixin.beam_search",parameters:[{name:"input_ids",val:": LongTensor"},{name:"beam_scorer",val:": BeamScorer"},{name:"logits_processor",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"stopping_criteria",val:": typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_scores",val:": typing.Optional[bool] = None"},{name:"return_dict_in_generate",val:": typing.Optional[bool] = None"},{name:"synced_gpus",val:": typing.Optional[bool] = False"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L1977",parametersDescription:[{anchor:"transformers.generation_utils.GenerationMixin.beam_search.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.`,name:"input_ids"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.beam_scorer",description:`<strong>beam_scorer</strong> (<code>BeamScorer</code>) —
An derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> should be read.`,name:"beam_scorer"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.logits_processor",description:`<strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.`,name:"logits_processor"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.stopping_criteria",description:`<strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.`,name:"stopping_criteria"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_utils.GenerationMixin.beam_search.synced_gpus",description:`<strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.`,name:"synced_gpus"}],returnDescription:`
<p><code>generation_utilsBeamSearchDecoderOnlyOutput</code>, <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a> or
<code>torch.LongTensor</code>: A <code>torch.LongTensor</code> containing the generated tokens (default behaviour) or a
<a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchDecoderOnlyOutput"
>BeamSearchDecoderOnlyOutput</a> if <code>model.config.is_encoder_decoder=False</code> and
<code>return_dict_in_generate=True</code> or a <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a> if
<code>model.config.is_encoder_decoder=True</code>.</p>
`}}),wt=new ye({props:{code:`from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
LogitsProcessorList,
MinLengthLogitsProcessor,
BeamSearchScorer,
)
import torch
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run beam search using 3 beams
num_beams = 3
# define decoder start token ids
input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {
"encoder_outputs": model.get_encoder()(
encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
)
}
# instantiate beam scorer
beam_scorer = BeamSearchScorer(
batch_size=1,
num_beams=num_beams,
device=model.device,
)
# instantiate logits processors
logits_processor = LogitsProcessorList(
[
MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
]
)
outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> BeamSearchScorer,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run beam search using 3 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = BeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> num_beams=num_beams,
<span class="hljs-meta">... </span> device=model.device,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt bist du?'</span>]`}}),Tt=new be({props:{name:"beam_sample",anchor:"transformers.generation_utils.GenerationMixin.beam_sample",parameters:[{name:"input_ids",val:": LongTensor"},{name:"beam_scorer",val:": BeamScorer"},{name:"logits_processor",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"stopping_criteria",val:": typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None"},{name:"logits_warper",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_scores",val:": typing.Optional[bool] = None"},{name:"return_dict_in_generate",val:": typing.Optional[bool] = None"},{name:"synced_gpus",val:": typing.Optional[bool] = False"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L2289",parametersDescription:[{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.`,name:"input_ids"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.beam_scorer",description:`<strong>beam_scorer</strong> (<code>BeamScorer</code>) —
A derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> should be read.`,name:"beam_scorer"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.logits_processor",description:`<strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.`,name:"logits_processor"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.stopping_criteria",description:`<strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.`,name:"stopping_criteria"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.logits_warper",description:`<strong>logits_warper</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.`,name:"logits_warper"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_utils.GenerationMixin.beam_sample.synced_gpus",description:`<strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.`,name:"synced_gpus"}],returnDescription:`
<p><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleDecoderOnlyOutput"
>BeamSampleDecoderOnlyOutput</a>, <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleEncoderDecoderOutput"
>BeamSampleEncoderDecoderOutput</a> or
<code>torch.LongTensor</code>: A <code>torch.LongTensor</code> containing the generated tokens (default behaviour) or a
<a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleDecoderOnlyOutput"
>BeamSampleDecoderOnlyOutput</a> if <code>model.config.is_encoder_decoder=False</code> and
<code>return_dict_in_generate=True</code> or a <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleEncoderDecoderOutput"
>BeamSampleEncoderDecoderOutput</a> if
<code>model.config.is_encoder_decoder=True</code>.</p>
`}}),Ot=new ye({props:{code:`from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
LogitsProcessorList,
MinLengthLogitsProcessor,
TopKLogitsWarper,
TemperatureLogitsWarper,
BeamSearchScorer,
)
import torch
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run beam search using 3 beams
num_beams = 3
# define decoder start token ids
input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {
"encoder_outputs": model.get_encoder()(
encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
)
}
# instantiate beam scorer
beam_scorer = BeamSearchScorer(
batch_size=1,
max_length=model.config.max_length,
num_beams=num_beams,
device=model.device,
)
# instantiate logits processors
logits_processor = LogitsProcessorList(
[MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id)]
)
# instantiate logits processors
logits_warper = LogitsProcessorList(
[
TopKLogitsWarper(50),
TemperatureLogitsWarper(0.7),
]
)
outputs = model.beam_sample(
input_ids, beam_scorer, logits_processor=logits_processor, logits_warper=logits_warper, **model_kwargs
)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> TopKLogitsWarper,
<span class="hljs-meta">... </span> TemperatureLogitsWarper,
<span class="hljs-meta">... </span> BeamSearchScorer,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run beam search using 3 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = BeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> max_length=model.config.max_length,
<span class="hljs-meta">... </span> num_beams=num_beams,
<span class="hljs-meta">... </span> device=model.device,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id)]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_warper = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> TopKLogitsWarper(<span class="hljs-number">50</span>),
<span class="hljs-meta">... </span> TemperatureLogitsWarper(<span class="hljs-number">0.7</span>),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.beam_sample(
<span class="hljs-meta">... </span> input_ids, beam_scorer, logits_processor=logits_processor, logits_warper=logits_warper, **model_kwargs
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt bist du?'</span>]`}}),qt=new be({props:{name:"group_beam_search",anchor:"transformers.generation_utils.GenerationMixin.group_beam_search",parameters:[{name:"input_ids",val:": LongTensor"},{name:"beam_scorer",val:": BeamScorer"},{name:"logits_processor",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"stopping_criteria",val:": typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_scores",val:": typing.Optional[bool] = None"},{name:"return_dict_in_generate",val:": typing.Optional[bool] = None"},{name:"synced_gpus",val:": typing.Optional[bool] = False"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L2611",parametersDescription:[{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.`,name:"input_ids"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.beam_scorer",description:`<strong>beam_scorer</strong> (<code>BeamScorer</code>) —
An derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> should be read.`,name:"beam_scorer"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.logits_processor",description:`<strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.`,name:"logits_processor"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.stopping_criteria",description:`<strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.`,name:"stopping_criteria"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_utils.GenerationMixin.group_beam_search.synced_gpus",description:`<strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)</p>
<p>model_kwargs —
Additional model specific kwargs that will be forwarded to the <code>forward</code> function of the model. If
model is an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.`,name:"synced_gpus"}],returnDescription:`
<p><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchDecoderOnlyOutput"
>BeamSearchDecoderOnlyOutput</a>, <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a> or
<code>torch.LongTensor</code>: A <code>torch.LongTensor</code> containing the generated tokens (default behaviour) or a
<a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchDecoderOnlyOutput"
>BeamSearchDecoderOnlyOutput</a> if <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchDecoderOnlyOutput"
>BeamSearchDecoderOnlyOutput</a> if
<code>model.config.is_encoder_decoder=False</code> and <code>return_dict_in_generate=True</code> or a
<a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a> if <code>model.config.is_encoder_decoder=True</code>.</p>
`}}),St=new ye({props:{code:`from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
LogitsProcessorList,
MinLengthLogitsProcessor,
HammingDiversityLogitsProcessor,
BeamSearchScorer,
)
import torch
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run diverse beam search using 6 beams
num_beams = 6
# define decoder start token ids
input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {
"encoder_outputs": model.get_encoder()(
encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
)
}
# instantiate beam scorer
beam_scorer = BeamSearchScorer(
batch_size=1,
max_length=model.config.max_length,
num_beams=num_beams,
device=model.device,
num_beam_groups=3,
)
# instantiate logits processors
logits_processor = LogitsProcessorList(
[
HammingDiversityLogitsProcessor(5.5, num_beams=6, num_beam_groups=3),
MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
]
)
outputs = model.group_beam_search(
input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs
)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> HammingDiversityLogitsProcessor,
<span class="hljs-meta">... </span> BeamSearchScorer,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run diverse beam search using 6 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">6</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = BeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> max_length=model.config.max_length,
<span class="hljs-meta">... </span> num_beams=num_beams,
<span class="hljs-meta">... </span> device=model.device,
<span class="hljs-meta">... </span> num_beam_groups=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> HammingDiversityLogitsProcessor(<span class="hljs-number">5.5</span>, num_beams=<span class="hljs-number">6</span>, num_beam_groups=<span class="hljs-number">3</span>),
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.group_beam_search(
<span class="hljs-meta">... </span> input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt bist du?'</span>]`}}),$t=new be({props:{name:"constrained_beam_search",anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search",parameters:[{name:"input_ids",val:": LongTensor"},{name:"constrained_beam_scorer",val:": ConstrainedBeamSearchScorer"},{name:"logits_processor",val:": typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None"},{name:"stopping_criteria",val:": typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"output_scores",val:": typing.Optional[bool] = None"},{name:"return_dict_in_generate",val:": typing.Optional[bool] = None"},{name:"synced_gpus",val:": typing.Optional[bool] = None"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L2976",parametersDescription:[{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.`,name:"input_ids"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.constrained_beam_scorer",description:`<strong>constrained_beam_scorer</strong> (<code>ConstrainedBeamSearchScorer</code>) —
A derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation, while satisfying a list of positive constraints. For more information, the
documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.ConstrainedBeamSearchScorer">ConstrainedBeamSearchScorer</a> should be read.`,name:"constrained_beam_scorer"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.logits_processor",description:`<strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.`,name:"logits_processor"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.stopping_criteria",description:`<strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.`,name:"stopping_criteria"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.logits_warper",description:`<strong>logits_warper</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.`,name:"logits_warper"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_utils.GenerationMixin.constrained_beam_search.synced_gpus",description:`<strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.`,name:"synced_gpus"}],returnDescription:`
<p><code>generation_utilsBeamSearchDecoderOnlyOutput</code>, <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a> or
<code>torch.LongTensor</code>: A <code>torch.LongTensor</code> containing the generated tokens (default behaviour) or a
<a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchDecoderOnlyOutput"
>BeamSearchDecoderOnlyOutput</a> if <code>model.config.is_encoder_decoder=False</code> and
<code>return_dict_in_generate=True</code> or a <a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a> if
<code>model.config.is_encoder_decoder=True</code>.</p>
`}}),At=new ye({props:{code:`from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
LogitsProcessorList,
MinLengthLogitsProcessor,
ConstrainedBeamSearchScorer,
PhrasalConstraint,
)
import torch
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
encoder_input_str = "translate English to German: How old are you?"
encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
# lets run beam search using 3 beams
num_beams = 3
# define decoder start token ids
input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
input_ids = input_ids * model.config.decoder_start_token_id
# add encoder_outputs to model keyword arguments
model_kwargs = {
"encoder_outputs": model.get_encoder()(
encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
)
}
constraint_str = "sind"
constraint_token_ids = tokenizer.encode(constraint_str)[:-1] # slice to remove eos token
constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]
# instantiate beam scorer
beam_scorer = ConstrainedBeamSearchScorer(
batch_size=1, num_beams=num_beams, device=model.device, constraints=constraints
)
# instantiate logits processors
logits_processor = LogitsProcessorList(
[
MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
]
)
outputs = model.constrained_beam_search(
input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> ConstrainedBeamSearchScorer,
<span class="hljs-meta">... </span> PhrasalConstraint,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run beam search using 3 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span>constraint_str = <span class="hljs-string">"sind"</span>
<span class="hljs-meta">>>> </span>constraint_token_ids = tokenizer.encode(constraint_str)[:-<span class="hljs-number">1</span>] <span class="hljs-comment"># slice to remove eos token</span>
<span class="hljs-meta">>>> </span>constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = ConstrainedBeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>, num_beams=num_beams, device=model.device, constraints=constraints
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.constrained_beam_search(
<span class="hljs-meta">... </span> input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt sind Sie?'</span>]`}}),zt=new Cs({}),Pt=new be({props:{name:"class transformers.generation_tf_utils.TFGenerationMixin",anchor:"transformers.generation_tf_utils.TFGenerationMixin",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_utils.py#L342"}}),Dt=new be({props:{name:"generate",anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate",parameters:[{name:"input_ids",val:" = None"},{name:"max_length",val:" = None"},{name:"min_length",val:" = None"},{name:"do_sample",val:" = None"},{name:"early_stopping",val:" = None"},{name:"num_beams",val:" = None"},{name:"temperature",val:" = None"},{name:"top_k",val:" = None"},{name:"top_p",val:" = None"},{name:"repetition_penalty",val:" = None"},{name:"bad_words_ids",val:" = None"},{name:"bos_token_id",val:" = None"},{name:"pad_token_id",val:" = None"},{name:"eos_token_id",val:" = None"},{name:"length_penalty",val:" = None"},{name:"no_repeat_ngram_size",val:" = None"},{name:"num_return_sequences",val:" = None"},{name:"attention_mask",val:" = None"},{name:"decoder_start_token_id",val:" = None"},{name:"use_cache",val:" = None"},{name:"output_scores",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict_in_generate",val:" = None"},{name:"forced_bos_token_id",val:" = None"},{name:"forced_eos_token_id",val:" = None"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_utils.py#L362",parametersDescription:[{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.input_ids",description:"<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, `(batch_size, sequence_length, —",name:"input_ids"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.feature_dim)`",description:`<strong>feature_dim)\`</strong> or <code>(batch_size, num_channels, height, width)</code>, <em>optional</em>) —
The sequence used as a prompt for the generation or as model inputs to the encoder. If <code>None</code> the
method initializes it with <code>bos_token_id</code> and a batch size of 1. For decoder-only models <code>inputs</code>
should of in the format of <code>input_ids</code>. For encoder-decoder models <em>inputs</em> can represent any of
<code>input_ids</code>, <code>input_values</code>, <code>input_features</code>, or <code>pixel_values</code>.`,name:"feature_dim)`"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.min_length",description:`<strong>min_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) —
The minimum length of the sequence to be generated.`,name:"min_length"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.do_sample",description:`<strong>do_sample</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to use sampling ; use greedy decoding otherwise.`,name:"do_sample"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.early_stopping",description:`<strong>early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.`,name:"early_stopping"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.num_beams",description:`<strong>num_beams</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of beams for beam search. 1 means no beam search.`,name:"num_beams"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.temperature",description:`<strong>temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The value used to module the next token probabilities.`,name:"temperature"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 50) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.top_p",description:`<strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If set to float < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher
are kept for generation.`,name:"top_p"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>beginning-of-sequence</em> token.`,name:"bos_token_id"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.length_penalty",description:`<strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty.</p>
<p>Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in
order to encourage the model to produce longer sequences.`,name:"length_penalty"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.no_repeat_ngram_size",description:`<strong>no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to int > 0, all ngrams of that size can only occur once.`,name:"no_repeat_ngram_size"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.bad_words_ids(List[int],",description:`<strong>bad_words_ids(<code>List[int]</code>,</strong> <em>optional</em>) —
List of token ids that are not allowed to be generated. In order to get the tokens of the words that
should not appear in the generated text, use <code>tokenizer.encode(bad_word, add_prefix_space=True)</code>.`,name:"bad_words_ids(List[int],"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.num_return_sequences(int,",description:`<strong>num_return_sequences(<code>int</code>,</strong> <em>optional</em>, defaults to 1) —
The number of independently computed returned sequences for each element in the batch.`,name:"num_return_sequences(int,"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.attention_mask",description:`<strong>attention_mask</strong> (<code>tf.Tensor</code> of <code>dtype=tf.int32</code> and shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
Mask to avoid performing attention on padding token indices. Mask values are in <code>[0, 1]</code>, 1 for tokens
that are not masked, and 0 for masked tokens.</p>
<p>If not provided, will default to a tensor the same shape as <code>input_ids</code> that masks the pad token.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.decoder_start_token_id",description:`<strong>decoder_start_token_id</strong> (<code>int</code>, <em>optional</em>) —
If an encoder-decoder model starts decoding with a different token than <em>bos</em>, the id of that token.
use_cache — (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.`,name:"decoder_start_token_id"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.`,name:"output_attentions"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.`,name:"output_hidden_states"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.output_scores",description:`<strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.`,name:"output_scores"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.return_dict_in_generate",description:`<strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict_in_generate"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.forced_bos_token_id",description:`<strong>forced_bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the first generated token after the <code>decoder_start_token_id</code>. Useful
for multilingual models like <a href="../model_doc/mbart">mBART</a> where the first generated token needs to be
the target language token.`,name:"forced_bos_token_id"},{anchor:"transformers.generation_tf_utils.TFGenerationMixin.generate.forced_eos_token_id",description:`<strong>forced_eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.
model_specific_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model.`,name:"forced_eos_token_id"}],returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> or <code>tf.Tensor</code></p>
`}}),Ht=new ye({props:{code:`tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"distilgpt2"
) # Download model and configuration from huggingface.co and cache.
outputs = model.generate(max_length=40) # do greedy decoding
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("openai-gpt") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"openai-gpt"
) # Download model and configuration from huggingface.co and cache.
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5
) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
for i in range(3): # 3 output sequences were generated
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("distilgpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"distilgpt2"
) # Download model and configuration from huggingface.co and cache.
input_context = "The dog"
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True
) # generate 3 candidates using sampling
for i in range(3): # 3 output sequences were generated
print(f"Generated {i}: {tokenizer.decode(outputs[i], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("ctrl") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"ctrl"
) # Download model and configuration from huggingface.co and cache.
input_context = "Legal My neighbor is" # "Legal" is one of the control codes for ctrl
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2
) # generate sequences
print(f"Generated: {tokenizer.decode(outputs[0], skip_special_tokens=True)}")
tokenizer = AutoTokenizer.from_pretrained("gpt2") # Initialize tokenizer
model = TFAutoModelWithLMHead.from_pretrained(
"gpt2"
) # Download model and configuration from huggingface.co and cache.
input_context = "My cute dog"
bad_words_ids = [
tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ["idiot", "stupid", "shut up"]
]
input_ids = tokenizer.encode(input_context, return_tensors="tf") # encode input context
outputs = model.generate(
input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids
) # generate sequences without allowing bad_words to be generated`,highlighted:`tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"distilgpt2"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
outputs = model.generate(max_length=<span class="hljs-number">40</span>) <span class="hljs-comment"># do greedy decoding</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated: <span class="hljs-subst">{tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"openai-gpt"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"openai-gpt"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"The dog"</span>
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, num_beams=<span class="hljs-number">5</span>, num_return_sequences=<span class="hljs-number">3</span>, temperature=<span class="hljs-number">1.5</span>
) <span class="hljs-comment"># generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>): <span class="hljs-comment"># 3 output sequences were generated</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated <span class="hljs-subst">{i}</span>: <span class="hljs-subst">{tokenizer.decode(outputs[i], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"distilgpt2"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"The dog"</span>
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, max_length=<span class="hljs-number">40</span>, temperature=<span class="hljs-number">0.7</span>, num_return_sequences=<span class="hljs-number">3</span>, do_sample=<span class="hljs-literal">True</span>
) <span class="hljs-comment"># generate 3 candidates using sampling</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>): <span class="hljs-comment"># 3 output sequences were generated</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated <span class="hljs-subst">{i}</span>: <span class="hljs-subst">{tokenizer.decode(outputs[i], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"ctrl"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"ctrl"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"Legal My neighbor is"</span> <span class="hljs-comment"># "Legal" is one of the control codes for ctrl</span>
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, max_length=<span class="hljs-number">50</span>, temperature=<span class="hljs-number">0.7</span>, repetition_penalty=<span class="hljs-number">1.2</span>
) <span class="hljs-comment"># generate sequences</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated: <span class="hljs-subst">{tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"gpt2"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"My cute dog"</span>
bad_words_ids = [
tokenizer.encode(bad_word, add_prefix_space=<span class="hljs-literal">True</span>) <span class="hljs-keyword">for</span> bad_word <span class="hljs-keyword">in</span> [<span class="hljs-string">"idiot"</span>, <span class="hljs-string">"stupid"</span>, <span class="hljs-string">"shut up"</span>]
]
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, max_length=<span class="hljs-number">100</span>, do_sample=<span class="hljs-literal">True</span>, bad_words_ids=bad_words_ids
) <span class="hljs-comment"># generate sequences without allowing bad_words to be generated</span>`}}),Rt=new Cs({}),Ut=new be({props:{name:"class transformers.generation_flax_utils.FlaxGenerationMixin",anchor:"transformers.generation_flax_utils.FlaxGenerationMixin",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_utils.py#L119"}}),Zt=new be({props:{name:"generate",anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate",parameters:[{name:"input_ids",val:": ndarray"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"bos_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"},{name:"decoder_start_token_id",val:": typing.Optional[int] = None"},{name:"do_sample",val:": typing.Optional[bool] = None"},{name:"prng_key",val:": typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None"},{name:"top_k",val:": typing.Optional[int] = None"},{name:"top_p",val:": typing.Optional[float] = None"},{name:"temperature",val:": typing.Optional[float] = None"},{name:"num_beams",val:": typing.Optional[int] = None"},{name:"no_repeat_ngram_size",val:": typing.Optional[int] = None"},{name:"min_length",val:": typing.Optional[int] = None"},{name:"forced_bos_token_id",val:": typing.Optional[int] = None"},{name:"forced_eos_token_id",val:": typing.Optional[int] = None"},{name:"length_penalty",val:": typing.Optional[float] = None"},{name:"early_stopping",val:": typing.Optional[bool] = None"},{name:"trace",val:": bool = True"},{name:"params",val:": typing.Union[typing.Dict[str, jax._src.numpy.lax_numpy.ndarray], NoneType] = None"},{name:"**model_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_utils.py#L163",parametersDescription:[{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.`,name:"input_ids"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.do_sample",description:`<strong>do_sample</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to use sampling ; use greedy decoding otherwise.`,name:"do_sample"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.temperature",description:`<strong>temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The value used to module the next token probabilities.`,name:"temperature"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 50) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.top_p",description:`<strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If set to float < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher
are kept for generation.`,name:"top_p"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>beginning-of-sequence</em> token.`,name:"bos_token_id"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.num_beams",description:`<strong>num_beams</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of beams for beam search. 1 means no beam search.`,name:"num_beams"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.decoder_start_token_id",description:`<strong>decoder_start_token_id</strong> (<code>int</code>, <em>optional</em>) —
If an encoder-decoder model starts decoding with a different token than <em>bos</em>, the id of that token.`,name:"decoder_start_token_id"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.trace",description:`<strong>trace</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to trace generation. Setting <code>trace=False</code> should only be used for debugging and will lead to a
considerably slower runtime.`,name:"trace"},{anchor:"transformers.generation_flax_utils.FlaxGenerationMixin.generate.params",description:`<strong>params</strong> (<code>Dict[str, jnp.ndarray]</code>, <em>optional</em>) —
Optionally the model parameters can be passed. Can be useful for parallelized generation.
model<em>kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If the model
is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs
should be prefixed with *decoder</em>*. Also accepts <code>encoder_outputs</code> to skip encoder part.`,name:"params"}],returnDescription:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a>.</p>
`}}),Ye=new Rm({props:{warning:"{true}",$$slots:{default:[Jm]},$$scope:{ctx:it}}}),Qt=new ye({props:{code:`from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = FlaxAutoModelForCausalLM.from_pretrained("distilgpt2")
input_context = "The dog"
# encode input context
input_ids = tokenizer(input_context, return_tensors="np").input_ids
# generate candidates using sampling
outputs = model.generate(input_ids=input_ids, max_length=20, top_k=30, do_sample=True)
tokenizer.batch_decode(outputs, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxAutoModelForCausalLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)
<span class="hljs-meta">>>> </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)
<span class="hljs-meta">>>> </span>input_context = <span class="hljs-string">"The dog"</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># encode input context</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(input_context, return_tensors=<span class="hljs-string">"np"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># generate candidates using sampling</span>
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids=input_ids, max_length=<span class="hljs-number">20</span>, top_k=<span class="hljs-number">30</span>, do_sample=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)`}}),{c(){p=s("meta"),P=l(),j=s("h1"),T=s("a"),I=s("span"),m(E.$$.fragment),xe=l(),W=s("span"),G=t("Generation"),B=l(),v=s("p"),$=t("The methods for auto-regressive text generation, namely "),b=s("a"),Me=t("generate()"),Le=t(" (for the PyTorch models), "),H=s("a"),Is=t("generate()"),Ws=t(" (for the TensorFlow models) and "),en=s("a"),Bs=t("generate()"),Hs=t(" (for the Flax/JAX models), are implemented in "),tn=s("a"),Rs=t("GenerationMixin"),Us=t(", "),nn=s("a"),Vs=t("TFGenerationMixin"),Ks=t(" and "),on=s("a"),Zs=t("FlaxGenerationMixin"),Xs=t(" respectively."),_s=l(),S=s("p"),Js=t("The "),On=s("code"),Qs=t("GenerationMixin"),Ys=t(" classes are inherited by the corresponding base model classes, "),qn=s("em"),ea=t("e.g."),ta=l(),sn=s("a"),na=t("PreTrainedModel"),oa=t(", "),an=s("a"),sa=t("TFPreTrainedModel"),aa=t(", and "),rn=s("a"),ra=t("FlaxPreTrainedModel"),ia=t(` respectively, therefore exposing all
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`),oo=a(Ne,"CODE",{});var yp=r(oo);Pr=n(yp,"do_sample=True"),yp.forEach(o),Nr=n(Ne,"."),Ne.forEach(o),Dr=d(fe),Y=a(fe,"LI",{});var De=r(Y);so=a(De,"EM",{});var jp=r(so);Cr=n(jp,"beam-search decoding"),jp.forEach(o),Ir=n(De," by calling "),bn=a(De,"A",{href:!0});var Mp=r(bn);Wr=n(Mp,"beam_search()"),Mp.forEach(o),Br=n(De," if "),ao=a(De,"CODE",{});var Lp=r(ao);Hr=n(Lp,"num_beams>1"),Lp.forEach(o),Rr=n(De,` and
`),ro=a(De,"CODE",{});var wp=r(ro);Ur=n(wp,"do_sample=False"),wp.forEach(o),Vr=n(De,"."),De.forEach(o),Kr=d(fe),ee=a(fe,"LI",{});var Ce=r(ee);io=a(Ce,"EM",{});var Tp=r(io);Zr=n(Tp,"beam-search multinomial sampling"),Tp.forEach(o),Xr=n(Ce," by calling "),xn=a(Ce,"A",{href:!0});var Ep=r(xn);Jr=n(Ep,"beam_sample()"),Ep.forEach(o),Qr=n(Ce,` if
`),lo=a(Ce,"CODE",{});var Op=r(lo);Yr=n(Op,"num_beams>1"),Op.forEach(o),ei=n(Ce," and "),co=a(Ce,"CODE",{});var qp=r(co);ti=n(qp,"do_sample=True"),qp.forEach(o),ni=n(Ce,"."),Ce.forEach(o),oi=d(fe),te=a(fe,"LI",{});var Ie=r(te);po=a(Ie,"EM",{});var Gp=r(po);si=n(Gp,"diverse beam-search decoding"),Gp.forEach(o),ai=n(Ie," by calling "),kn=a(Ie,"A",{href:!0});var Sp=r(kn);ri=n(Sp,"group_beam_search()"),Sp.forEach(o),ii=n(Ie,`, if
`),mo=a(Ie,"CODE",{});var $p=r(mo);li=n($p,"num_beams>1"),$p.forEach(o),di=n(Ie," and "),go=a(Ie,"CODE",{});var Fp=r(go);ci=n(Fp,"num_beam_groups>1"),Fp.forEach(o),pi=n(Ie,"."),Ie.forEach(o),mi=d(fe),ne=a(fe,"LI",{});var We=r(ne);_o=a(We,"EM",{});var Ap=r(_o);gi=n(Ap,"constrained beam-search decoding"),Ap.forEach(o),_i=n(We,` by calling
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R=new se({}),Ro=new Kb({props:{warning:"{true}",$$slots:{default:[cL]},$$scope:{ctx:Ye}}}),pa=new P({props:{code:`from torch import nn
from transformers import Trainer
class CustomTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
labels = inputs.get("labels")
# forward pass
outputs = model(**inputs)
logits = outputs.get("logits")
# compute custom loss (suppose one has 3 labels with different weights)
loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0]))
loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
return (loss, outputs) if return_outputs else loss`,highlighted:`<span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer
<span class="hljs-keyword">class</span> <span class="hljs-title class_">CustomTrainer</span>(<span class="hljs-title class_ inherited__">Trainer</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_loss</span>(<span class="hljs-params">self, model, inputs, return_outputs=<span class="hljs-literal">False</span></span>):
labels = inputs.get(<span class="hljs-string">"labels"</span>)
<span class="hljs-comment"># forward pass</span>
outputs = model(**inputs)
logits = outputs.get(<span class="hljs-string">"logits"</span>)
<span class="hljs-comment"># compute custom loss (suppose one has 3 labels with different weights)</span>
loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([<span class="hljs-number">1.0</span>, <span class="hljs-number">2.0</span>, <span class="hljs-number">3.0</span>]))
loss = loss_fct(logits.view(-<span class="hljs-number">1</span>, self.model.config.num_labels), labels.view(-<span class="hljs-number">1</span>))
<span class="hljs-keyword">return</span> (loss, outputs) <span class="hljs-keyword">if</span> return_outputs <span class="hljs-keyword">else</span> loss`}}),ma=new se({}),ha=new $({props:{name:"class transformers.Trainer",anchor:"transformers.Trainer",parameters:[{name:"model",val:": typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] = None"},{name:"args",val:": TrainingArguments = None"},{name:"data_collator",val:": typing.Optional[DataCollator] = None"},{name:"train_dataset",val:": typing.Optional[torch.utils.data.dataset.Dataset] = None"},{name:"eval_dataset",val:": typing.Optional[torch.utils.data.dataset.Dataset] = None"},{name:"tokenizer",val:": typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None"},{name:"model_init",val:": typing.Callable[[], transformers.modeling_utils.PreTrainedModel] = None"},{name:"compute_metrics",val:": typing.Union[typing.Callable[[transformers.trainer_utils.EvalPrediction], typing.Dict], NoneType] = None"},{name:"callbacks",val:": typing.Optional[typing.List[transformers.trainer_callback.TrainerCallback]] = None"},{name:"optimizers",val:": typing.Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)"},{name:"preprocess_logits_for_metrics",val:": typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L199",parametersDescription:[{anchor:"transformers.Trainer.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <code>torch.nn.Module</code>, <em>optional</em>) —
The model to train, evaluate or use for predictions. If not provided, a <code>model_init</code> must be passed.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p><a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> is optimized to work with the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> provided by the library. You can still use
your own models defined as <code>torch.nn.Module</code> as long as they work the same way as the 🤗 Transformers
models.</p>
</div>`,name:"model"},{anchor:"transformers.Trainer.args",description:`<strong>args</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>, <em>optional</em>) —
The arguments to tweak for training. Will default to a basic instance of <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> with the
<code>output_dir</code> set to a directory named <em>tmp_trainer</em> in the current directory if not provided.`,name:"args"},{anchor:"transformers.Trainer.data_collator",description:`<strong>data_collator</strong> (<code>DataCollator</code>, <em>optional</em>) —
The function to use to form a batch from a list of elements of <code>train_dataset</code> or <code>eval_dataset</code>. Will
default to <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.default_data_collator">default_data_collator()</a> if no <code>tokenizer</code> is provided, an instance of
<a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorWithPadding">DataCollatorWithPadding</a> otherwise.`,name:"data_collator"},{anchor:"transformers.Trainer.train_dataset",description:`<strong>train_dataset</strong> (<code>torch.utils.data.Dataset</code> or <code>torch.utils.data.IterableDataset</code>, <em>optional</em>) —
The dataset to use for training. If it is an <code>datasets.Dataset</code>, columns not accepted by the
<code>model.forward()</code> method are automatically removed.</p>
<p>Note that if it’s a <code>torch.utils.data.IterableDataset</code> with some randomization and you are training in a
distributed fashion, your iterable dataset should either use a internal attribute <code>generator</code> that is a
<code>torch.Generator</code> for the randomization that must be identical on all processes (and the Trainer will
manually set the seed of this <code>generator</code> at each epoch) or have a <code>set_epoch()</code> method that internally
sets the seed of the RNGs used.`,name:"train_dataset"},{anchor:"transformers.Trainer.eval_dataset",description:`<strong>eval_dataset</strong> (<code>torch.utils.data.Dataset</code>, <em>optional</em>) —
The dataset to use for evaluation. If it is an <code>datasets.Dataset</code>, columns not accepted by the
<code>model.forward()</code> method are automatically removed.`,name:"eval_dataset"},{anchor:"transformers.Trainer.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a>, <em>optional</em>) —
The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs the
maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
interrupted training or reuse the fine-tuned model.`,name:"tokenizer"},{anchor:"transformers.Trainer.model_init",description:`<strong>model_init</strong> (<code>Callable[[], PreTrainedModel]</code>, <em>optional</em>) —
A function that instantiates the model to be used. If provided, each call to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> will start
from a new instance of the model as given by this function.</p>
<p>The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
be able to choose different architectures according to hyper parameters (such as layer count, sizes of
inner layers, dropout probabilities etc).`,name:"model_init"},{anchor:"transformers.Trainer.compute_metrics",description:`<strong>compute_metrics</strong> (<code>Callable[[EvalPrediction], Dict]</code>, <em>optional</em>) —
The function that will be used to compute metrics at evaluation. Must take a <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.EvalPrediction">EvalPrediction</a> and return
a dictionary string to metric values.`,name:"compute_metrics"},{anchor:"transformers.Trainer.callbacks",description:`<strong>callbacks</strong> (List of <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>, <em>optional</em>) —
A list of callbacks to customize the training loop. Will add those to the list of default callbacks
detailed in <a href="callback">here</a>.</p>
<p>If you want to remove one of the default callbacks used, use the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.remove_callback">Trainer.remove_callback()</a> method.`,name:"callbacks"},{anchor:"transformers.Trainer.optimizers",description:`<strong>optimizers</strong> (<code>Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]</code>, <em>optional</em>) — A tuple
containing the optimizer and the scheduler to use. Will default to an instance of <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> on your model
and a scheduler given by <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.get_linear_schedule_with_warmup">get_linear_schedule_with_warmup()</a> controlled by <code>args</code>.`,name:"optimizers"},{anchor:"transformers.Trainer.preprocess_logits_for_metrics",description:`<strong>preprocess_logits_for_metrics</strong> (<code>Callable[[torch.Tensor, torch.Tensor], torch.Tensor]</code>, <em>optional</em>) —
A function that preprocess the logits right before caching them at each evaluation step. Must take two
tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
by this function will be reflected in the predictions received by <code>compute_metrics</code>.</p>
<p>Note that the labels (second parameter) will be <code>None</code> if the dataset does not have them.`,name:"preprocess_logits_for_metrics"}]}}),ua=new $({props:{name:"add_callback",anchor:"transformers.Trainer.add_callback",parameters:[{name:"callback",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L513",parametersDescription:[{anchor:"transformers.Trainer.add_callback.callback",description:`<strong>callback</strong> (<code>type</code> or <code>TrainerCallback</code>) —
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<p><code>int</code></p>
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<ul>
<li>the documentation of
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<li>the documentation of <a href="https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run" rel="nofollow">tune.run</a></li>
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train_mem_gpu_alloc_delta = 693MB
train_mem_gpu_peaked_delta = 7MB`,highlighted:`<span class="hljs-attr">init_mem_cpu_alloc_delta</span> = <span class="hljs-number">1301</span>MB
<span class="hljs-attr">init_mem_cpu_peaked_delta</span> = <span class="hljs-number">154</span>MB
<span class="hljs-attr">init_mem_gpu_alloc_delta</span> = <span class="hljs-number">230</span>MB
<span class="hljs-attr">init_mem_gpu_peaked_delta</span> = <span class="hljs-number">0</span>MB
<span class="hljs-attr">train_mem_cpu_alloc_delta</span> = <span class="hljs-number">1345</span>MB
<span class="hljs-attr">train_mem_cpu_peaked_delta</span> = <span class="hljs-number">0</span>MB
<span class="hljs-attr">train_mem_gpu_alloc_delta</span> = <span class="hljs-number">693</span>MB
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Whether or not to return the loss only.`,name:"prediction_loss_only"},{anchor:"transformers.Trainer.prediction_step.ignore_keys",description:`<strong>ignore_keys</strong> (<code>Lst[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.`,name:"ignore_keys"}],returnDescription:`
<p>A tuple with the loss,
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`,returnType:`
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`}}),an=new $({props:{name:"push_to_hub",anchor:"transformers.Trainer.push_to_hub",parameters:[{name:"commit_message",val:": typing.Optional[str] = 'End of training'"},{name:"blocking",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2824",parametersDescription:[{anchor:"transformers.Trainer.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"End of training"</code>) —
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Additional keyword arguments passed along to <code>create_model_card()</code>`,name:"blocking"}],returnDescription:`
<p>The url of the commit of your model in the given repository if <code>blocking=False</code>, a tuple with the url of
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`}}),nn=new $({props:{name:"remove_callback",anchor:"transformers.Trainer.remove_callback",parameters:[{name:"callback",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L540",parametersDescription:[{anchor:"transformers.Trainer.remove_callback.callback",description:`<strong>callback</strong> (<code>type</code> or <code>TrainerCallback</code>) —
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`,returnType:`
<p><code>torch.Tensor</code></p>
`}}),gn=new se({}),_n=new $({props:{name:"class transformers.Seq2SeqTrainer",anchor:"transformers.Seq2SeqTrainer",parameters:[{name:"model",val:": typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] = None"},{name:"args",val:": TrainingArguments = None"},{name:"data_collator",val:": typing.Optional[DataCollator] = None"},{name:"train_dataset",val:": typing.Optional[torch.utils.data.dataset.Dataset] = None"},{name:"eval_dataset",val:": typing.Optional[torch.utils.data.dataset.Dataset] = None"},{name:"tokenizer",val:": typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None"},{name:"model_init",val:": typing.Callable[[], transformers.modeling_utils.PreTrainedModel] = None"},{name:"compute_metrics",val:": typing.Union[typing.Callable[[transformers.trainer_utils.EvalPrediction], typing.Dict], NoneType] = None"},{name:"callbacks",val:": typing.Optional[typing.List[transformers.trainer_callback.TrainerCallback]] = None"},{name:"optimizers",val:": typing.Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)"},{name:"preprocess_logits_for_metrics",val:": typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_seq2seq.py#L30"}}),vn=new $({props:{name:"evaluate",anchor:"transformers.Seq2SeqTrainer.evaluate",parameters:[{name:"eval_dataset",val:": typing.Optional[torch.utils.data.dataset.Dataset] = None"},{name:"ignore_keys",val:": typing.Optional[typing.List[str]] = None"},{name:"metric_key_prefix",val:": str = 'eval'"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"num_beams",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_seq2seq.py#L31",parametersDescription:[{anchor:"transformers.Seq2SeqTrainer.evaluate.eval_dataset",description:`<strong>eval_dataset</strong> (<code>Dataset</code>, <em>optional</em>) —
Pass a dataset if you wish to override <code>self.eval_dataset</code>. If it is an <code>datasets.Dataset</code>, columns not
accepted by the <code>model.forward()</code> method are automatically removed. It must implement the <code>__len__</code>
method.`,name:"eval_dataset"},{anchor:"transformers.Seq2SeqTrainer.evaluate.ignore_keys",description:`<strong>ignore_keys</strong> (<code>List[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.`,name:"ignore_keys"},{anchor:"transformers.Seq2SeqTrainer.evaluate.metric_key_prefix",description:`<strong>metric_key_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"eval"</code>) —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is <code>"eval"</code> (default)`,name:"metric_key_prefix"},{anchor:"transformers.Seq2SeqTrainer.evaluate.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
The maximum target length to use when predicting with the generate method.`,name:"max_length"},{anchor:"transformers.Seq2SeqTrainer.evaluate.num_beams",description:`<strong>num_beams</strong> (<code>int</code>, <em>optional</em>) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.`,name:"num_beams"}],returnDescription:`
<p>A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
dictionary also contains the epoch number which comes from the training state.</p>
`}}),yn=new $({props:{name:"predict",anchor:"transformers.Seq2SeqTrainer.predict",parameters:[{name:"test_dataset",val:": Dataset"},{name:"ignore_keys",val:": typing.Optional[typing.List[str]] = None"},{name:"metric_key_prefix",val:": str = 'test'"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"num_beams",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_seq2seq.py#L72",parametersDescription:[{anchor:"transformers.Seq2SeqTrainer.predict.test_dataset",description:`<strong>test_dataset</strong> (<code>Dataset</code>) —
Dataset to run the predictions on. If it is an <code>datasets.Dataset</code>, columns not accepted by the
<code>model.forward()</code> method are automatically removed. Has to implement the method <code>__len__</code>`,name:"test_dataset"},{anchor:"transformers.Seq2SeqTrainer.predict.ignore_keys",description:`<strong>ignore_keys</strong> (<code>List[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.`,name:"ignore_keys"},{anchor:"transformers.Seq2SeqTrainer.predict.metric_key_prefix",description:`<strong>metric_key_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"eval"</code>) —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is <code>"eval"</code> (default)`,name:"metric_key_prefix"},{anchor:"transformers.Seq2SeqTrainer.predict.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
The maximum target length to use when predicting with the generate method.`,name:"max_length"},{anchor:"transformers.Seq2SeqTrainer.predict.num_beams",description:`<strong>num_beams</strong> (<code>int</code>, <em>optional</em>) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.`,name:"num_beams"}]}}),nr=new Kb({props:{$$slots:{default:[hL]},$$scope:{ctx:Ye}}}),Tn=new se({}),$n=new $({props:{name:"class transformers.TrainingArguments",anchor:"transformers.TrainingArguments",parameters:[{name:"output_dir",val:": str"},{name:"overwrite_output_dir",val:": bool = False"},{name:"do_train",val:": bool = False"},{name:"do_eval",val:": bool = False"},{name:"do_predict",val:": bool = False"},{name:"evaluation_strategy",val:": IntervalStrategy = 'no'"},{name:"prediction_loss_only",val:": bool = False"},{name:"per_device_train_batch_size",val:": int = 8"},{name:"per_device_eval_batch_size",val:": int = 8"},{name:"per_gpu_train_batch_size",val:": typing.Optional[int] = None"},{name:"per_gpu_eval_batch_size",val:": typing.Optional[int] = None"},{name:"gradient_accumulation_steps",val:": int = 1"},{name:"eval_accumulation_steps",val:": typing.Optional[int] = None"},{name:"learning_rate",val:": float = 5e-05"},{name:"weight_decay",val:": float = 0.0"},{name:"adam_beta1",val:": float = 0.9"},{name:"adam_beta2",val:": float = 0.999"},{name:"adam_epsilon",val:": float = 1e-08"},{name:"max_grad_norm",val:": float = 1.0"},{name:"num_train_epochs",val:": float = 3.0"},{name:"max_steps",val:": int = -1"},{name:"lr_scheduler_type",val:": SchedulerType = 'linear'"},{name:"warmup_ratio",val:": float = 0.0"},{name:"warmup_steps",val:": int = 0"},{name:"log_level",val:": typing.Optional[str] = 'passive'"},{name:"log_level_replica",val:": typing.Optional[str] = 'passive'"},{name:"log_on_each_node",val:": bool = True"},{name:"logging_dir",val:": typing.Optional[str] = None"},{name:"logging_strategy",val:": IntervalStrategy = 'steps'"},{name:"logging_first_step",val:": bool = False"},{name:"logging_steps",val:": int = 500"},{name:"logging_nan_inf_filter",val:": str = True"},{name:"save_strategy",val:": IntervalStrategy = 'steps'"},{name:"save_steps",val:": int = 500"},{name:"save_total_limit",val:": typing.Optional[int] = None"},{name:"save_on_each_node",val:": bool = False"},{name:"no_cuda",val:": bool = False"},{name:"seed",val:": int = 42"},{name:"data_seed",val:": int = None"},{name:"bf16",val:": bool = False"},{name:"fp16",val:": bool = False"},{name:"fp16_opt_level",val:": str = 'O1'"},{name:"half_precision_backend",val:": str = 'auto'"},{name:"bf16_full_eval",val:": bool = False"},{name:"fp16_full_eval",val:": bool = False"},{name:"tf32",val:": bool = None"},{name:"local_rank",val:": int = -1"},{name:"xpu_backend",val:": str = None"},{name:"tpu_num_cores",val:": typing.Optional[int] = None"},{name:"tpu_metrics_debug",val:": bool = False"},{name:"debug",val:": str = ''"},{name:"dataloader_drop_last",val:": bool = False"},{name:"eval_steps",val:": int = None"},{name:"dataloader_num_workers",val:": int = 0"},{name:"past_index",val:": int = -1"},{name:"run_name",val:": typing.Optional[str] = None"},{name:"disable_tqdm",val:": typing.Optional[bool] = None"},{name:"remove_unused_columns",val:": typing.Optional[bool] = True"},{name:"label_names",val:": typing.Optional[typing.List[str]] = None"},{name:"load_best_model_at_end",val:": typing.Optional[bool] = False"},{name:"metric_for_best_model",val:": typing.Optional[str] = None"},{name:"greater_is_better",val:": typing.Optional[bool] = None"},{name:"ignore_data_skip",val:": bool = False"},{name:"sharded_ddp",val:": str = ''"},{name:"deepspeed",val:": typing.Optional[str] = None"},{name:"label_smoothing_factor",val:": float = 0.0"},{name:"optim",val:": OptimizerNames = 'adamw_hf'"},{name:"adafactor",val:": bool = False"},{name:"group_by_length",val:": bool = False"},{name:"length_column_name",val:": typing.Optional[str] = 'length'"},{name:"report_to",val:": typing.Optional[typing.List[str]] = None"},{name:"ddp_find_unused_parameters",val:": typing.Optional[bool] = None"},{name:"ddp_bucket_cap_mb",val:": typing.Optional[int] = None"},{name:"dataloader_pin_memory",val:": bool = True"},{name:"skip_memory_metrics",val:": bool = True"},{name:"use_legacy_prediction_loop",val:": bool = False"},{name:"push_to_hub",val:": bool = False"},{name:"resume_from_checkpoint",val:": typing.Optional[str] = None"},{name:"hub_model_id",val:": str = None"},{name:"hub_strategy",val:": HubStrategy = 'every_save'"},{name:"hub_token",val:": str = None"},{name:"gradient_checkpointing",val:": bool = False"},{name:"fp16_backend",val:": str = 'auto'"},{name:"push_to_hub_model_id",val:": str = None"},{name:"push_to_hub_organization",val:": str = None"},{name:"push_to_hub_token",val:": str = None"},{name:"mp_parameters",val:": str = ''"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L86",parametersDescription:[{anchor:"transformers.TrainingArguments.output_dir",description:`<strong>output_dir</strong> (<code>str</code>) —
The output directory where the model predictions and checkpoints will be written.`,name:"output_dir"},{anchor:"transformers.TrainingArguments.overwrite_output_dir",description:`<strong>overwrite_output_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If <code>True</code>, overwrite the content of the output directory. Use this to continue training if <code>output_dir</code>
points to a checkpoint directory.`,name:"overwrite_output_dir"},{anchor:"transformers.TrainingArguments.do_train",description:`<strong>do_train</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run training or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used
by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"do_train"},{anchor:"transformers.TrainingArguments.do_eval",description:`<strong>do_eval</strong> (<code>bool</code>, <em>optional</em>) —
Whether to run evaluation on the validation set or not. Will be set to <code>True</code> if <code>evaluation_strategy</code> is
different from <code>"no"</code>. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your
training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"do_eval"},{anchor:"transformers.TrainingArguments.do_predict",description:`<strong>do_predict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run predictions on the test set or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s
intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"do_predict"},{anchor:"transformers.TrainingArguments.evaluation_strategy",description:`<strong>evaluation_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"no"</code>) —
The evaluation strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No evaluation is done during training.</li>
<li><code>"steps"</code>: Evaluation is done (and logged) every <code>eval_steps</code>.</li>
<li><code>"epoch"</code>: Evaluation is done at the end of each epoch.</li>
</ul>`,name:"evaluation_strategy"},{anchor:"transformers.TrainingArguments.prediction_loss_only",description:`<strong>prediction_loss_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When performing evaluation and generating predictions, only returns the loss.`,name:"prediction_loss_only"},{anchor:"transformers.TrainingArguments.per_device_train_batch_size",description:`<strong>per_device_train_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for training.`,name:"per_device_train_batch_size"},{anchor:"transformers.TrainingArguments.per_device_eval_batch_size",description:`<strong>per_device_eval_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for evaluation.`,name:"per_device_eval_batch_size"},{anchor:"transformers.TrainingArguments.gradient_accumulation_steps",description:`<strong>gradient_accumulation_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
evaluation, save will be conducted every <code>gradient_accumulation_steps * xxx_step</code> training examples.</p>
</div>`,name:"gradient_accumulation_steps"},{anchor:"transformers.TrainingArguments.eval_accumulation_steps",description:`<strong>eval_accumulation_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
requires more memory).`,name:"eval_accumulation_steps"},{anchor:"transformers.TrainingArguments.learning_rate",description:`<strong>learning_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 5e-5) —
The initial learning rate for <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"learning_rate"},{anchor:"transformers.TrainingArguments.weight_decay",description:`<strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a>
optimizer.`,name:"weight_decay"},{anchor:"transformers.TrainingArguments.adam_beta1",description:`<strong>adam_beta1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"adam_beta1"},{anchor:"transformers.TrainingArguments.adam_beta2",description:`<strong>adam_beta2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"adam_beta2"},{anchor:"transformers.TrainingArguments.adam_epsilon",description:`<strong>adam_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-8) —
The epsilon hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"adam_epsilon"},{anchor:"transformers.TrainingArguments.max_grad_norm",description:`<strong>max_grad_norm</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Maximum gradient norm (for gradient clipping).`,name:"max_grad_norm"},{anchor:"transformers.TrainingArguments.num_train_epochs(float,",description:`<strong>num_train_epochs(<code>float</code>,</strong> <em>optional</em>, defaults to 3.0) —
Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
the last epoch before stopping training).`,name:"num_train_epochs(float,"},{anchor:"transformers.TrainingArguments.max_steps",description:`<strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
If set to a positive number, the total number of training steps to perform. Overrides <code>num_train_epochs</code>.
In case of using a finite iterable dataset the training may stop before reaching the set number of steps
when all data is exhausted`,name:"max_steps"},{anchor:"transformers.TrainingArguments.lr_scheduler_type",description:`<strong>lr_scheduler_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a>, <em>optional</em>, defaults to <code>"linear"</code>) —
The scheduler type to use. See the documentation of <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a> for all possible values.`,name:"lr_scheduler_type"},{anchor:"transformers.TrainingArguments.warmup_ratio",description:`<strong>warmup_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
Ratio of total training steps used for a linear warmup from 0 to <code>learning_rate</code>.`,name:"warmup_ratio"},{anchor:"transformers.TrainingArguments.warmup_steps",description:`<strong>warmup_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of steps used for a linear warmup from 0 to <code>learning_rate</code>. Overrides any effect of <code>warmup_ratio</code>.`,name:"warmup_steps"},{anchor:"transformers.TrainingArguments.log_level",description:`<strong>log_level</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’,
‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and lets the
application set the level.`,name:"log_level"},{anchor:"transformers.TrainingArguments.log_level_replica",description:`<strong>log_level_replica</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on replicas. Same choices as <code>log_level</code>”`,name:"log_level_replica"},{anchor:"transformers.TrainingArguments.log_on_each_node",description:`<strong>log_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
In multinode distributed training, whether to log using <code>log_level</code> once per node, or only on the main
node.`,name:"log_on_each_node"},{anchor:"transformers.TrainingArguments.logging_dir",description:`<strong>logging_dir</strong> (<code>str</code>, <em>optional</em>) —
<a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a> log directory. Will default to
*output_dir/runs/<strong>CURRENT_DATETIME_HOSTNAME*</strong>.`,name:"logging_dir"},{anchor:"transformers.TrainingArguments.logging_strategy",description:`<strong>logging_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The logging strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No logging is done during training.</li>
<li><code>"epoch"</code>: Logging is done at the end of each epoch.</li>
<li><code>"steps"</code>: Logging is done every <code>logging_steps</code>.</li>
</ul>`,name:"logging_strategy"},{anchor:"transformers.TrainingArguments.logging_first_step",description:`<strong>logging_first_step</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to log and evaluate the first <code>global_step</code> or not.`,name:"logging_first_step"},{anchor:"transformers.TrainingArguments.logging_steps",description:`<strong>logging_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of update steps between two logs if <code>logging_strategy="steps"</code>.`,name:"logging_steps"},{anchor:"transformers.TrainingArguments.logging_nan_inf_filter",description:`<strong>logging_nan_inf_filter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to filter <code>nan</code> and <code>inf</code> losses for logging. If set to <code>True</code> the loss of every step that is <code>nan</code>
or <code>inf</code> is filtered and the average loss of the current logging window is taken instead.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p><code>logging_nan_inf_filter</code> only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.</p>
</div>`,name:"logging_nan_inf_filter"},{anchor:"transformers.TrainingArguments.save_strategy",description:`<strong>save_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The checkpoint save strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No save is done during training.</li>
<li><code>"epoch"</code>: Save is done at the end of each epoch.</li>
<li><code>"steps"</code>: Save is done every <code>save_steps</code>.</li>
</ul>`,name:"save_strategy"},{anchor:"transformers.TrainingArguments.save_steps",description:`<strong>save_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of updates steps before two checkpoint saves if <code>save_strategy="steps"</code>.`,name:"save_steps"},{anchor:"transformers.TrainingArguments.save_total_limit",description:`<strong>save_total_limit</strong> (<code>int</code>, <em>optional</em>) —
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
<code>output_dir</code>.`,name:"save_total_limit"},{anchor:"transformers.TrainingArguments.save_on_each_node",description:`<strong>save_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
the main one.</p>
<p>This should not be activated when the different nodes use the same storage as the files will be saved with
the same names for each node.`,name:"save_on_each_node"},{anchor:"transformers.TrainingArguments.no_cuda",description:`<strong>no_cuda</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to not use CUDA even when it is available or not.`,name:"no_cuda"},{anchor:"transformers.TrainingArguments.seed",description:`<strong>seed</strong> (<code>int</code>, <em>optional</em>, defaults to 42) —
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
<code>model_init</code> function to instantiate the model if it has some randomly initialized parameters.`,name:"seed"},{anchor:"transformers.TrainingArguments.data_seed",description:`<strong>data_seed</strong> (<code>int</code>, <em>optional</em>) —
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as <code>seed</code>. This can be used to ensure reproducibility of data sampling, independent of the model
seed.`,name:"data_seed"},{anchor:"transformers.TrainingArguments.bf16",description:`<strong>bf16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
NVIDIA architecture. This is an experimental API and it may change.`,name:"bf16"},{anchor:"transformers.TrainingArguments.fp16",description:`<strong>fp16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.`,name:"fp16"},{anchor:"transformers.TrainingArguments.fp16_opt_level",description:`<strong>fp16_opt_level</strong> (<code>str</code>, <em>optional</em>, defaults to ‘O1’) —
For <code>fp16</code> training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on
the <a href="https://nvidia.github.io/apex/amp" rel="nofollow">Apex documentation</a>.`,name:"fp16_opt_level"},{anchor:"transformers.TrainingArguments.fp16_backend",description:`<strong>fp16_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
This argument is deprecated. Use <code>half_precision_backend</code> instead.`,name:"fp16_backend"},{anchor:"transformers.TrainingArguments.half_precision_backend",description:`<strong>half_precision_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
The backend to use for mixed precision training. Must be one of <code>"auto"</code>, <code>"amp"</code> or <code>"apex"</code>. <code>"auto"</code>
will use AMP or APEX depending on the PyTorch version detected, while the other choices will force the
requested backend.`,name:"half_precision_backend"},{anchor:"transformers.TrainingArguments.bf16_full_eval",description:`<strong>bf16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values. This is an experimental API and it may change.`,name:"bf16_full_eval"},{anchor:"transformers.TrainingArguments.fp16_full_eval",description:`<strong>fp16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values.`,name:"fp16_full_eval"},{anchor:"transformers.TrainingArguments.tf32",description:`<strong>tf32</strong> (<code>bool</code>, <em>optional</em>) —
Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API
and it may change.`,name:"tf32"},{anchor:"transformers.TrainingArguments.local_rank",description:`<strong>local_rank</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Rank of the process during distributed training.`,name:"local_rank"},{anchor:"transformers.TrainingArguments.xpu_backend",description:`<strong>xpu_backend</strong> (<code>str</code>, <em>optional</em>) —
The backend to use for xpu distributed training. Must be one of <code>"mpi"</code> or <code>"ccl"</code>.`,name:"xpu_backend"},{anchor:"transformers.TrainingArguments.tpu_num_cores",description:`<strong>tpu_num_cores</strong> (<code>int</code>, <em>optional</em>) —
When training on TPU, the number of TPU cores (automatically passed by launcher script).`,name:"tpu_num_cores"},{anchor:"transformers.TrainingArguments.dataloader_drop_last",description:`<strong>dataloader_drop_last</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.`,name:"dataloader_drop_last"},{anchor:"transformers.TrainingArguments.eval_steps",description:`<strong>eval_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of update steps between two evaluations if <code>evaluation_strategy="steps"</code>. Will default to the same
value as <code>logging_steps</code> if not set.`,name:"eval_steps"},{anchor:"transformers.TrainingArguments.dataloader_num_workers",description:`<strong>dataloader_num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
main process.`,name:"dataloader_num_workers"},{anchor:"transformers.TrainingArguments.past_index",description:`<strong>past_index</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Some models like <a href="../model_doc/transformerxl">TransformerXL</a> or <a href="../model_doc/xlnet">XLNet</a> can make use of
the past hidden states for their predictions. If this argument is set to a positive int, the <code>Trainer</code> will
use the corresponding output (usually index 2) as the past state and feed it to the model at the next
training step under the keyword argument <code>mems</code>.`,name:"past_index"},{anchor:"transformers.TrainingArguments.run_name",description:`<strong>run_name</strong> (<code>str</code>, <em>optional</em>) —
A descriptor for the run. Typically used for <a href="https://www.wandb.com/" rel="nofollow">wandb</a> and
<a href="https://www.mlflow.org/" rel="nofollow">mlflow</a> logging.`,name:"run_name"},{anchor:"transformers.TrainingArguments.disable_tqdm",description:`<strong>disable_tqdm</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not to disable the tqdm progress bars and table of metrics produced by
<code>NotebookTrainingTracker</code> in Jupyter Notebooks. Will default to <code>True</code> if the logging level is
set to warn or lower (default), <code>False</code> otherwise.`,name:"disable_tqdm"},{anchor:"transformers.TrainingArguments.remove_unused_columns",description:`<strong>remove_unused_columns</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If using <code>datasets.Dataset</code> datasets, whether or not to automatically remove the columns unused by the
model forward method.</p>
<p>(Note that this behavior is not implemented for <code>TFTrainer</code>yet.)`,name:"remove_unused_columns"},{anchor:"transformers.TrainingArguments.label_names",description:`<strong>label_names</strong> (<code>List[str]</code>, <em>optional</em>) —
The list of keys in your dictionary of inputs that correspond to the labels.</p>
<p>Will eventually default to <code>["labels"]</code> except if the model used is one of the <code>XxxForQuestionAnswering</code> in
which case it will default to <code>["start_positions", "end_positions"]</code>.`,name:"label_names"},{anchor:"transformers.TrainingArguments.load_best_model_at_end",description:`<strong>load_best_model_at_end</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to load the best model found during training at the end of training.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p>When set to <code>True</code>, the parameters <code>save_strategy</code> needs to be the same as <code>eval_strategy</code>, and in the case
it is “steps”, <code>save_steps</code> must be a round multiple of <code>eval_steps</code>.</p>
</div>`,name:"load_best_model_at_end"},{anchor:"transformers.TrainingArguments.metric_for_best_model",description:`<strong>metric_for_best_model</strong> (<code>str</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> to specify the metric to use to compare two different
models. Must be the name of a metric returned by the evaluation with or without the prefix <code>"eval_"</code>. Will
default to <code>"loss"</code> if unspecified and <code>load_best_model_at_end=True</code> (to use the evaluation loss).</p>
<p>If you set this value, <code>greater_is_better</code> will default to <code>True</code>. Don’t forget to set it to <code>False</code> if
your metric is better when lower.`,name:"metric_for_best_model"},{anchor:"transformers.TrainingArguments.greater_is_better",description:`<strong>greater_is_better</strong> (<code>bool</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> and <code>metric_for_best_model</code> to specify if better models
should have a greater metric or not. Will default to:</p>
<ul>
<li><code>True</code> if <code>metric_for_best_model</code> is set to a value that isn’t <code>"loss"</code> or <code>"eval_loss"</code>.</li>
<li><code>False</code> if <code>metric_for_best_model</code> is not set, or set to <code>"loss"</code> or <code>"eval_loss"</code>.</li>
</ul>`,name:"greater_is_better"},{anchor:"transformers.TrainingArguments.ignore_data_skip",description:`<strong>ignore_data_skip</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to <code>True</code>, the training will begin faster (as that skipping step
can take a long time) but will not yield the same results as the interrupted training would have.`,name:"ignore_data_skip"},{anchor:"transformers.TrainingArguments.sharded_ddp",description:`<strong>sharded_ddp</strong> (<code>bool</code>, <code>str</code> or list of <code>ShardedDDPOption</code> <em>optional</em>, defaults to <code>False</code>) —
Use Sharded DDP training from <a href="https://github.com/facebookresearch/fairscale" rel="nofollow">FairScale</a> (in distributed
training only). This is an experimental feature.</p>
<p>A list of options along the following:</p>
<ul>
<li><code>"simple"</code>: to use first instance of sharded DDP released by fairscale (<code>ShardedDDP</code>) similar to ZeRO-2.</li>
<li><code>"zero_dp_2"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-2 mode (with <code>reshard_after_forward=False</code>).</li>
<li><code>"zero_dp_3"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-3 mode (with <code>reshard_after_forward=True</code>).</li>
<li><code>"offload"</code>: to add ZeRO-offload (only compatible with <code>"zero_dp_2"</code> and <code>"zero_dp_3"</code>).</li>
</ul>
<p>If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty
list for <code>False</code> and <code>["simple"]</code> for <code>True</code>.`,name:"sharded_ddp"},{anchor:"transformers.TrainingArguments.deepspeed",description:`<strong>deepspeed</strong> (<code>str</code> or <code>dict</code>, <em>optional</em>) —
Use <a href="https://github.com/microsoft/deepspeed" rel="nofollow">Deepspeed</a>. This is an experimental feature and its API may
evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
<code>ds_config.json</code>) or an already loaded json file as a <code>dict</code>”`,name:"deepspeed"},{anchor:"transformers.TrainingArguments.label_smoothing_factor",description:`<strong>label_smoothing_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
labels are changed from 0s and 1s to <code>label_smoothing_factor/num_labels</code> and <code>1 - label_smoothing_factor + label_smoothing_factor/num_labels</code> respectively.`,name:"label_smoothing_factor"},{anchor:"transformers.TrainingArguments.debug",description:`<strong>debug</strong> (<code>str</code> or list of <code>DebugOption</code> <em>optional</em>, defaults to <code>""</code>) —
Enable one or more debug features. This is an experimental feature.</p>
<p>Possible options are:</p>
<ul>
<li><code>"underflow_overflow"</code>: detects overflow in model’s input/outputs and reports the last frames that led to
the event</li>
<li><code>"tpu_metrics_debug"</code>: print debug metrics on TPU</li>
</ul>
<p>The options should be separated by whitespaces.`,name:"debug"},{anchor:"transformers.TrainingArguments.optim",description:`<strong>optim</strong> (<code>str</code> or <code>training_args.OptimizerNames</code> <em>optional</em>, defaults to <code>"adamw_hf"</code>) —
The optimizer to use: adamw_hf, adamw_torch, adamw_apex_fused, or adafactor.`,name:"optim"},{anchor:"transformers.TrainingArguments.adafactor",description:`<strong>adafactor</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
This argument is deprecated. Use <code>--optim adafactor</code> instead.`,name:"adafactor"},{anchor:"transformers.TrainingArguments.group_by_length",description:`<strong>group_by_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to group together samples of roughly the same length in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.`,name:"group_by_length"},{anchor:"transformers.TrainingArguments.length_column_name",description:`<strong>length_column_name</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"length"</code>) —
Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
than computing them on train startup. Ignored unless <code>group_by_length</code> is <code>True</code> and the dataset is an
instance of <code>Dataset</code>.`,name:"length_column_name"},{anchor:"transformers.TrainingArguments.report_to",description:`<strong>report_to</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>, defaults to <code>"all"</code>) —
The list of integrations to report the results and logs to. Supported platforms are <code>"azure_ml"</code>,
<code>"comet_ml"</code>, <code>"mlflow"</code>, <code>"tensorboard"</code> and <code>"wandb"</code>. Use <code>"all"</code> to report to all integrations
installed, <code>"none"</code> for no integrations.`,name:"report_to"},{anchor:"transformers.TrainingArguments.ddp_find_unused_parameters",description:`<strong>ddp_find_unused_parameters</strong> (<code>bool</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>find_unused_parameters</code> passed to
<code>DistributedDataParallel</code>. Will default to <code>False</code> if gradient checkpointing is used, <code>True</code> otherwise.`,name:"ddp_find_unused_parameters"},{anchor:"transformers.TrainingArguments.ddp_bucket_cap_mb",description:`<strong>ddp_bucket_cap_mb</strong> (<code>int</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>bucket_cap_mb</code> passed to <code>DistributedDataParallel</code>.`,name:"ddp_bucket_cap_mb"},{anchor:"transformers.TrainingArguments.dataloader_pin_memory",description:`<strong>dataloader_pin_memory</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether you want to pin memory in data loaders or not. Will default to <code>True</code>.`,name:"dataloader_pin_memory"},{anchor:"transformers.TrainingArguments.skip_memory_metrics",description:`<strong>skip_memory_metrics</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
down the training and evaluation speed.`,name:"skip_memory_metrics"},{anchor:"transformers.TrainingArguments.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push the model to the Hub every time the model is saved. If this is activated,
<code>output_dir</code> will begin a git directory synced with the the repo (determined by <code>hub_model_id</code>) and the
content will be pushed each time a save is triggered (depending on your <code>save_strategy</code>). Calling
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> will also trigger a push.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>If <code>output_dir</code> exists, it needs to be a local clone of the repository to which the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will be
pushed.</p>
</div>`,name:"push_to_hub"},{anchor:"transformers.TrainingArguments.resume_from_checkpoint",description:`<strong>resume_from_checkpoint</strong> (<code>str</code>, <em>optional</em>) —
The path to a folder with a valid checkpoint for your model. This argument is not directly used by
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"resume_from_checkpoint"},{anchor:"transformers.TrainingArguments.hub_model_id",description:`<strong>hub_model_id</strong> (<code>str</code>, <em>optional</em>) —
The name of the repository to keep in sync with the local <em>output_dir</em>. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance <code>"user_name/model"</code>, which allows you to push to an organization you are a member of with
<code>"organization_name/model"</code>. Will default to <code>user_name/output_dir_name</code> with <em>output_dir_name</em> being the
name of <code>output_dir</code>.</p>
<p>Will default to to the name of <code>output_dir</code>.`,name:"hub_model_id"},{anchor:"transformers.TrainingArguments.hub_strategy",description:`<strong>hub_strategy</strong> (<code>str</code> or <code>HubStrategy</code> <em>optional</em>, defaults to <code>"every_save"</code>) —
Defines the scope of what is pushed to the Hub and when. Possible values are:</p>
<ul>
<li><code>"end"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and a
draft of a model card when the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> method is called.</li>
<li><code>"every_save"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.</li>
<li><code>"checkpoint"</code>: like <code>"every_save"</code> but the latest checkpoint is also pushed in a subfolder named
last-checkpoint, allowing you to resume training easily with
<code>trainer.train(resume_from_checkpoint="last-checkpoint")</code>.</li>
<li><code>"all_checkpoints"</code>: like <code>"checkpoint"</code> but all checkpoints are pushed like they appear in the output
folder (so you will get one checkpoint folder per folder in your final repository)</li>
</ul>`,name:"hub_strategy"},{anchor:"transformers.TrainingArguments.hub_token",description:`<strong>hub_token</strong> (<code>str</code>, <em>optional</em>) —
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
<code>huggingface-cli login</code>.`,name:"hub_token"},{anchor:"transformers.TrainingArguments.gradient_checkpointing",description:`<strong>gradient_checkpointing</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If True, use gradient checkpointing to save memory at the expense of slower backward pass.`,name:"gradient_checkpointing"}]}}),An=new $({props:{name:"get_process_log_level",anchor:"transformers.TrainingArguments.get_process_log_level",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1199"}}),qn=new $({props:{name:"get_warmup_steps",anchor:"transformers.TrainingArguments.get_warmup_steps",parameters:[{name:"num_training_steps",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1285"}}),Pn=new $({props:{name:"main_process_first",anchor:"transformers.TrainingArguments.main_process_first",parameters:[{name:"local",val:" = True"},{name:"desc",val:" = 'work'"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1230",parametersDescription:[{anchor:"transformers.TrainingArguments.main_process_first.local",description:`<strong>local</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
if <code>True</code> first means process of rank 0 of each node if <code>False</code> first means process of rank 0 of node
rank 0 In multi-node environment with a shared filesystem you most likely will want to use
<code>local=False</code> so that only the main process of the first node will do the processing. If however, the
filesystem is not shared, then the main process of each node will need to do the processing, which is
the default behavior.`,name:"local"},{anchor:"transformers.TrainingArguments.main_process_first.desc",description:`<strong>desc</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"work"</code>) —
a work description to be used in debug logs`,name:"desc"}]}}),Sn=new $({props:{name:"to_dict",anchor:"transformers.TrainingArguments.to_dict",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1294"}}),Cn=new $({props:{name:"to_json_string",anchor:"transformers.TrainingArguments.to_json_string",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1309"}}),In=new $({props:{name:"to_sanitized_dict",anchor:"transformers.TrainingArguments.to_sanitized_dict",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1315"}}),Un=new se({}),Nn=new $({props:{name:"class transformers.Seq2SeqTrainingArguments",anchor:"transformers.Seq2SeqTrainingArguments",parameters:[{name:"output_dir",val:": str"},{name:"overwrite_output_dir",val:": bool = False"},{name:"do_train",val:": bool = False"},{name:"do_eval",val:": bool = False"},{name:"do_predict",val:": bool = False"},{name:"evaluation_strategy",val:": IntervalStrategy = 'no'"},{name:"prediction_loss_only",val:": bool = False"},{name:"per_device_train_batch_size",val:": int = 8"},{name:"per_device_eval_batch_size",val:": int = 8"},{name:"per_gpu_train_batch_size",val:": typing.Optional[int] = None"},{name:"per_gpu_eval_batch_size",val:": typing.Optional[int] = None"},{name:"gradient_accumulation_steps",val:": int = 1"},{name:"eval_accumulation_steps",val:": typing.Optional[int] = None"},{name:"learning_rate",val:": float = 5e-05"},{name:"weight_decay",val:": float = 0.0"},{name:"adam_beta1",val:": float = 0.9"},{name:"adam_beta2",val:": float = 0.999"},{name:"adam_epsilon",val:": float = 1e-08"},{name:"max_grad_norm",val:": float = 1.0"},{name:"num_train_epochs",val:": float = 3.0"},{name:"max_steps",val:": int = -1"},{name:"lr_scheduler_type",val:": SchedulerType = 'linear'"},{name:"warmup_ratio",val:": float = 0.0"},{name:"warmup_steps",val:": int = 0"},{name:"log_level",val:": typing.Optional[str] = 'passive'"},{name:"log_level_replica",val:": typing.Optional[str] = 'passive'"},{name:"log_on_each_node",val:": bool = True"},{name:"logging_dir",val:": typing.Optional[str] = None"},{name:"logging_strategy",val:": IntervalStrategy = 'steps'"},{name:"logging_first_step",val:": bool = False"},{name:"logging_steps",val:": int = 500"},{name:"logging_nan_inf_filter",val:": str = True"},{name:"save_strategy",val:": IntervalStrategy = 'steps'"},{name:"save_steps",val:": int = 500"},{name:"save_total_limit",val:": typing.Optional[int] = None"},{name:"save_on_each_node",val:": bool = False"},{name:"no_cuda",val:": bool = False"},{name:"seed",val:": int = 42"},{name:"data_seed",val:": int = None"},{name:"bf16",val:": bool = False"},{name:"fp16",val:": bool = False"},{name:"fp16_opt_level",val:": str = 'O1'"},{name:"half_precision_backend",val:": str = 'auto'"},{name:"bf16_full_eval",val:": bool = False"},{name:"fp16_full_eval",val:": bool = False"},{name:"tf32",val:": bool = None"},{name:"local_rank",val:": int = -1"},{name:"xpu_backend",val:": str = None"},{name:"tpu_num_cores",val:": typing.Optional[int] = None"},{name:"tpu_metrics_debug",val:": bool = False"},{name:"debug",val:": str = ''"},{name:"dataloader_drop_last",val:": bool = False"},{name:"eval_steps",val:": int = None"},{name:"dataloader_num_workers",val:": int = 0"},{name:"past_index",val:": int = -1"},{name:"run_name",val:": typing.Optional[str] = None"},{name:"disable_tqdm",val:": typing.Optional[bool] = None"},{name:"remove_unused_columns",val:": typing.Optional[bool] = True"},{name:"label_names",val:": typing.Optional[typing.List[str]] = None"},{name:"load_best_model_at_end",val:": typing.Optional[bool] = False"},{name:"metric_for_best_model",val:": typing.Optional[str] = None"},{name:"greater_is_better",val:": typing.Optional[bool] = None"},{name:"ignore_data_skip",val:": bool = False"},{name:"sharded_ddp",val:": str = ''"},{name:"deepspeed",val:": typing.Optional[str] = None"},{name:"label_smoothing_factor",val:": float = 0.0"},{name:"optim",val:": OptimizerNames = 'adamw_hf'"},{name:"adafactor",val:": bool = False"},{name:"group_by_length",val:": bool = False"},{name:"length_column_name",val:": typing.Optional[str] = 'length'"},{name:"report_to",val:": typing.Optional[typing.List[str]] = None"},{name:"ddp_find_unused_parameters",val:": typing.Optional[bool] = None"},{name:"ddp_bucket_cap_mb",val:": typing.Optional[int] = None"},{name:"dataloader_pin_memory",val:": bool = True"},{name:"skip_memory_metrics",val:": bool = True"},{name:"use_legacy_prediction_loop",val:": bool = False"},{name:"push_to_hub",val:": bool = False"},{name:"resume_from_checkpoint",val:": typing.Optional[str] = None"},{name:"hub_model_id",val:": str = None"},{name:"hub_strategy",val:": HubStrategy = 'every_save'"},{name:"hub_token",val:": str = None"},{name:"gradient_checkpointing",val:": bool = False"},{name:"fp16_backend",val:": str = 'auto'"},{name:"push_to_hub_model_id",val:": str = None"},{name:"push_to_hub_organization",val:": str = None"},{name:"push_to_hub_token",val:": str = None"},{name:"mp_parameters",val:": str = ''"},{name:"sortish_sampler",val:": bool = False"},{name:"predict_with_generate",val:": bool = False"},{name:"generation_max_length",val:": typing.Optional[int] = None"},{name:"generation_num_beams",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args_seq2seq.py#L28",parametersDescription:[{anchor:"transformers.Seq2SeqTrainingArguments.output_dir",description:`<strong>output_dir</strong> (<code>str</code>) —
The output directory where the model predictions and checkpoints will be written.`,name:"output_dir"},{anchor:"transformers.Seq2SeqTrainingArguments.overwrite_output_dir",description:`<strong>overwrite_output_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If <code>True</code>, overwrite the content of the output directory. Use this to continue training if <code>output_dir</code>
points to a checkpoint directory.`,name:"overwrite_output_dir"},{anchor:"transformers.Seq2SeqTrainingArguments.do_train",description:`<strong>do_train</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run training or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used
by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"do_train"},{anchor:"transformers.Seq2SeqTrainingArguments.do_eval",description:`<strong>do_eval</strong> (<code>bool</code>, <em>optional</em>) —
Whether to run evaluation on the validation set or not. Will be set to <code>True</code> if <code>evaluation_strategy</code> is
different from <code>"no"</code>. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your
training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"do_eval"},{anchor:"transformers.Seq2SeqTrainingArguments.do_predict",description:`<strong>do_predict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run predictions on the test set or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s
intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"do_predict"},{anchor:"transformers.Seq2SeqTrainingArguments.evaluation_strategy",description:`<strong>evaluation_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"no"</code>) —
The evaluation strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No evaluation is done during training.</li>
<li><code>"steps"</code>: Evaluation is done (and logged) every <code>eval_steps</code>.</li>
<li><code>"epoch"</code>: Evaluation is done at the end of each epoch.</li>
</ul>`,name:"evaluation_strategy"},{anchor:"transformers.Seq2SeqTrainingArguments.prediction_loss_only",description:`<strong>prediction_loss_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When performing evaluation and generating predictions, only returns the loss.`,name:"prediction_loss_only"},{anchor:"transformers.Seq2SeqTrainingArguments.per_device_train_batch_size",description:`<strong>per_device_train_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for training.`,name:"per_device_train_batch_size"},{anchor:"transformers.Seq2SeqTrainingArguments.per_device_eval_batch_size",description:`<strong>per_device_eval_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for evaluation.`,name:"per_device_eval_batch_size"},{anchor:"transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps",description:`<strong>gradient_accumulation_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
evaluation, save will be conducted every <code>gradient_accumulation_steps * xxx_step</code> training examples.</p>
</div>`,name:"gradient_accumulation_steps"},{anchor:"transformers.Seq2SeqTrainingArguments.eval_accumulation_steps",description:`<strong>eval_accumulation_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
requires more memory).`,name:"eval_accumulation_steps"},{anchor:"transformers.Seq2SeqTrainingArguments.learning_rate",description:`<strong>learning_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 5e-5) —
The initial learning rate for <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"learning_rate"},{anchor:"transformers.Seq2SeqTrainingArguments.weight_decay",description:`<strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a>
optimizer.`,name:"weight_decay"},{anchor:"transformers.Seq2SeqTrainingArguments.adam_beta1",description:`<strong>adam_beta1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"adam_beta1"},{anchor:"transformers.Seq2SeqTrainingArguments.adam_beta2",description:`<strong>adam_beta2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"adam_beta2"},{anchor:"transformers.Seq2SeqTrainingArguments.adam_epsilon",description:`<strong>adam_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-8) —
The epsilon hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.`,name:"adam_epsilon"},{anchor:"transformers.Seq2SeqTrainingArguments.max_grad_norm",description:`<strong>max_grad_norm</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Maximum gradient norm (for gradient clipping).`,name:"max_grad_norm"},{anchor:"transformers.Seq2SeqTrainingArguments.num_train_epochs(float,",description:`<strong>num_train_epochs(<code>float</code>,</strong> <em>optional</em>, defaults to 3.0) —
Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
the last epoch before stopping training).`,name:"num_train_epochs(float,"},{anchor:"transformers.Seq2SeqTrainingArguments.max_steps",description:`<strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
If set to a positive number, the total number of training steps to perform. Overrides <code>num_train_epochs</code>.
In case of using a finite iterable dataset the training may stop before reaching the set number of steps
when all data is exhausted`,name:"max_steps"},{anchor:"transformers.Seq2SeqTrainingArguments.lr_scheduler_type",description:`<strong>lr_scheduler_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a>, <em>optional</em>, defaults to <code>"linear"</code>) —
The scheduler type to use. See the documentation of <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a> for all possible values.`,name:"lr_scheduler_type"},{anchor:"transformers.Seq2SeqTrainingArguments.warmup_ratio",description:`<strong>warmup_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
Ratio of total training steps used for a linear warmup from 0 to <code>learning_rate</code>.`,name:"warmup_ratio"},{anchor:"transformers.Seq2SeqTrainingArguments.warmup_steps",description:`<strong>warmup_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of steps used for a linear warmup from 0 to <code>learning_rate</code>. Overrides any effect of <code>warmup_ratio</code>.`,name:"warmup_steps"},{anchor:"transformers.Seq2SeqTrainingArguments.log_level",description:`<strong>log_level</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’,
‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and lets the
application set the level.`,name:"log_level"},{anchor:"transformers.Seq2SeqTrainingArguments.log_level_replica",description:`<strong>log_level_replica</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on replicas. Same choices as <code>log_level</code>”`,name:"log_level_replica"},{anchor:"transformers.Seq2SeqTrainingArguments.log_on_each_node",description:`<strong>log_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
In multinode distributed training, whether to log using <code>log_level</code> once per node, or only on the main
node.`,name:"log_on_each_node"},{anchor:"transformers.Seq2SeqTrainingArguments.logging_dir",description:`<strong>logging_dir</strong> (<code>str</code>, <em>optional</em>) —
<a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a> log directory. Will default to
*output_dir/runs/<strong>CURRENT_DATETIME_HOSTNAME*</strong>.`,name:"logging_dir"},{anchor:"transformers.Seq2SeqTrainingArguments.logging_strategy",description:`<strong>logging_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The logging strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No logging is done during training.</li>
<li><code>"epoch"</code>: Logging is done at the end of each epoch.</li>
<li><code>"steps"</code>: Logging is done every <code>logging_steps</code>.</li>
</ul>`,name:"logging_strategy"},{anchor:"transformers.Seq2SeqTrainingArguments.logging_first_step",description:`<strong>logging_first_step</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to log and evaluate the first <code>global_step</code> or not.`,name:"logging_first_step"},{anchor:"transformers.Seq2SeqTrainingArguments.logging_steps",description:`<strong>logging_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of update steps between two logs if <code>logging_strategy="steps"</code>.`,name:"logging_steps"},{anchor:"transformers.Seq2SeqTrainingArguments.logging_nan_inf_filter",description:`<strong>logging_nan_inf_filter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to filter <code>nan</code> and <code>inf</code> losses for logging. If set to <code>True</code> the loss of every step that is <code>nan</code>
or <code>inf</code> is filtered and the average loss of the current logging window is taken instead.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p><code>logging_nan_inf_filter</code> only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.</p>
</div>`,name:"logging_nan_inf_filter"},{anchor:"transformers.Seq2SeqTrainingArguments.save_strategy",description:`<strong>save_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The checkpoint save strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No save is done during training.</li>
<li><code>"epoch"</code>: Save is done at the end of each epoch.</li>
<li><code>"steps"</code>: Save is done every <code>save_steps</code>.</li>
</ul>`,name:"save_strategy"},{anchor:"transformers.Seq2SeqTrainingArguments.save_steps",description:`<strong>save_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of updates steps before two checkpoint saves if <code>save_strategy="steps"</code>.`,name:"save_steps"},{anchor:"transformers.Seq2SeqTrainingArguments.save_total_limit",description:`<strong>save_total_limit</strong> (<code>int</code>, <em>optional</em>) —
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
<code>output_dir</code>.`,name:"save_total_limit"},{anchor:"transformers.Seq2SeqTrainingArguments.save_on_each_node",description:`<strong>save_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
the main one.</p>
<p>This should not be activated when the different nodes use the same storage as the files will be saved with
the same names for each node.`,name:"save_on_each_node"},{anchor:"transformers.Seq2SeqTrainingArguments.no_cuda",description:`<strong>no_cuda</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to not use CUDA even when it is available or not.`,name:"no_cuda"},{anchor:"transformers.Seq2SeqTrainingArguments.seed",description:`<strong>seed</strong> (<code>int</code>, <em>optional</em>, defaults to 42) —
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
<code>model_init</code> function to instantiate the model if it has some randomly initialized parameters.`,name:"seed"},{anchor:"transformers.Seq2SeqTrainingArguments.data_seed",description:`<strong>data_seed</strong> (<code>int</code>, <em>optional</em>) —
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as <code>seed</code>. This can be used to ensure reproducibility of data sampling, independent of the model
seed.`,name:"data_seed"},{anchor:"transformers.Seq2SeqTrainingArguments.bf16",description:`<strong>bf16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
NVIDIA architecture. This is an experimental API and it may change.`,name:"bf16"},{anchor:"transformers.Seq2SeqTrainingArguments.fp16",description:`<strong>fp16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.`,name:"fp16"},{anchor:"transformers.Seq2SeqTrainingArguments.fp16_opt_level",description:`<strong>fp16_opt_level</strong> (<code>str</code>, <em>optional</em>, defaults to ‘O1’) —
For <code>fp16</code> training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on
the <a href="https://nvidia.github.io/apex/amp" rel="nofollow">Apex documentation</a>.`,name:"fp16_opt_level"},{anchor:"transformers.Seq2SeqTrainingArguments.fp16_backend",description:`<strong>fp16_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
This argument is deprecated. Use <code>half_precision_backend</code> instead.`,name:"fp16_backend"},{anchor:"transformers.Seq2SeqTrainingArguments.half_precision_backend",description:`<strong>half_precision_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
The backend to use for mixed precision training. Must be one of <code>"auto"</code>, <code>"amp"</code> or <code>"apex"</code>. <code>"auto"</code>
will use AMP or APEX depending on the PyTorch version detected, while the other choices will force the
requested backend.`,name:"half_precision_backend"},{anchor:"transformers.Seq2SeqTrainingArguments.bf16_full_eval",description:`<strong>bf16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values. This is an experimental API and it may change.`,name:"bf16_full_eval"},{anchor:"transformers.Seq2SeqTrainingArguments.fp16_full_eval",description:`<strong>fp16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values.`,name:"fp16_full_eval"},{anchor:"transformers.Seq2SeqTrainingArguments.tf32",description:`<strong>tf32</strong> (<code>bool</code>, <em>optional</em>) —
Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API
and it may change.`,name:"tf32"},{anchor:"transformers.Seq2SeqTrainingArguments.local_rank",description:`<strong>local_rank</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Rank of the process during distributed training.`,name:"local_rank"},{anchor:"transformers.Seq2SeqTrainingArguments.xpu_backend",description:`<strong>xpu_backend</strong> (<code>str</code>, <em>optional</em>) —
The backend to use for xpu distributed training. Must be one of <code>"mpi"</code> or <code>"ccl"</code>.`,name:"xpu_backend"},{anchor:"transformers.Seq2SeqTrainingArguments.tpu_num_cores",description:`<strong>tpu_num_cores</strong> (<code>int</code>, <em>optional</em>) —
When training on TPU, the number of TPU cores (automatically passed by launcher script).`,name:"tpu_num_cores"},{anchor:"transformers.Seq2SeqTrainingArguments.dataloader_drop_last",description:`<strong>dataloader_drop_last</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.`,name:"dataloader_drop_last"},{anchor:"transformers.Seq2SeqTrainingArguments.eval_steps",description:`<strong>eval_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of update steps between two evaluations if <code>evaluation_strategy="steps"</code>. Will default to the same
value as <code>logging_steps</code> if not set.`,name:"eval_steps"},{anchor:"transformers.Seq2SeqTrainingArguments.dataloader_num_workers",description:`<strong>dataloader_num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
main process.`,name:"dataloader_num_workers"},{anchor:"transformers.Seq2SeqTrainingArguments.past_index",description:`<strong>past_index</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Some models like <a href="../model_doc/transformerxl">TransformerXL</a> or <a href="../model_doc/xlnet">XLNet</a> can make use of
the past hidden states for their predictions. If this argument is set to a positive int, the <code>Trainer</code> will
use the corresponding output (usually index 2) as the past state and feed it to the model at the next
training step under the keyword argument <code>mems</code>.`,name:"past_index"},{anchor:"transformers.Seq2SeqTrainingArguments.run_name",description:`<strong>run_name</strong> (<code>str</code>, <em>optional</em>) —
A descriptor for the run. Typically used for <a href="https://www.wandb.com/" rel="nofollow">wandb</a> and
<a href="https://www.mlflow.org/" rel="nofollow">mlflow</a> logging.`,name:"run_name"},{anchor:"transformers.Seq2SeqTrainingArguments.disable_tqdm",description:`<strong>disable_tqdm</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not to disable the tqdm progress bars and table of metrics produced by
<code>NotebookTrainingTracker</code> in Jupyter Notebooks. Will default to <code>True</code> if the logging level is
set to warn or lower (default), <code>False</code> otherwise.`,name:"disable_tqdm"},{anchor:"transformers.Seq2SeqTrainingArguments.remove_unused_columns",description:`<strong>remove_unused_columns</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If using <code>datasets.Dataset</code> datasets, whether or not to automatically remove the columns unused by the
model forward method.</p>
<p>(Note that this behavior is not implemented for <code>TFTrainer</code>yet.)`,name:"remove_unused_columns"},{anchor:"transformers.Seq2SeqTrainingArguments.label_names",description:`<strong>label_names</strong> (<code>List[str]</code>, <em>optional</em>) —
The list of keys in your dictionary of inputs that correspond to the labels.</p>
<p>Will eventually default to <code>["labels"]</code> except if the model used is one of the <code>XxxForQuestionAnswering</code> in
which case it will default to <code>["start_positions", "end_positions"]</code>.`,name:"label_names"},{anchor:"transformers.Seq2SeqTrainingArguments.load_best_model_at_end",description:`<strong>load_best_model_at_end</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to load the best model found during training at the end of training.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p>When set to <code>True</code>, the parameters <code>save_strategy</code> needs to be the same as <code>eval_strategy</code>, and in the case
it is “steps”, <code>save_steps</code> must be a round multiple of <code>eval_steps</code>.</p>
</div>`,name:"load_best_model_at_end"},{anchor:"transformers.Seq2SeqTrainingArguments.metric_for_best_model",description:`<strong>metric_for_best_model</strong> (<code>str</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> to specify the metric to use to compare two different
models. Must be the name of a metric returned by the evaluation with or without the prefix <code>"eval_"</code>. Will
default to <code>"loss"</code> if unspecified and <code>load_best_model_at_end=True</code> (to use the evaluation loss).</p>
<p>If you set this value, <code>greater_is_better</code> will default to <code>True</code>. Don’t forget to set it to <code>False</code> if
your metric is better when lower.`,name:"metric_for_best_model"},{anchor:"transformers.Seq2SeqTrainingArguments.greater_is_better",description:`<strong>greater_is_better</strong> (<code>bool</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> and <code>metric_for_best_model</code> to specify if better models
should have a greater metric or not. Will default to:</p>
<ul>
<li><code>True</code> if <code>metric_for_best_model</code> is set to a value that isn’t <code>"loss"</code> or <code>"eval_loss"</code>.</li>
<li><code>False</code> if <code>metric_for_best_model</code> is not set, or set to <code>"loss"</code> or <code>"eval_loss"</code>.</li>
</ul>`,name:"greater_is_better"},{anchor:"transformers.Seq2SeqTrainingArguments.ignore_data_skip",description:`<strong>ignore_data_skip</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to <code>True</code>, the training will begin faster (as that skipping step
can take a long time) but will not yield the same results as the interrupted training would have.`,name:"ignore_data_skip"},{anchor:"transformers.Seq2SeqTrainingArguments.sharded_ddp",description:`<strong>sharded_ddp</strong> (<code>bool</code>, <code>str</code> or list of <code>ShardedDDPOption</code> <em>optional</em>, defaults to <code>False</code>) —
Use Sharded DDP training from <a href="https://github.com/facebookresearch/fairscale" rel="nofollow">FairScale</a> (in distributed
training only). This is an experimental feature.</p>
<p>A list of options along the following:</p>
<ul>
<li><code>"simple"</code>: to use first instance of sharded DDP released by fairscale (<code>ShardedDDP</code>) similar to ZeRO-2.</li>
<li><code>"zero_dp_2"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-2 mode (with <code>reshard_after_forward=False</code>).</li>
<li><code>"zero_dp_3"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-3 mode (with <code>reshard_after_forward=True</code>).</li>
<li><code>"offload"</code>: to add ZeRO-offload (only compatible with <code>"zero_dp_2"</code> and <code>"zero_dp_3"</code>).</li>
</ul>
<p>If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty
list for <code>False</code> and <code>["simple"]</code> for <code>True</code>.`,name:"sharded_ddp"},{anchor:"transformers.Seq2SeqTrainingArguments.deepspeed",description:`<strong>deepspeed</strong> (<code>str</code> or <code>dict</code>, <em>optional</em>) —
Use <a href="https://github.com/microsoft/deepspeed" rel="nofollow">Deepspeed</a>. This is an experimental feature and its API may
evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
<code>ds_config.json</code>) or an already loaded json file as a <code>dict</code>”`,name:"deepspeed"},{anchor:"transformers.Seq2SeqTrainingArguments.label_smoothing_factor",description:`<strong>label_smoothing_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
labels are changed from 0s and 1s to <code>label_smoothing_factor/num_labels</code> and <code>1 - label_smoothing_factor + label_smoothing_factor/num_labels</code> respectively.`,name:"label_smoothing_factor"},{anchor:"transformers.Seq2SeqTrainingArguments.debug",description:`<strong>debug</strong> (<code>str</code> or list of <code>DebugOption</code> <em>optional</em>, defaults to <code>""</code>) —
Enable one or more debug features. This is an experimental feature.</p>
<p>Possible options are:</p>
<ul>
<li><code>"underflow_overflow"</code>: detects overflow in model’s input/outputs and reports the last frames that led to
the event</li>
<li><code>"tpu_metrics_debug"</code>: print debug metrics on TPU</li>
</ul>
<p>The options should be separated by whitespaces.`,name:"debug"},{anchor:"transformers.Seq2SeqTrainingArguments.optim",description:`<strong>optim</strong> (<code>str</code> or <code>training_args.OptimizerNames</code> <em>optional</em>, defaults to <code>"adamw_hf"</code>) —
The optimizer to use: adamw_hf, adamw_torch, adamw_apex_fused, or adafactor.`,name:"optim"},{anchor:"transformers.Seq2SeqTrainingArguments.adafactor",description:`<strong>adafactor</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
This argument is deprecated. Use <code>--optim adafactor</code> instead.`,name:"adafactor"},{anchor:"transformers.Seq2SeqTrainingArguments.group_by_length",description:`<strong>group_by_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to group together samples of roughly the same length in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.`,name:"group_by_length"},{anchor:"transformers.Seq2SeqTrainingArguments.length_column_name",description:`<strong>length_column_name</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"length"</code>) —
Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
than computing them on train startup. Ignored unless <code>group_by_length</code> is <code>True</code> and the dataset is an
instance of <code>Dataset</code>.`,name:"length_column_name"},{anchor:"transformers.Seq2SeqTrainingArguments.report_to",description:`<strong>report_to</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>, defaults to <code>"all"</code>) —
The list of integrations to report the results and logs to. Supported platforms are <code>"azure_ml"</code>,
<code>"comet_ml"</code>, <code>"mlflow"</code>, <code>"tensorboard"</code> and <code>"wandb"</code>. Use <code>"all"</code> to report to all integrations
installed, <code>"none"</code> for no integrations.`,name:"report_to"},{anchor:"transformers.Seq2SeqTrainingArguments.ddp_find_unused_parameters",description:`<strong>ddp_find_unused_parameters</strong> (<code>bool</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>find_unused_parameters</code> passed to
<code>DistributedDataParallel</code>. Will default to <code>False</code> if gradient checkpointing is used, <code>True</code> otherwise.`,name:"ddp_find_unused_parameters"},{anchor:"transformers.Seq2SeqTrainingArguments.ddp_bucket_cap_mb",description:`<strong>ddp_bucket_cap_mb</strong> (<code>int</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>bucket_cap_mb</code> passed to <code>DistributedDataParallel</code>.`,name:"ddp_bucket_cap_mb"},{anchor:"transformers.Seq2SeqTrainingArguments.dataloader_pin_memory",description:`<strong>dataloader_pin_memory</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether you want to pin memory in data loaders or not. Will default to <code>True</code>.`,name:"dataloader_pin_memory"},{anchor:"transformers.Seq2SeqTrainingArguments.skip_memory_metrics",description:`<strong>skip_memory_metrics</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
down the training and evaluation speed.`,name:"skip_memory_metrics"},{anchor:"transformers.Seq2SeqTrainingArguments.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push the model to the Hub every time the model is saved. If this is activated,
<code>output_dir</code> will begin a git directory synced with the the repo (determined by <code>hub_model_id</code>) and the
content will be pushed each time a save is triggered (depending on your <code>save_strategy</code>). Calling
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> will also trigger a push.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>If <code>output_dir</code> exists, it needs to be a local clone of the repository to which the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will be
pushed.</p>
</div>`,name:"push_to_hub"},{anchor:"transformers.Seq2SeqTrainingArguments.resume_from_checkpoint",description:`<strong>resume_from_checkpoint</strong> (<code>str</code>, <em>optional</em>) —
The path to a folder with a valid checkpoint for your model. This argument is not directly used by
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.`,name:"resume_from_checkpoint"},{anchor:"transformers.Seq2SeqTrainingArguments.hub_model_id",description:`<strong>hub_model_id</strong> (<code>str</code>, <em>optional</em>) —
The name of the repository to keep in sync with the local <em>output_dir</em>. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance <code>"user_name/model"</code>, which allows you to push to an organization you are a member of with
<code>"organization_name/model"</code>. Will default to <code>user_name/output_dir_name</code> with <em>output_dir_name</em> being the
name of <code>output_dir</code>.</p>
<p>Will default to to the name of <code>output_dir</code>.`,name:"hub_model_id"},{anchor:"transformers.Seq2SeqTrainingArguments.hub_strategy",description:`<strong>hub_strategy</strong> (<code>str</code> or <code>HubStrategy</code> <em>optional</em>, defaults to <code>"every_save"</code>) —
Defines the scope of what is pushed to the Hub and when. Possible values are:</p>
<ul>
<li><code>"end"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and a
draft of a model card when the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> method is called.</li>
<li><code>"every_save"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.</li>
<li><code>"checkpoint"</code>: like <code>"every_save"</code> but the latest checkpoint is also pushed in a subfolder named
last-checkpoint, allowing you to resume training easily with
<code>trainer.train(resume_from_checkpoint="last-checkpoint")</code>.</li>
<li><code>"all_checkpoints"</code>: like <code>"checkpoint"</code> but all checkpoints are pushed like they appear in the output
folder (so you will get one checkpoint folder per folder in your final repository)</li>
</ul>`,name:"hub_strategy"},{anchor:"transformers.Seq2SeqTrainingArguments.hub_token",description:`<strong>hub_token</strong> (<code>str</code>, <em>optional</em>) —
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
<code>huggingface-cli login</code>.`,name:"hub_token"},{anchor:"transformers.Seq2SeqTrainingArguments.gradient_checkpointing",description:`<strong>gradient_checkpointing</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If True, use gradient checkpointing to save memory at the expense of slower backward pass.`,name:"gradient_checkpointing"}]}}),Fn=new se({}),Rn=new se({}),Wn=new P({props:{code:`[...]
logger = logging.getLogger(__name__)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
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# set the main code and the modules it uses to the same log-level according to the node
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
trainer = Trainer(...)`,highlighted:`[...]
logger = logging.getLogger(__name__)
<span class="hljs-comment"># Setup logging</span>
logging.basicConfig(
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echo $LD_LIBRARY_PATH`,highlighted:`<span class="hljs-built_in">echo</span> <span class="hljs-variable">$PATH</span>
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<span class="hljs-built_in">rm</span> -r dist build
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separately.`),uy=l(),ii=n("li"),mc=n("strong"),fy=r("create_optimizer"),gy=r(" \u2014 Sets up the optimizer if it wasn\u2019t passed at init."),_y=l(),li=n("li"),hc=n("strong"),vy=r("create_scheduler"),by=r(" \u2014 Sets up the learning rate scheduler if it wasn\u2019t passed at init."),yy=l(),di=n("li"),uc=n("strong"),wy=r("compute_loss"),Ey=r(" - Computes the loss on a batch of training inputs."),Ty=l(),ci=n("li"),fc=n("strong"),$y=r("training_step"),ky=r(" \u2014 Performs a training step."),xy=l(),pi=n("li"),gc=n("strong"),Ay=r("prediction_step"),Dy=r(" \u2014 Performs an evaluation/test step."),qy=l(),mi=n("li"),_c=n("strong"),Py=r("evaluate"),Sy=r(" \u2014 Runs an evaluation loop and returns metrics."),Oy=l(),hi=n("li"),vc=n("strong"),Cy=r("predict"),Iy=r(" \u2014 Returns predictions (with metrics if labels are available) on a test set."),Og=l(),h(Ro.$$.fragment),Cg=l(),Wo=n("p"),Uy=r("Here is an example of how to customize "),ui=n("a"),Ny=r("Trainer"),zy=r(" to use a weighted loss (useful when you have an unbalanced training set):"),Ig=l(),h(pa.$$.fragment),Ug=l(),lt=n("p"),Ly=r("Another way to customize the training loop behavior for the PyTorch "),fi=n("a"),Fy=r("Trainer"),Ry=r(" is to use "),gi=n("a"),Wy=r("callbacks"),Gy=r(" that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms\u2026) and take decisions (like early stopping)."),Ng=l(),Qt=n("h2"),Go=n("a"),bc=n("span"),h(ma.$$.fragment),jy=l(),yc=n("span"),My=r("Trainer"),zg=l(),b=n("div"),h(ha.$$.fragment),Vy=l(),wc=n("p"),Hy=r("Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for \u{1F917} Transformers."),By=l(),Ec=n("p"),Yy=r("Important attributes:"),Zy=l(),we=n("ul"),jo=n("li"),Tc=n("strong"),Jy=r("model"),Xy=r(" \u2014 Always points to the core model. If using a transformers model, it will be a "),_i=n("a"),Ky=r("PreTrainedModel"),Qy=r(`
subclass.`),e2=l(),J=n("li"),$c=n("strong"),t2=r("model_wrapped"),o2=r(` \u2014 Always points to the most external model in case one or more other modules wrap the
original model. This is the model that should be used for the forward pass. For example, under `),kc=n("code"),r2=r("DeepSpeed"),a2=r(`,
the inner model is wrapped in `),xc=n("code"),n2=r("DeepSpeed"),s2=r(" and then again in "),Ac=n("code"),i2=r("torch.nn.DistributedDataParallel"),l2=r(`. If the inner
model hasn\u2019t been wrapped, then `),Dc=n("code"),d2=r("self.model_wrapped"),c2=r(" is the same as "),qc=n("code"),p2=r("self.model"),m2=r("."),h2=l(),vi=n("li"),Pc=n("strong"),u2=r("is_model_parallel"),f2=r(` \u2014 Whether or not a model has been switched to a model parallel mode (different from
data parallelism, this means some of the model layers are split on different GPUs).`),g2=l(),De=n("li"),Sc=n("strong"),_2=r("place_model_on_device"),v2=r(` \u2014 Whether or not to automatically place the model on the device - it will be set
to `),Oc=n("code"),b2=r("False"),y2=r(` if model parallel or deepspeed is used, or if the default
`),Cc=n("code"),w2=r("TrainingArguments.place_model_on_device"),E2=r(" is overridden to return "),Ic=n("code"),T2=r("False"),$2=r(" ."),k2=l(),qe=n("li"),Uc=n("strong"),x2=r("is_in_train"),A2=r(" \u2014 Whether or not a model is currently running "),Nc=n("code"),D2=r("train"),q2=r(" (e.g. when "),zc=n("code"),P2=r("evaluate"),S2=r(` is called while
in `),Lc=n("code"),O2=r("train"),C2=r(")"),I2=l(),Mo=n("div"),h(ua.$$.fragment),U2=l(),fa=n("p"),N2=r("Add a callback to the current list of "),Fc=n("code"),z2=r("TrainerCallback"),L2=r("."),F2=l(),Vo=n("div"),h(ga.$$.fragment),R2=l(),_a=n("p"),W2=r("A helper wrapper that creates an appropriate context manager for "),Rc=n("code"),G2=r("autocast"),j2=r(` while feeding it the desired
arguments, depending on the situation.`),M2=l(),dt=n("div"),h(va.$$.fragment),V2=l(),Wc=n("p"),H2=r("How the loss is computed by Trainer. By default, all models return the loss in the first element."),B2=l(),Gc=n("p"),Y2=r("Subclass and override for custom behavior."),Z2=l(),ct=n("div"),h(ba.$$.fragment),J2=l(),jc=n("p"),X2=r("Setup the optimizer."),K2=l(),ya=n("p"),Q2=r(`We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer\u2019s init through `),Mc=n("code"),ew=r("optimizers"),tw=r(", or subclass and override this method in a subclass."),ow=l(),pt=n("div"),h(wa.$$.fragment),rw=l(),Vc=n("p"),aw=r("Setup the optimizer and the learning rate scheduler."),nw=l(),Xe=n("p"),sw=r(`We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer\u2019s init through `),Hc=n("code"),iw=r("optimizers"),lw=r(", or subclass and override this method (or "),Bc=n("code"),dw=r("create_optimizer"),cw=r(` and/or
`),Yc=n("code"),pw=r("create_scheduler"),mw=r(") in a subclass."),hw=l(),Ho=n("div"),h(Ea.$$.fragment),uw=l(),Zc=n("p"),fw=r(`Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.`),gw=l(),Pe=n("div"),h(Ta.$$.fragment),_w=l(),Jc=n("p"),vw=r("Run evaluation and returns metrics."),bw=l(),$a=n("p"),yw=r(`The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `),Xc=n("code"),ww=r("compute_metrics"),Ew=r(" argument)."),Tw=l(),Kc=n("p"),$w=r("You can also subclass and override this method to inject custom behavior."),kw=l(),mt=n("div"),h(ka.$$.fragment),xw=l(),eo=n("p"),Aw=r("Prediction/evaluation loop, shared by "),Qc=n("code"),Dw=r("Trainer.evaluate()"),qw=r(" and "),ep=n("code"),Pw=r("Trainer.predict()"),Sw=r("."),Ow=l(),tp=n("p"),Cw=r("Works both with or without labels."),Iw=l(),Bo=n("div"),h(xa.$$.fragment),Uw=l(),Aa=n("p"),Nw=r("For models that inherit from "),bi=n("a"),zw=r("PreTrainedModel"),Lw=r(`, uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
model or subclass and override this method.`),Fw=l(),ht=n("div"),h(Da.$$.fragment),Rw=l(),qa=n("p"),Ww=r("Returns the evaluation "),op=n("code"),Gw=r("DataLoader"),jw=r("."),Mw=l(),rp=n("p"),Vw=r("Subclass and override this method if you want to inject some custom behavior."),Hw=l(),Yo=n("div"),h(Pa.$$.fragment),Bw=l(),ap=n("p"),Yw=r("Returns the optimizer class and optimizer parameters based on the training arguments."),Zw=l(),ut=n("div"),h(Sa.$$.fragment),Jw=l(),Oa=n("p"),Xw=r("Returns the test "),np=n("code"),Kw=r("DataLoader"),Qw=r("."),e0=l(),sp=n("p"),t0=r("Subclass and override this method if you want to inject some custom behavior."),o0=l(),Se=n("div"),h(Ca.$$.fragment),r0=l(),Ia=n("p"),a0=r("Returns the training "),ip=n("code"),n0=r("DataLoader"),s0=r("."),i0=l(),to=n("p"),l0=r("Will use no sampler if "),lp=n("code"),d0=r("self.train_dataset"),c0=r(" does not implement "),dp=n("code"),p0=r("__len__"),m0=r(`, a random sampler (adapted to
distributed training if necessary) otherwise.`),h0=l(),cp=n("p"),u0=r("Subclass and override this method if you want to inject some custom behavior."),f0=l(),ft=n("div"),h(Ua.$$.fragment),g0=l(),Ee=n("p"),_0=r("Launch an hyperparameter search using "),pp=n("code"),v0=r("optuna"),b0=r(" or "),mp=n("code"),y0=r("Ray Tune"),w0=r(" or "),hp=n("code"),E0=r("SigOpt"),T0=r(`. The optimized quantity is determined
by `),up=n("code"),$0=r("compute_objective"),k0=r(`, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise.`),x0=l(),h(Zo.$$.fragment),A0=l(),Jo=n("div"),h(Na.$$.fragment),D0=l(),za=n("p"),q0=r("Initializes a git repo in "),fp=n("code"),P0=r("self.args.hub_model_id"),S0=r("."),O0=l(),Xo=n("div"),h(La.$$.fragment),C0=l(),gp=n("p"),I0=r(`Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.`),U0=l(),Ko=n("div"),h(Fa.$$.fragment),N0=l(),Ra=n("p"),z0=r(`Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be `),_p=n("code"),L0=r("True"),F0=r(" for one process)."),R0=l(),gt=n("div"),h(Wa.$$.fragment),W0=l(),Ga=n("p"),G0=r("Log "),vp=n("code"),j0=r("logs"),M0=r(" on the various objects watching training."),V0=l(),bp=n("p"),H0=r("Subclass and override this method to inject custom behavior."),B0=l(),k=n("div"),h(ja.$$.fragment),Y0=l(),yp=n("p"),Z0=r("Log metrics in a specially formatted way"),J0=l(),wp=n("p"),X0=r("Under distributed environment this is done only for a process with rank 0."),K0=l(),Ep=n("p"),Q0=r("Notes on memory reports:"),eE=l(),oo=n("p"),tE=r("In order to get memory usage report you need to install "),Tp=n("code"),oE=r("psutil"),rE=r(". You can do that with "),$p=n("code"),aE=r("pip install psutil"),nE=r("."),sE=l(),kp=n("p"),iE=r("Now when this method is run, you will see a report that will include: :"),lE=l(),h(Ma.$$.fragment),dE=l(),xp=n("p"),Ap=n("strong"),cE=r("Understanding the reports:"),pE=l(),Ke=n("ul"),Te=n("li"),mE=r("the first segment, e.g., "),Dp=n("code"),hE=r("train__"),uE=r(", tells you which stage the metrics are for. Reports starting with "),qp=n("code"),fE=r("init_"),gE=r(`
will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the
`),Pp=n("code"),_E=r("__init__"),vE=r(" will be reported along with the "),Sp=n("code"),bE=r("eval_"),yE=r(" metrics."),wE=l(),ro=n("li"),EE=r("the third segment, is either "),Op=n("code"),TE=r("cpu"),$E=r(" or "),Cp=n("code"),kE=r("gpu"),xE=r(`, tells you whether it\u2019s the general RAM or the gpu0 memory
metric.`),AE=l(),yi=n("li"),Ip=n("code"),DE=r("*_alloc_delta"),qE=r(` - is the difference in the used/allocated memory counter between the end and the start of the
stage - it can be negative if a function released more memory than it allocated.`),PE=l(),_t=n("li"),Up=n("code"),SE=r("*_peaked_delta"),OE=r(` - is any extra memory that was consumed and then freed - relative to the current allocated
memory counter - it is never negative. When you look at the metrics of any stage you add up `),Np=n("code"),CE=r("alloc_delta"),IE=r(` +
`),zp=n("code"),UE=r("peaked_delta"),NE=r(" and you know how much memory was needed to complete that stage."),zE=l(),Lp=n("p"),LE=r(`The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the
main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may
use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more
memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the
future these reports will evolve to measure those too.`),FE=l(),Fp=n("p"),RE=r(`The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the
memory shared with other processes. It is important to note that it does not include swapped out memory, so the
reports could be imprecise.`),WE=l(),Va=n("p"),GE=r(`The CPU peak memory is measured using a sampling thread. Due to python\u2019s GIL it may miss some of the peak memory if
that thread didn\u2019t get a chance to run when the highest memory was used. Therefore this report can be less than
reality. Using `),Rp=n("code"),jE=r("tracemalloc"),ME=r(` would have reported the exact peak memory, but it doesn\u2019t report memory allocations
outside of python. So if some C++ CUDA extension allocated its own memory it won\u2019t be reported. And therefore it
was dropped in favor of the memory sampling approach, which reads the current process memory usage.`),VE=l(),Qe=n("p"),HE=r("The GPU allocated and peak memory reporting is done with "),Wp=n("code"),BE=r("torch.cuda.memory_allocated()"),YE=r(` and
`),Gp=n("code"),ZE=r("torch.cuda.max_memory_allocated()"),JE=r(`. This metric reports only \u201Cdeltas\u201D for pytorch-specific allocations, as
`),jp=n("code"),XE=r("torch.cuda"),KE=r(` memory management system doesn\u2019t track any memory allocated outside of pytorch. For example, the very
first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.`),QE=l(),re=n("p"),e4=r("Note that this tracker doesn\u2019t account for memory allocations outside of "),wi=n("a"),t4=r("Trainer"),o4=r("\u2019s "),Mp=n("code"),r4=r("__init__"),a4=r(", "),Vp=n("code"),n4=r("train"),s4=r(`,
`),Hp=n("code"),i4=r("evaluate"),l4=r(" and "),Bp=n("code"),d4=r("predict"),c4=r(" calls."),p4=l(),I=n("p"),m4=r("Because "),Yp=n("code"),h4=r("evaluation"),u4=r(" calls may happen during "),Zp=n("code"),f4=r("train"),g4=r(`, we can\u2019t handle nested invocations because
`),Jp=n("code"),_4=r("torch.cuda.max_memory_allocated"),v4=r(" is a single counter, so if it gets reset by a nested eval call, "),Xp=n("code"),b4=r("train"),y4=r(`\u2019s tracker
will report incorrect info. If this `),Ha=n("a"),w4=r("pytorch issue"),E4=r(` gets resolved
it will be possible to change this class to be re-entrant. Until then we will only track the outer level of
`),Kp=n("code"),T4=r("train"),$4=r(", "),Qp=n("code"),k4=r("evaluate"),x4=r(" and "),em=n("code"),A4=r("predict"),D4=r(" methods. Which means that if "),tm=n("code"),q4=r("eval"),P4=r(" is called during "),om=n("code"),S4=r("train"),O4=r(`, it\u2019s the latter
that will account for its memory usage and that of the former.`),C4=l(),$e=n("p"),I4=r("This also means that if any other tool that is used along the "),Ei=n("a"),U4=r("Trainer"),N4=r(` calls
`),rm=n("code"),z4=r("torch.cuda.reset_peak_memory_stats"),L4=r(", the gpu peak memory stats could be invalid. And the "),Ti=n("a"),F4=r("Trainer"),R4=r(` will disrupt
the normal behavior of any such tools that rely on calling `),am=n("code"),W4=r("torch.cuda.reset_peak_memory_stats"),G4=r(" themselves."),j4=l(),nm=n("p"),M4=r("For best performance you may want to consider turning the memory profiling off for production runs."),V4=l(),Qo=n("div"),h(Ba.$$.fragment),H4=l(),sm=n("p"),B4=r("Reformat Trainer metrics values to a human-readable format"),Y4=l(),vt=n("div"),h(Ya.$$.fragment),Z4=l(),Za=n("p"),J4=r("Helper to get number of samples in a "),im=n("code"),X4=r("DataLoader"),K4=r(" by accessing its dataset."),Q4=l(),$i=n("p"),eT=r("Will raise an exception if the underlying dataset does not implement method "),lm=n("code"),tT=r("__len__"),oT=l(),bt=n("div"),h(Ja.$$.fragment),rT=l(),Xa=n("p"),aT=r("Remove a callback from the current list of "),dm=n("code"),nT=r("TrainerCallback"),sT=r(" and returns it."),iT=l(),Ka=n("p"),lT=r("If the callback is not found, returns "),cm=n("code"),dT=r("None"),cT=r(" (and no error is raised)."),pT=l(),X=n("div"),h(Qa.$$.fragment),mT=l(),pm=n("p"),hT=r("Run prediction and returns predictions and potential metrics."),uT=l(),en=n("p"),fT=r(`Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
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labels).`),WT=l(),yt=n("div"),h(on.$$.fragment),GT=l(),lo=n("p"),jT=r("Prediction/evaluation loop, shared by "),ym=n("code"),MT=r("Trainer.evaluate()"),VT=r(" and "),wm=n("code"),HT=r("Trainer.predict()"),BT=r("."),YT=l(),Em=n("p"),ZT=r("Works both with or without labels."),JT=l(),wt=n("div"),h(rn.$$.fragment),XT=l(),co=n("p"),KT=r("Perform an evaluation step on "),Tm=n("code"),QT=r("model"),e3=r(" using "),$m=n("code"),t3=r("inputs"),o3=r("."),r3=l(),km=n("p"),a3=r("Subclass and override to inject custom behavior."),n3=l(),tr=n("div"),h(an.$$.fragment),s3=l(),et=n("p"),i3=r("Upload "),xm=n("em"),l3=r("self.model"),d3=r(" and "),Am=n("em"),c3=r("self.tokenizer"),p3=r(" to the \u{1F917} model hub on the repo "),Dm=n("em"),m3=r("self.args.hub_model_id"),h3=r("."),u3=l(),or=n("div"),h(nn.$$.fragment),f3=l(),sn=n("p"),g3=r("Remove a callback from the current list of "),qm=n("code"),_3=r("TrainerCallback"),v3=r("."),b3=l(),Oe=n("div"),h(ln.$$.fragment),y3=l(),dn=n("p"),w3=r("Save metrics into a json file for that split, e.g. "),Pm=n("code"),E3=r("train_results.json"),T3=r("."),$3=l(),Sm=n("p"),k3=r("Under distributed environment this is done only for a process with rank 0."),x3=l(),cn=n("p"),A3=r("To understand the metrics please read the docstring of "),Om=n("code"),D3=r("log_metrics()"),q3=r(` The only difference is that raw
unformatted numbers are saved in the current method.`),P3=l(),Et=n("div"),h(pn.$$.fragment),S3=l(),mn=n("p"),O3=r("Will save the model, so you can reload it using "),Cm=n("code"),C3=r("from_pretrained()"),I3=r("."),U3=l(),Im=n("p"),N3=r("Will only save from the main process."),z3=l(),Tt=n("div"),h(hn.$$.fragment),L3=l(),Um=n("p"),F3=r("Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model"),R3=l(),Nm=n("p"),W3=r("Under distributed environment this is done only for a process with rank 0."),G3=l(),rr=n("div"),h(un.$$.fragment),j3=l(),zm=n("p"),M3=r("Main training entry point."),V3=l(),$t=n("div"),h(fn.$$.fragment),H3=l(),Lm=n("p"),B3=r("Perform a training step on a batch of inputs."),Y3=l(),Fm=n("p"),Z3=r("Subclass and override to inject custom behavior."),Lg=l(),po=n("h2"),ar=n("a"),Rm=n("span"),h(gn.$$.fragment),J3=l(),Wm=n("span"),X3=r("Seq2SeqTrainer"),Fg=l(),tt=n("div"),h(_n.$$.fragment),K3=l(),Ce=n("div"),h(vn.$$.fragment),Q3=l(),Gm=n("p"),e6=r("Run evaluation and returns metrics."),t6=l(),bn=n("p"),o6=r(`The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `),jm=n("code"),r6=r("compute_metrics"),a6=r(" argument)."),n6=l(),Mm=n("p"),s6=r("You can also subclass and override this method to inject custom behavior."),i6=l(),K=n("div"),h(yn.$$.fragment),l6=l(),Vm=n("p"),d6=r("Run prediction and returns predictions and potential metrics."),c6=l(),wn=n("p"),p6=r(`Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `),Hm=n("code"),m6=r("evaluate()"),h6=r("."),u6=l(),h(nr.$$.fragment),f6=l(),En=n("p"),g6=r("Returns: "),Bm=n("em"),_6=r("NamedTuple"),v6=r(" A namedtuple with the following keys:"),b6=l(),mo=n("ul"),ho=n("li"),y6=r("predictions ("),Ym=n("code"),w6=r("np.ndarray"),E6=r("): The predictions on "),Zm=n("code"),T6=r("test_dataset"),$6=r("."),k6=l(),uo=n("li"),x6=r("label_ids ("),Jm=n("code"),A6=r("np.ndarray"),D6=r(", "),Xm=n("em"),q6=r("optional"),P6=r("): The labels (if the dataset contained some)."),S6=l(),fo=n("li"),O6=r("metrics ("),Km=n("code"),C6=r("Dict[str, float]"),I6=r(", "),Qm=n("em"),U6=r("optional"),N6=r(`): The potential dictionary of metrics (if the dataset contained
labels).`),Rg=l(),go=n("h2"),sr=n("a"),eh=n("span"),h(Tn.$$.fragment),z6=l(),th=n("span"),L6=r("TrainingArguments"),Wg=l(),z=n("div"),h($n.$$.fragment),F6=l(),kn=n("p"),R6=r("TrainingArguments is the subset of the arguments we use in our example scripts "),oh=n("strong"),W6=r(`which relate to the training loop
itself`),G6=r("."),j6=l(),_o=n("p"),M6=r("Using "),ki=n("a"),V6=r("HfArgumentParser"),H6=r(` we can turn this class into
`),xn=n("a"),B6=r("argparse"),Y6=r(` arguments that can be specified on the
command line.`),Z6=l(),ce=n("div"),h(An.$$.fragment),J6=l(),rh=n("p"),X6=r(`Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.`),K6=l(),vo=n("p"),Q6=r("For the main process the log level defaults to "),ah=n("code"),e$=r("logging.INFO"),t$=r(" unless overridden by "),nh=n("code"),o$=r("log_level"),r$=r(" argument."),a$=l(),bo=n("p"),n$=r("For the replica processes the log level defaults to "),sh=n("code"),s$=r("logging.WARNING"),i$=r(" unless overridden by "),ih=n("code"),l$=r("log_level_replica"),d$=r(`
argument.`),c$=l(),Dn=n("p"),p$=r("The choice between the main and replica process settings is made according to the return value of "),lh=n("code"),m$=r("should_log"),h$=r("."),u$=l(),ir=n("div"),h(qn.$$.fragment),f$=l(),dh=n("p"),g$=r("Get number of steps used for a linear warmup."),_$=l(),kt=n("div"),h(Pn.$$.fragment),v$=l(),ch=n("p"),b$=r(`A context manager for torch distributed environment where on needs to do something on the main process, while
blocking replicas, and when it\u2019s finished releasing the replicas.`),y$=l(),yo=n("p"),w$=r("One such use is for "),ph=n("code"),E$=r("datasets"),T$=r("\u2019s "),mh=n("code"),$$=r("map"),k$=r(` feature which to be efficient should be run once on the main process,
which upon completion saves a cached version of results and which then automatically gets loaded by the
replicas.`),x$=l(),lr=n("div"),h(Sn.$$.fragment),A$=l(),On=n("p"),D$=r("Serializes this instance while replace "),hh=n("code"),q$=r("Enum"),P$=r(` by their values (for JSON serialization support). It obfuscates
the token values by removing their value.`),S$=l(),dr=n("div"),h(Cn.$$.fragment),O$=l(),uh=n("p"),C$=r("Serializes this instance to a JSON string."),I$=l(),cr=n("div"),h(In.$$.fragment),U$=l(),fh=n("p"),N$=r("Sanitized serialization to use with TensorBoard\u2019s hparams"),Gg=l(),wo=n("h2"),pr=n("a"),gh=n("span"),h(Un.$$.fragment),z$=l(),_h=n("span"),L$=r("Seq2SeqTrainingArguments"),jg=l(),ae=n("div"),h(Nn.$$.fragment),F$=l(),zn=n("p"),R$=r("TrainingArguments is the subset of the arguments we use in our example scripts "),vh=n("strong"),W$=r(`which relate to the training loop
itself`),G$=r("."),j$=l(),Eo=n("p"),M$=r("Using "),xi=n("a"),V$=r("HfArgumentParser"),H$=r(` we can turn this class into
`),Ln=n("a"),B$=r("argparse"),Y$=r(` arguments that can be specified on the
command line.`),Z$=l(),ne=n("p"),J$=r("sortish_sampler ("),bh=n("code"),X$=r("bool"),K$=r(", "),yh=n("em"),Q$=r("optional"),ek=r(", defaults to "),wh=n("code"),tk=r("False"),ok=r(`):
Whether to use a `),Eh=n("em"),rk=r("sortish sampler"),ak=r(" or not. Only possible if the underlying datasets are "),Th=n("em"),nk=r("Seq2SeqDataset"),sk=r(` for
now but will become generally available in the near future.`),ik=l(),q=n("p"),lk=r(`It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for
the training set.
predict_with_generate (`),$h=n("code"),dk=r("bool"),ck=r(", "),kh=n("em"),pk=r("optional"),mk=r(", defaults to "),xh=n("code"),hk=r("False"),uk=r(`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
generation_max_length (`),Ah=n("code"),fk=r("int"),gk=r(", "),Dh=n("em"),_k=r("optional"),vk=r(`):
The `),qh=n("code"),bk=r("max_length"),yk=r(" to use on each evaluation loop when "),Ph=n("code"),wk=r("predict_with_generate=True"),Ek=r(`. Will default to the
`),Sh=n("code"),Tk=r("max_length"),$k=r(` value of the model configuration.
generation_num_beams (`),Oh=n("code"),kk=r("int"),xk=r(", "),Ch=n("em"),Ak=r("optional"),Dk=r(`):
The `),Ih=n("code"),qk=r("num_beams"),Pk=r(" to use on each evaluation loop when "),Uh=n("code"),Sk=r("predict_with_generate=True"),Ok=r(`. Will default to the
`),Nh=n("code"),Ck=r("num_beams"),Ik=r(" value of the model configuration."),Mg=l(),To=n("h2"),mr=n("a"),zh=n("span"),h(Fn.$$.fragment),Uk=l(),Lh=n("span"),Nk=r("Checkpoints"),Vg=l(),pe=n("p"),zk=r("By default, "),Ai=n("a"),Lk=r("Trainer"),Fk=r(" will save all checkpoints in the "),Fh=n("code"),Rk=r("output_dir"),Wk=r(` you set in the
`),Di=n("a"),Gk=r("TrainingArguments"),jk=r(" you are using. Those will go in subfolder named "),Rh=n("code"),Mk=r("checkpoint-xxx"),Vk=r(` with xxx
being the step at which the training was at.`),Hg=l(),hr=n("p"),Hk=r("Resuming training from a checkpoint can be done when calling "),qi=n("a"),Bk=r("Trainer.train()"),Yk=r(" with either:"),Bg=l(),ur=n("ul"),Pi=n("li"),Wh=n("code"),Zk=r("resume_from_checkpoint=True"),Jk=r(" which will resume training from the latest checkpoint"),Xk=l(),Si=n("li"),Gh=n("code"),Kk=r("resume_from_checkpoint=checkpoint_dir"),Qk=r(` which will resume training from the specific checkpoint in the directory
passed.`),Yg=l(),Ie=n("p"),e5=r("In addition, you can easily save your checkpoints on the Model Hub when using "),jh=n("code"),t5=r("push_to_hub=True"),o5=r(`. By default, all
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
the `),Mh=n("code"),r5=r("hub-strategy"),a5=r(" value of your "),Oi=n("a"),n5=r("TrainingArguments"),s5=r(" to either:"),Zg=l(),fr=n("ul"),gr=n("li"),Vh=n("code"),i5=r('"checkpoint"'),l5=r(`: the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
resume training easily with `),Hh=n("code"),d5=r('trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")'),c5=r("."),p5=l(),Ci=n("li"),Bh=n("code"),m5=r('"all_checkpoints"'),h5=r(`: all checkpoints are pushed like they appear in the output folder (so you will get one
checkpoint folder per folder in your final repository)`),Jg=l(),$o=n("h2"),_r=n("a"),Yh=n("span"),h(Rn.$$.fragment),u5=l(),Zh=n("span"),f5=r("Logging"),Xg=l(),Ue=n("p"),g5=r("By default "),Ii=n("a"),_5=r("Trainer"),v5=r(" will use "),Jh=n("code"),b5=r("logging.INFO"),y5=r(" for the main process and "),Xh=n("code"),w5=r("logging.WARNING"),E5=r(" for the replicas if any."),Kg=l(),xt=n("p"),T5=r("These defaults can be overridden to use any of the 5 "),Kh=n("code"),$5=r("logging"),k5=r(" levels with "),Ui=n("a"),x5=r("TrainingArguments"),A5=r(`\u2019s
arguments:`),Qg=l(),vr=n("ul"),Ni=n("li"),Qh=n("code"),D5=r("log_level"),q5=r(" - for the main process"),P5=l(),zi=n("li"),eu=n("code"),S5=r("log_level_replica"),O5=r(" - for the replicas"),e_=l(),Ne=n("p"),C5=r("Further, if "),Li=n("a"),I5=r("TrainingArguments"),U5=r("\u2019s "),tu=n("code"),N5=r("log_on_each_node"),z5=r(" is set to "),ou=n("code"),L5=r("False"),F5=r(` only the main node will
use the log level settings for its main process, all other nodes will use the log level settings for replicas.`),t_=l(),Q=n("p"),R5=r("Note that "),Fi=n("a"),W5=r("Trainer"),G5=r(" is going to set "),ru=n("code"),j5=r("transformers"),M5=r(`\u2019s log level separately for each node in its
`),au=n("code"),V5=r("Trainer.__init__()"),H5=r(` So you may want to set this sooner (see the next example) if you tap into other
`),nu=n("code"),B5=r("transformers"),Y5=r(" functionality before creating the "),Ri=n("a"),Z5=r("Trainer"),J5=r(" object."),o_=l(),Wi=n("p"),X5=r("Here is an example of how this can be used in an application:"),r_=l(),h(Wn.$$.fragment),a_=l(),Gi=n("p"),K5=r(`And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
warnings you could run it as:`),n_=l(),h(Gn.$$.fragment),s_=l(),ji=n("p"),Q5=r(`In the multi-node environment if you also don\u2019t want the logs to repeat for each node\u2019s main process, you will want to
change the above to:`),i_=l(),h(jn.$$.fragment),l_=l(),Mi=n("p"),ex=r(`and then only the main process of the first node will log at the \u201Cwarning\u201D level, and all other processes on the main
node and all processes on other nodes will log at the \u201Cerror\u201D level.`),d_=l(),Vi=n("p"),tx=r("If you need your application to be as quiet as possible you could do:"),c_=l(),h(Mn.$$.fragment),p_=l(),br=n("p"),ox=r("(add "),su=n("code"),rx=r("--log_on_each_node 0"),ax=r(" if on multi-node environment)"),m_=l(),ko=n("h2"),yr=n("a"),iu=n("span"),h(Vn.$$.fragment),nx=l(),lu=n("span"),sx=r("Randomness"),h_=l(),me=n("p"),ix=r("When resuming from a checkpoint generated by "),Hi=n("a"),lx=r("Trainer"),dx=r(` all efforts are made to restore the
`),du=n("em"),cx=r("python"),px=r(", "),cu=n("em"),mx=r("numpy"),hx=r(" and "),pu=n("em"),ux=r("pytorch"),fx=r(` RNG states to the same states as they were at the moment of saving that checkpoint,
which should make the \u201Cstop and resume\u201D style of training as close as possible to non-stop training.`),u_=l(),At=n("p"),gx=r(`However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
determinism please refer to `),Hn=n("a"),_x=r("Controlling sources of randomness"),vx=r(`. As explained in the document, that some of those settings
that make things deterministic (.e.g., `),mu=n("code"),bx=r("torch.backends.cudnn.deterministic"),yx=r(`) may slow things down, therefore this
can\u2019t be done by default, but you can enable those yourself if needed.`),f_=l(),xo=n("h2"),wr=n("a"),hu=n("span"),h(Bn.$$.fragment),wx=l(),uu=n("span"),Ex=r("Specific GPUs Selection"),g_=l(),Bi=n("p"),Tx=r("Let\u2019s discuss how you can tell your program which GPUs are to be used and in what order."),__=l(),Er=n("p"),$x=r("When using "),Yn=n("a"),fu=n("code"),kx=r("DistributedDataParallel"),xx=r(" to use only a subset of your GPUs, you simply specify the number of GPUs to use. For example, if you have 4 GPUs, but you wish to use the first 2 you can do:"),v_=l(),h(Zn.$$.fragment),b_=l(),Dt=n("p"),Ax=r("if you have either "),Jn=n("a"),gu=n("code"),Dx=r("accelerate"),qx=r(" or "),Xn=n("a"),_u=n("code"),Px=r("deepspeed"),Sx=r(" installed you can also accomplish the same by using one of:"),y_=l(),h(Kn.$$.fragment),w_=l(),h(Qn.$$.fragment),E_=l(),Tr=n("p"),Ox=r("You don\u2019t need to use the Accelerate or "),Yi=n("a"),Cx=r("the Deepspeed integration"),Ix=r(" features to use these launchers."),T_=l(),Zi=n("p"),Ux=r("Until now you were able to tell the program how many GPUs to use. Now let\u2019s discuss how to select specific GPUs and control their order."),$_=l(),Ji=n("p"),Nx=r("The following environment variables help you control which GPUs to use and their order."),k_=l(),Xi=n("p"),vu=n("strong"),bu=n("code"),zx=r("CUDA_VISIBLE_DEVICES"),x_=l(),$r=n("p"),Lx=r("If you have multiple GPUs and you\u2019d like to use only 1 or a few of those GPUs, set the environment variable "),yu=n("code"),Fx=r("CUDA_VISIBLE_DEVICES"),Rx=r(" to a list of the GPUs to be used."),A_=l(),Ki=n("p"),Wx=r("For example, let\u2019s say you have 4 GPUs: 0, 1, 2 and 3. To run only on the physical GPUs 0 and 2, you can do:"),D_=l(),h(es.$$.fragment),q_=l(),qt=n("p"),Gx=r("So now pytorch will see only 2 GPUs, where your physical GPUs 0 and 2 are mapped to "),wu=n("code"),jx=r("cuda:0"),Mx=r(" and "),Eu=n("code"),Vx=r("cuda:1"),Hx=r(" correspondingly."),P_=l(),Qi=n("p"),Bx=r("You can even change their order:"),S_=l(),h(ts.$$.fragment),O_=l(),Pt=n("p"),Yx=r("Here your physical GPUs 0 and 2 are mapped to "),Tu=n("code"),Zx=r("cuda:1"),Jx=r(" and "),$u=n("code"),Xx=r("cuda:0"),Kx=r(" correspondingly."),C_=l(),St=n("p"),Qx=r("The above examples were all for "),ku=n("code"),eA=r("DistributedDataParallel"),tA=r(" use pattern, but the same method works for "),os=n("a"),xu=n("code"),oA=r("DataParallel"),rA=r(" as well:"),I_=l(),h(rs.$$.fragment),U_=l(),el=n("p"),aA=r("To emulate an environment without GPUs simply set this environment variable to an empty value like so:"),N_=l(),h(as.$$.fragment),z_=l(),tl=n("p"),nA=r("As with any environment variable you can, of course, export those instead of adding these to the command line, as in:"),L_=l(),h(ns.$$.fragment),F_=l(),ol=n("p"),sA=r("but this approach can be confusing since you may forget you set up the environment variable earlier and not understand why the wrong GPUs are used. Therefore, it\u2019s a common practice to set the environment variable just for a specific run on the same command line as it\u2019s shown in most examples of this section."),R_=l(),rl=n("p"),Au=n("strong"),Du=n("code"),iA=r("CUDA_DEVICE_ORDER"),W_=l(),kr=n("p"),lA=r("There is an additional environment variable "),qu=n("code"),dA=r("CUDA_DEVICE_ORDER"),cA=r(" that controls how the physical devices are ordered. The two choices are:"),G_=l(),al=n("ol"),ss=n("li"),pA=r("ordered by PCIe bus IDs (matches "),Pu=n("code"),mA=r("nvidia-smi"),hA=r("\u2019s order) - this is the default."),j_=l(),h(is.$$.fragment),M_=l(),ls=n("ol"),Su=n("li"),uA=r("ordered by GPU compute capabilities"),V_=l(),h(ds.$$.fragment),H_=l(),Ot=n("p"),fA=r("Most of the time you don\u2019t need to care about this environment variable, but it\u2019s very helpful if you have a lopsided setup where you have an old and a new GPUs physically inserted in such a way so that the slow older card appears to be first. One way to fix that is to swap the cards. But if you can\u2019t swap the cards (e.g., if the cooling of the devices gets impacted) then setting "),Ou=n("code"),gA=r("CUDA_DEVICE_ORDER=FASTEST_FIRST"),_A=r(" will always put the newer faster card first. It\u2019ll be somewhat confusing though since "),Cu=n("code"),vA=r("nvidia-smi"),bA=r(" will still report them in the PCIe order."),B_=l(),nl=n("p"),yA=r("The other solution to swapping the order is to use:"),Y_=l(),h(cs.$$.fragment),Z_=l(),sl=n("p"),wA=r("In this example we are working with just 2 GPUs, but of course the same would apply to as many GPUs as your computer has."),J_=l(),xr=n("p"),EA=r("Also if you do set this environment variable it\u2019s the best to set it in your "),Iu=n("code"),TA=r("~/.bashrc"),$A=r(" file or some other startup config file and forget about it."),X_=l(),Ao=n("h2"),Ar=n("a"),Uu=n("span"),h(ps.$$.fragment),kA=l(),Nu=n("span"),xA=r("Trainer Integrations"),K_=l(),Dr=n("p"),AA=r("The "),il=n("a"),DA=r("Trainer"),qA=r(` has been extended to support libraries that may dramatically improve your training
time and fit much bigger models.`),Q_=l(),ze=n("p"),PA=r("Currently it supports third party solutions, "),ms=n("a"),SA=r("DeepSpeed"),OA=r(" and "),hs=n("a"),CA=r("FairScale"),IA=r(", which implement parts of the paper "),us=n("a"),UA=r(`ZeRO: Memory Optimizations
Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He`),NA=r("."),ev=l(),ll=n("p"),zA=r("This provided support is new and experimental as of this writing."),tv=l(),dl=n("a"),ov=l(),Do=n("h3"),qr=n("a"),zu=n("span"),h(fs.$$.fragment),LA=l(),Lu=n("span"),FA=r("CUDA Extension Installation Notes"),rv=l(),cl=n("p"),RA=r("As of this writing, both FairScale and Deepspeed require compilation of CUDA C++ code, before they can be used."),av=l(),Ct=n("p"),WA=r("While all installation issues should be dealt with through the corresponding GitHub Issues of "),gs=n("a"),GA=r("FairScale"),jA=r(" and "),_s=n("a"),MA=r("Deepspeed"),VA=r(`, there are a few common issues that one may encounter while building
any PyTorch extension that needs to build CUDA extensions.`),nv=l(),pl=n("p"),HA=r("Therefore, if you encounter a CUDA-related build issue while doing one of the following or both:"),sv=l(),h(vs.$$.fragment),iv=l(),ml=n("p"),BA=r("please, read the following notes first."),lv=l(),It=n("p"),YA=r("In these notes we give examples for what to do when "),Fu=n("code"),ZA=r("pytorch"),JA=r(" has been built with CUDA "),Ru=n("code"),XA=r("10.2"),KA=r(`. If your situation is
different remember to adjust the version number to the one you are after.`),dv=l(),qo=n("h4"),Pr=n("a"),Wu=n("span"),h(bs.$$.fragment),QA=l(),Gu=n("span"),e9=r("Possible problem #1"),cv=l(),hl=n("p"),t9=r(`While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA
installed system-wide.`),pv=l(),Le=n("p"),o9=r("For example, if you installed "),ju=n("code"),r9=r("pytorch"),a9=r(" with "),Mu=n("code"),n9=r("cudatoolkit==10.2"),s9=r(` in the Python environment, you also need to have
CUDA `),Vu=n("code"),i9=r("10.2"),l9=r(" installed system-wide."),mv=l(),Ut=n("p"),d9=r("The exact location may vary from system to system, but "),Hu=n("code"),c9=r("/usr/local/cuda-10.2"),p9=r(` is the most common location on many
Unix systems. When CUDA is correctly set up and added to the `),Bu=n("code"),m9=r("PATH"),h9=r(` environment variable, one can find the
installation location by doing:`),hv=l(),h(ys.$$.fragment),uv=l(),Sr=n("p"),u9=r(`If you don\u2019t have CUDA installed system-wide, install it first. You will find the instructions by using your favorite
search engine. For example, if you\u2019re on Ubuntu you may want to search for: `),ws=n("a"),f9=r("ubuntu cuda 10.2 install"),g9=r("."),fv=l(),Po=n("h4"),Or=n("a"),Yu=n("span"),h(Es.$$.fragment),_9=l(),Zu=n("span"),v9=r("Possible problem #2"),gv=l(),ul=n("p"),b9=r(`Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you
may have:`),_v=l(),h(Ts.$$.fragment),vv=l(),Nt=n("p"),y9=r("Now, in this situation you need to make sure that your "),Ju=n("code"),w9=r("PATH"),E9=r(" and "),Xu=n("code"),T9=r("LD_LIBRARY_PATH"),$9=r(` environment variables contain
the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the
last version was installed. If you encounter the problem, where the package build fails because it can\u2019t find the right
CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned
environment variables.`),bv=l(),fl=n("p"),k9=r("First, you may look at their contents:"),yv=l(),h($s.$$.fragment),wv=l(),gl=n("p"),x9=r("so you get an idea of what is inside."),Ev=l(),Cr=n("p"),A9=r("It\u2019s possible that "),Ku=n("code"),D9=r("LD_LIBRARY_PATH"),q9=r(" is empty."),Tv=l(),ot=n("p"),Qu=n("code"),P9=r("PATH"),S9=r(" lists the locations of where executables can be found and "),ef=n("code"),O9=r("LD_LIBRARY_PATH"),C9=r(` is for where shared libraries
are to looked for. In both cases, earlier entries have priority over the later ones. `),tf=n("code"),I9=r(":"),U9=r(` is used to separate multiple
entries.`),$v=l(),_l=n("p"),N9=r(`Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by
doing:`),kv=l(),h(ks.$$.fragment),xv=l(),vl=n("p"),z9=r("Note that we aren\u2019t overwriting the existing values, but prepending instead."),Av=l(),Fe=n("p"),L9=r(`Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do
exist. `),of=n("code"),F9=r("lib64"),R9=r(" sub-directory is where the various CUDA "),rf=n("code"),W9=r(".so"),G9=r(" objects, like "),af=n("code"),j9=r("libcudart.so"),M9=r(` reside, it\u2019s unlikely
that your system will have it named differently, but if it is adjust it to reflect your reality.`),Dv=l(),So=n("h4"),Ir=n("a"),nf=n("span"),h(xs.$$.fragment),V9=l(),sf=n("span"),H9=r("Possible problem #3"),qv=l(),zt=n("p"),B9=r("Some older CUDA versions may refuse to build with newer compilers. For example, you my have "),lf=n("code"),Y9=r("gcc-9"),Z9=r(` but it wants
`),df=n("code"),J9=r("gcc-7"),X9=r("."),Pv=l(),bl=n("p"),K9=r("There are various ways to go about it."),Sv=l(),yl=n("p"),Q9=r("If you can install the latest CUDA toolkit it typically should support the newer compiler."),Ov=l(),Ur=n("p"),e8=r(`Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may
already have it but it\u2019s not the default one, so the build system can\u2019t see it. If you have `),cf=n("code"),t8=r("gcc-7"),o8=r(` installed but the
build system complains it can\u2019t find it, the following might do the trick:`),Cv=l(),h(As.$$.fragment),Iv=l(),j=n("p"),r8=r("Here, we are making a symlink to "),pf=n("code"),a8=r("gcc-7"),n8=r(" from "),mf=n("code"),s8=r("/usr/local/cuda-10.2/bin/gcc"),i8=r(` and since
`),hf=n("code"),l8=r("/usr/local/cuda-10.2/bin/"),d8=r(" should be in the "),uf=n("code"),c8=r("PATH"),p8=r(` environment variable (see the previous problem\u2019s solution), it
should find `),ff=n("code"),m8=r("gcc-7"),h8=r(" (and "),gf=n("code"),u8=r("g++7"),f8=r(") and then the build will succeed."),Uv=l(),wl=n("p"),g8=r("As always make sure to edit the paths in the example to match your situation."),Nv=l(),Oo=n("h3"),Nr=n("a"),_f=n("span"),h(Ds.$$.fragment),_8=l(),vf=n("span"),v8=r("FairScale"),zv=l(),Re=n("p"),b8=r("By integrating "),qs=n("a"),y8=r("FairScale"),w8=r(" the "),El=n("a"),E8=r("Trainer"),T8=r(`
provides support for the following features from `),Ps=n("a"),$8=r("the ZeRO paper"),k8=r(":"),Lv=l(),We=n("ol"),bf=n("li"),x8=r("Optimizer State Sharding"),A8=l(),yf=n("li"),D8=r("Gradient Sharding"),q8=l(),wf=n("li"),P8=r("Model Parameters Sharding (new and very experimental)"),S8=l(),Ef=n("li"),O8=r("CPU offload (new and very experimental)"),Fv=l(),Tl=n("p"),C8=r("You will need at least two GPUs to use this feature."),Rv=l(),Ss=n("p"),Tf=n("strong"),I8=r("Installation"),U8=r(":"),Wv=l(),$l=n("p"),N8=r("Install the library via pypi:"),Gv=l(),h(Os.$$.fragment),jv=l(),Lt=n("p"),z8=r("or via "),$f=n("code"),L8=r("transformers"),F8=r("\u2019 "),kf=n("code"),R8=r("extras"),W8=r(":"),Mv=l(),h(Cs.$$.fragment),Vv=l(),Ft=n("p"),G8=r("(available starting from "),xf=n("code"),j8=r("transformers==4.6.0"),M8=r(") or find more details on "),Is=n("a"),V8=r("the FairScale\u2019s GitHub page"),H8=r("."),Hv=l(),zr=n("p"),B8=r("If you\u2019re still struggling with the build, first make sure to read "),kl=n("a"),Y8=r("CUDA Extension Installation Notes"),Z8=r("."),Bv=l(),xl=n("p"),J8=r("If it\u2019s still not resolved the build issue, here are a few more ideas."),Yv=l(),Us=n("p"),Af=n("code"),X8=r("fairscale"),K8=r(` seems to have an issue with the recently introduced by pip build isolation feature. If you have a problem
with it, you may want to try one of:`),Zv=l(),h(Ns.$$.fragment),Jv=l(),Al=n("p"),Q8=r("or:"),Xv=l(),h(zs.$$.fragment),Kv=l(),Ls=n("p"),Df=n("code"),eD=r("fairscale"),tD=r(" also has issues with building against pytorch-nightly, so if you use it you may have to try one of:"),Qv=l(),h(Fs.$$.fragment),e1=l(),Dl=n("p"),oD=r("or:"),t1=l(),h(Rs.$$.fragment),o1=l(),ql=n("p"),rD=r("Of course, adjust the urls to match the cuda version you use."),r1=l(),Lr=n("p"),aD=r(`If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of
`),Ws=n("a"),nD=r("FairScale"),sD=r("."),a1=l(),Gs=n("p"),qf=n("strong"),iD=r("Usage"),lD=r(":"),n1=l(),Rt=n("p"),dD=r("To use the first version of Sharded data-parallelism, add "),Pf=n("code"),cD=r("--sharded_ddp simple"),pD=r(` to the command line arguments, and
make sure you have added the distributed launcher `),Sf=n("code"),mD=r("-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE"),hD=r(" if you haven\u2019t been using it already."),s1=l(),Fr=n("p"),uD=r("For example here is how you could use it for "),Of=n("code"),fD=r("run_translation.py"),gD=r(" with 2 GPUs:"),i1=l(),h(js.$$.fragment),l1=l(),Pl=n("p"),_D=r("Notes:"),d1=l(),Ge=n("ul"),Cf=n("li"),vD=r("This feature requires distributed training (so multiple GPUs)."),bD=l(),If=n("li"),yD=r("It is not implemented for TPUs."),wD=l(),Ms=n("li"),ED=r("It works with "),Uf=n("code"),TD=r("--fp16"),$D=r(" too, to make things even faster."),kD=l(),Vs=n("li"),xD=r("One of the main benefits of enabling "),Nf=n("code"),AD=r("--sharded_ddp simple"),DD=r(` is that it uses a lot less GPU memory, so you should be
able to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
significantly shorter training time.`),c1=l(),Hs=n("ol"),rt=n("li"),qD=r("To use the second version of Sharded data-parallelism, add "),zf=n("code"),PD=r("--sharded_ddp zero_dp_2"),SD=r(" or "),Lf=n("code"),OD=r("--sharded_ddp zero_dp_3"),CD=r(" to the command line arguments, and make sure you have added the distributed launcher "),Ff=n("code"),ID=r("-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE"),UD=r(" if you haven\u2019t been using it already."),p1=l(),Rr=n("p"),ND=r("For example here is how you could use it for "),Rf=n("code"),zD=r("run_translation.py"),LD=r(" with 2 GPUs:"),m1=l(),h(Bs.$$.fragment),h1=l(),Co=n("p"),Wf=n("code"),FD=r("zero_dp_2"),RD=r(" is an optimized version of the simple wrapper, while "),Gf=n("code"),WD=r("zero_dp_3"),GD=r(` fully shards model weights,
gradients and optimizer states.`),u1=l(),Wt=n("p"),jD=r("Both are compatible with adding "),jf=n("code"),MD=r("cpu_offload"),VD=r(" to enable ZeRO-offload (activate it like this: "),Mf=n("code"),HD=r('--sharded_ddp "zero_dp_2 cpu_offload"'),BD=r(")."),f1=l(),Sl=n("p"),YD=r("Notes:"),g1=l(),he=n("ul"),Vf=n("li"),ZD=r("This feature requires distributed training (so multiple GPUs)."),JD=l(),Hf=n("li"),XD=r("It is not implemented for TPUs."),KD=l(),Ys=n("li"),QD=r("It works with "),Bf=n("code"),e7=r("--fp16"),t7=r(" too, to make things even faster."),o7=l(),Io=n("li"),r7=r("The "),Yf=n("code"),a7=r("cpu_offload"),n7=r(" additional option requires "),Zf=n("code"),s7=r("--fp16"),i7=r("."),l7=l(),Jf=n("li"),d7=r(`This is an area of active development, so make sure you have a source install of fairscale to use this feature as
some bugs you encounter may have been fixed there already.`),_1=l(),Ol=n("p"),c7=r("Known caveats:"),v1=l(),Wr=n("ul"),Uo=n("li"),p7=r("This feature is incompatible with "),Xf=n("code"),m7=r("--predict_with_generate"),h7=r(" in the "),Kf=n("em"),u7=r("run_translation.py"),f7=r(" script."),g7=l(),ke=n("li"),_7=r("Using "),Qf=n("code"),v7=r("--sharded_ddp zero_dp_3"),b7=r(` requires wrapping each layer of the model in the special container
`),eg=n("code"),y7=r("FullyShardedDataParallelism"),w7=r(" of fairscale. It should be used with the option "),tg=n("code"),E7=r("auto_wrap"),T7=r(` if you are not
doing this yourself: `),og=n("code"),$7=r('--sharded_ddp "zero_dp_3 auto_wrap"'),k7=r("."),b1=l(),Cl=n("p"),x7=r("Sections that were moved:"),y1=l(),y=n("p"),A7=r("[ "),Il=n("a"),D7=r("DeepSpeed"),rg=n("a"),q7=r(`
| `),Ul=n("a"),P7=r("Installation"),ag=n("a"),S7=r(`
| `),Nl=n("a"),O7=r("Deployment with multiple GPUs"),ng=n("a"),C7=r(`
| `),zl=n("a"),I7=r("Deployment with one GPU"),sg=n("a"),U7=r(`
| `),Ll=n("a"),N7=r("Deployment in Notebooks"),ig=n("a"),z7=r(`
| `),Fl=n("a"),L7=r("Configuration"),lg=n("a"),F7=r(`
| `),Rl=n("a"),R7=r("Passing Configuration"),dg=n("a"),W7=r(`
| `),Wl=n("a"),G7=r("Shared Configuration"),cg=n("a"),j7=r(`
| `),Gl=n("a"),M7=r("ZeRO"),pg=n("a"),V7=r(`
| `),jl=n("a"),H7=r("ZeRO-2 Config"),mg=n("a"),B7=r(`
| `),Ml=n("a"),Y7=r("ZeRO-3 Config"),hg=n("a"),Z7=r(`
| `),Vl=n("a"),J7=r("NVMe Support"),ug=n("a"),X7=r(`
| `),Hl=n("a"),K7=r("ZeRO-2 vs ZeRO-3 Performance"),fg=n("a"),Q7=r(`
| `),Bl=n("a"),eq=r("ZeRO-2 Example"),gg=n("a"),tq=r(`
| `),Yl=n("a"),oq=r("ZeRO-3 Example"),_g=n("a"),rq=r(`
| `),Zl=n("a"),aq=r("Optimizer"),vg=n("a"),nq=r(`
| `),Jl=n("a"),sq=r("Scheduler"),bg=n("a"),iq=r(`
| `),Xl=n("a"),lq=r("fp32 Precision"),yg=n("a"),dq=r(`
| `),Kl=n("a"),cq=r("Automatic Mixed Precision"),wg=n("a"),pq=r(`
| `),Ql=n("a"),mq=r("Batch Size"),Eg=n("a"),hq=r(`
| `),ed=n("a"),uq=r("Gradient Accumulation"),Tg=n("a"),fq=r(`
| `),td=n("a"),gq=r("Gradient Clipping"),$g=n("a"),_q=r(`
| `),od=n("a"),vq=r("Getting The Model Weights Out"),kg=n("a"),bq=r(`
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Trainer\u2019s init through `),Mc=s(P1,"CODE",{});var FP=i(Mc);ew=a(FP,"optimizers"),FP.forEach(o),tw=a(P1,", or subclass and override this method in a subclass."),P1.forEach(o),id.forEach(o),ow=d(w),pt=s(w,"DIV",{class:!0});var ld=i(pt);u(wa.$$.fragment,ld),rw=d(ld),Vc=s(ld,"P",{});var RP=i(Vc);aw=a(RP,"Setup the optimizer and the learning rate scheduler."),RP.forEach(o),nw=d(ld),Xe=s(ld,"P",{});var Gr=i(Xe);sw=a(Gr,`We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer\u2019s init through `),Hc=s(Gr,"CODE",{});var WP=i(Hc);iw=a(WP,"optimizers"),WP.forEach(o),lw=a(Gr,", or subclass and override this method (or "),Bc=s(Gr,"CODE",{});var GP=i(Bc);dw=a(GP,"create_optimizer"),GP.forEach(o),cw=a(Gr,` and/or
`),Yc=s(Gr,"CODE",{});var jP=i(Yc);pw=a(jP,"create_scheduler"),jP.forEach(o),mw=a(Gr,") in a subclass."),Gr.forEach(o),ld.forEach(o),hw=d(w),Ho=s(w,"DIV",{class:!0});var S1=i(Ho);u(Ea.$$.fragment,S1),uw=d(S1),Zc=s(S1,"P",{});var MP=i(Zc);fw=a(MP,`Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.`),MP.forEach(o),S1.forEach(o),gw=d(w),Pe=s(w,"DIV",{class:!0});var jr=i(Pe);u(Ta.$$.fragment,jr),_w=d(jr),Jc=s(jr,"P",{});var VP=i(Jc);vw=a(VP,"Run evaluation and returns metrics."),VP.forEach(o),bw=d(jr),$a=s(jr,"P",{});var O1=i($a);yw=a(O1,`The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `),Xc=s(O1,"CODE",{});var HP=i(Xc);ww=a(HP,"compute_metrics"),HP.forEach(o),Ew=a(O1," argument)."),O1.forEach(o),Tw=d(jr),Kc=s(jr,"P",{});var BP=i(Kc);$w=a(BP,"You can also subclass and override this method to inject custom behavior."),BP.forEach(o),jr.forEach(o),kw=d(w),mt=s(w,"DIV",{class:!0});var dd=i(mt);u(ka.$$.fragment,dd),xw=d(dd),eo=s(dd,"P",{});var cd=i(eo);Aw=a(cd,"Prediction/evaluation loop, shared by "),Qc=s(cd,"CODE",{});var YP=i(Qc);Dw=a(YP,"Trainer.evaluate()"),YP.forEach(o),qw=a(cd," and "),ep=s(cd,"CODE",{});var ZP=i(ep);Pw=a(ZP,"Trainer.predict()"),ZP.forEach(o),Sw=a(cd,"."),cd.forEach(o),Ow=d(dd),tp=s(dd,"P",{});var JP=i(tp);Cw=a(JP,"Works both with or without labels."),JP.forEach(o),dd.forEach(o),Iw=d(w),Bo=s(w,"DIV",{class:!0});var C1=i(Bo);u(xa.$$.fragment,C1),Uw=d(C1),Aa=s(C1,"P",{});var I1=i(Aa);Nw=a(I1,"For models that inherit from "),bi=s(I1,"A",{href:!0});var XP=i(bi);zw=a(XP,"PreTrainedModel"),XP.forEach(o),Lw=a(I1,`, uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
model or subclass and override this method.`),I1.forEach(o),C1.forEach(o),Fw=d(w),ht=s(w,"DIV",{class:!0});var pd=i(ht);u(Da.$$.fragment,pd),Rw=d(pd),qa=s(pd,"P",{});var U1=i(qa);Ww=a(U1,"Returns the evaluation "),op=s(U1,"CODE",{});var KP=i(op);Gw=a(KP,"DataLoader"),KP.forEach(o),jw=a(U1,"."),U1.forEach(o),Mw=d(pd),rp=s(pd,"P",{});var QP=i(rp);Vw=a(QP,"Subclass and override this method if you want to inject some custom behavior."),QP.forEach(o),pd.forEach(o),Hw=d(w),Yo=s(w,"DIV",{class:!0});var N1=i(Yo);u(Pa.$$.fragment,N1),Bw=d(N1),ap=s(N1,"P",{});var eS=i(ap);Yw=a(eS,"Returns the optimizer class and optimizer parameters based on the training arguments."),eS.forEach(o),N1.forEach(o),Zw=d(w),ut=s(w,"DIV",{class:!0});var md=i(ut);u(Sa.$$.fragment,md),Jw=d(md),Oa=s(md,"P",{});var z1=i(Oa);Xw=a(z1,"Returns the test "),np=s(z1,"CODE",{});var tS=i(np);Kw=a(tS,"DataLoader"),tS.forEach(o),Qw=a(z1,"."),z1.forEach(o),e0=d(md),sp=s(md,"P",{});var oS=i(sp);t0=a(oS,"Subclass and override this method if you want to inject some custom behavior."),oS.forEach(o),md.forEach(o),o0=d(w),Se=s(w,"DIV",{class:!0});var Mr=i(Se);u(Ca.$$.fragment,Mr),r0=d(Mr),Ia=s(Mr,"P",{});var L1=i(Ia);a0=a(L1,"Returns the training "),ip=s(L1,"CODE",{});var rS=i(ip);n0=a(rS,"DataLoader"),rS.forEach(o),s0=a(L1,"."),L1.forEach(o),i0=d(Mr),to=s(Mr,"P",{});var hd=i(to);l0=a(hd,"Will use no sampler if "),lp=s(hd,"CODE",{});var aS=i(lp);d0=a(aS,"self.train_dataset"),aS.forEach(o),c0=a(hd," does not implement "),dp=s(hd,"CODE",{});var nS=i(dp);p0=a(nS,"__len__"),nS.forEach(o),m0=a(hd,`, a random sampler (adapted to
distributed training if necessary) otherwise.`),hd.forEach(o),h0=d(Mr),cp=s(Mr,"P",{});var sS=i(cp);u0=a(sS,"Subclass and override this method if you want to inject some custom behavior."),sS.forEach(o),Mr.forEach(o),f0=d(w),ft=s(w,"DIV",{class:!0});var ud=i(ft);u(Ua.$$.fragment,ud),g0=d(ud),Ee=s(ud,"P",{});var jt=i(Ee);_0=a(jt,"Launch an hyperparameter search using "),pp=s(jt,"CODE",{});var iS=i(pp);v0=a(iS,"optuna"),iS.forEach(o),b0=a(jt," or "),mp=s(jt,"CODE",{});var lS=i(mp);y0=a(lS,"Ray Tune"),lS.forEach(o),w0=a(jt," or "),hp=s(jt,"CODE",{});var dS=i(hp);E0=a(dS,"SigOpt"),dS.forEach(o),T0=a(jt,`. The optimized quantity is determined
by `),up=s(jt,"CODE",{});var cS=i(up);$0=a(cS,"compute_objective"),cS.forEach(o),k0=a(jt,`, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise.`),jt.forEach(o),x0=d(ud),u(Zo.$$.fragment,ud),ud.forEach(o),A0=d(w),Jo=s(w,"DIV",{class:!0});var F1=i(Jo);u(Na.$$.fragment,F1),D0=d(F1),za=s(F1,"P",{});var R1=i(za);q0=a(R1,"Initializes a git repo in "),fp=s(R1,"CODE",{});var pS=i(fp);P0=a(pS,"self.args.hub_model_id"),pS.forEach(o),S0=a(R1,"."),R1.forEach(o),F1.forEach(o),O0=d(w),Xo=s(w,"DIV",{class:!0});var W1=i(Xo);u(La.$$.fragment,W1),C0=d(W1),gp=s(W1,"P",{});var mS=i(gp);I0=a(mS,`Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.`),mS.forEach(o),W1.forEach(o),U0=d(w),Ko=s(w,"DIV",{class:!0});var G1=i(Ko);u(Fa.$$.fragment,G1),N0=d(G1),Ra=s(G1,"P",{});var j1=i(Ra);z0=a(j1,`Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be `),_p=s(j1,"CODE",{});var hS=i(_p);L0=a(hS,"True"),hS.forEach(o),F0=a(j1," for one process)."),j1.forEach(o),G1.forEach(o),R0=d(w),gt=s(w,"DIV",{class:!0});var fd=i(gt);u(Wa.$$.fragment,fd),W0=d(fd),Ga=s(fd,"P",{});var M1=i(Ga);G0=a(M1,"Log "),vp=s(M1,"CODE",{});var uS=i(vp);j0=a(uS,"logs"),uS.forEach(o),M0=a(M1," on the various objects watching training."),M1.forEach(o),V0=d(fd),bp=s(fd,"P",{});var fS=i(bp);H0=a(fS,"Subclass and override this method to inject custom behavior."),fS.forEach(o),fd.forEach(o),B0=d(w),k=s(w,"DIV",{class:!0});var A=i(k);u(ja.$$.fragment,A),Y0=d(A),yp=s(A,"P",{});var gS=i(yp);Z0=a(gS,"Log metrics in a specially formatted way"),gS.forEach(o),J0=d(A),wp=s(A,"P",{});var _S=i(wp);X0=a(_S,"Under distributed environment this is done only for a process with rank 0."),_S.forEach(o),K0=d(A),Ep=s(A,"P",{});var vS=i(Ep);Q0=a(vS,"Notes on memory reports:"),vS.forEach(o),eE=d(A),oo=s(A,"P",{});var gd=i(oo);tE=a(gd,"In order to get memory usage report you need to install "),Tp=s(gd,"CODE",{});var bS=i(Tp);oE=a(bS,"psutil"),bS.forEach(o),rE=a(gd,". You can do that with "),$p=s(gd,"CODE",{});var yS=i($p);aE=a(yS,"pip install psutil"),yS.forEach(o),nE=a(gd,"."),gd.forEach(o),sE=d(A),kp=s(A,"P",{});var wS=i(kp);iE=a(wS,"Now when this method is run, you will see a report that will include: :"),wS.forEach(o),lE=d(A),u(Ma.$$.fragment,A),dE=d(A),xp=s(A,"P",{});var ES=i(xp);Ap=s(ES,"STRONG",{});var TS=i(Ap);cE=a(TS,"Understanding the reports:"),TS.forEach(o),ES.forEach(o),pE=d(A),Ke=s(A,"UL",{});var Vr=i(Ke);Te=s(Vr,"LI",{});var Mt=i(Te);mE=a(Mt,"the first segment, e.g., "),Dp=s(Mt,"CODE",{});var $S=i(Dp);hE=a($S,"train__"),$S.forEach(o),uE=a(Mt,", tells you which stage the metrics are for. Reports starting with "),qp=s(Mt,"CODE",{});var kS=i(qp);fE=a(kS,"init_"),kS.forEach(o),gE=a(Mt,`
will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the
`),Pp=s(Mt,"CODE",{});var xS=i(Pp);_E=a(xS,"__init__"),xS.forEach(o),vE=a(Mt," will be reported along with the "),Sp=s(Mt,"CODE",{});var AS=i(Sp);bE=a(AS,"eval_"),AS.forEach(o),yE=a(Mt," metrics."),Mt.forEach(o),wE=d(Vr),ro=s(Vr,"LI",{});var _d=i(ro);EE=a(_d,"the third segment, is either "),Op=s(_d,"CODE",{});var DS=i(Op);TE=a(DS,"cpu"),DS.forEach(o),$E=a(_d," or "),Cp=s(_d,"CODE",{});var qS=i(Cp);kE=a(qS,"gpu"),qS.forEach(o),xE=a(_d,`, tells you whether it\u2019s the general RAM or the gpu0 memory
metric.`),_d.forEach(o),AE=d(Vr),yi=s(Vr,"LI",{});var Oq=i(yi);Ip=s(Oq,"CODE",{});var PS=i(Ip);DE=a(PS,"*_alloc_delta"),PS.forEach(o),qE=a(Oq,` - is the difference in the used/allocated memory counter between the end and the start of the
stage - it can be negative if a function released more memory than it allocated.`),Oq.forEach(o),PE=d(Vr),_t=s(Vr,"LI",{});var Xs=i(_t);Up=s(Xs,"CODE",{});var SS=i(Up);SE=a(SS,"*_peaked_delta"),SS.forEach(o),OE=a(Xs,` - is any extra memory that was consumed and then freed - relative to the current allocated
memory counter - it is never negative. When you look at the metrics of any stage you add up `),Np=s(Xs,"CODE",{});var OS=i(Np);CE=a(OS,"alloc_delta"),OS.forEach(o),IE=a(Xs,` +
`),zp=s(Xs,"CODE",{});var CS=i(zp);UE=a(CS,"peaked_delta"),CS.forEach(o),NE=a(Xs," and you know how much memory was needed to complete that stage."),Xs.forEach(o),Vr.forEach(o),zE=d(A),Lp=s(A,"P",{});var IS=i(Lp);LE=a(IS,`The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the
main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may
use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more
memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the
future these reports will evolve to measure those too.`),IS.forEach(o),FE=d(A),Fp=s(A,"P",{});var US=i(Fp);RE=a(US,`The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the
memory shared with other processes. It is important to note that it does not include swapped out memory, so the
reports could be imprecise.`),US.forEach(o),WE=d(A),Va=s(A,"P",{});var V1=i(Va);GE=a(V1,`The CPU peak memory is measured using a sampling thread. Due to python\u2019s GIL it may miss some of the peak memory if
that thread didn\u2019t get a chance to run when the highest memory was used. Therefore this report can be less than
reality. Using `),Rp=s(V1,"CODE",{});var NS=i(Rp);jE=a(NS,"tracemalloc"),NS.forEach(o),ME=a(V1,` would have reported the exact peak memory, but it doesn\u2019t report memory allocations
outside of python. So if some C++ CUDA extension allocated its own memory it won\u2019t be reported. And therefore it
was dropped in favor of the memory sampling approach, which reads the current process memory usage.`),V1.forEach(o),VE=d(A),Qe=s(A,"P",{});var Hr=i(Qe);HE=a(Hr,"The GPU allocated and peak memory reporting is done with "),Wp=s(Hr,"CODE",{});var zS=i(Wp);BE=a(zS,"torch.cuda.memory_allocated()"),zS.forEach(o),YE=a(Hr,` and
`),Gp=s(Hr,"CODE",{});var LS=i(Gp);ZE=a(LS,"torch.cuda.max_memory_allocated()"),LS.forEach(o),JE=a(Hr,`. This metric reports only \u201Cdeltas\u201D for pytorch-specific allocations, as
`),jp=s(Hr,"CODE",{});var FS=i(jp);XE=a(FS,"torch.cuda"),FS.forEach(o),KE=a(Hr,` memory management system doesn\u2019t track any memory allocated outside of pytorch. For example, the very
first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.`),Hr.forEach(o),QE=d(A),re=s(A,"P",{});var je=i(re);e4=a(je,"Note that this tracker doesn\u2019t account for memory allocations outside of "),wi=s(je,"A",{href:!0});var RS=i(wi);t4=a(RS,"Trainer"),RS.forEach(o),o4=a(je,"\u2019s "),Mp=s(je,"CODE",{});var WS=i(Mp);r4=a(WS,"__init__"),WS.forEach(o),a4=a(je,", "),Vp=s(je,"CODE",{});var GS=i(Vp);n4=a(GS,"train"),GS.forEach(o),s4=a(je,`,
`),Hp=s(je,"CODE",{});var jS=i(Hp);i4=a(jS,"evaluate"),jS.forEach(o),l4=a(je," and "),Bp=s(je,"CODE",{});var MS=i(Bp);d4=a(MS,"predict"),MS.forEach(o),c4=a(je," calls."),je.forEach(o),p4=d(A),I=s(A,"P",{});var F=i(I);m4=a(F,"Because "),Yp=s(F,"CODE",{});var VS=i(Yp);h4=a(VS,"evaluation"),VS.forEach(o),u4=a(F," calls may happen during "),Zp=s(F,"CODE",{});var HS=i(Zp);f4=a(HS,"train"),HS.forEach(o),g4=a(F,`, we can\u2019t handle nested invocations because
`),Jp=s(F,"CODE",{});var BS=i(Jp);_4=a(BS,"torch.cuda.max_memory_allocated"),BS.forEach(o),v4=a(F," is a single counter, so if it gets reset by a nested eval call, "),Xp=s(F,"CODE",{});var YS=i(Xp);b4=a(YS,"train"),YS.forEach(o),y4=a(F,`\u2019s tracker
will report incorrect info. If this `),Ha=s(F,"A",{href:!0,rel:!0});var ZS=i(Ha);w4=a(ZS,"pytorch issue"),ZS.forEach(o),E4=a(F,` gets resolved
it will be possible to change this class to be re-entrant. Until then we will only track the outer level of
`),Kp=s(F,"CODE",{});var JS=i(Kp);T4=a(JS,"train"),JS.forEach(o),$4=a(F,", "),Qp=s(F,"CODE",{});var XS=i(Qp);k4=a(XS,"evaluate"),XS.forEach(o),x4=a(F," and "),em=s(F,"CODE",{});var KS=i(em);A4=a(KS,"predict"),KS.forEach(o),D4=a(F," methods. Which means that if "),tm=s(F,"CODE",{});var QS=i(tm);q4=a(QS,"eval"),QS.forEach(o),P4=a(F," is called during "),om=s(F,"CODE",{});var eO=i(om);S4=a(eO,"train"),eO.forEach(o),O4=a(F,`, it\u2019s the latter
that will account for its memory usage and that of the former.`),F.forEach(o),C4=d(A),$e=s(A,"P",{});var Vt=i($e);I4=a(Vt,"This also means that if any other tool that is used along the "),Ei=s(Vt,"A",{href:!0});var tO=i(Ei);U4=a(tO,"Trainer"),tO.forEach(o),N4=a(Vt,` calls
`),rm=s(Vt,"CODE",{});var oO=i(rm);z4=a(oO,"torch.cuda.reset_peak_memory_stats"),oO.forEach(o),L4=a(Vt,", the gpu peak memory stats could be invalid. And the "),Ti=s(Vt,"A",{href:!0});var rO=i(Ti);F4=a(rO,"Trainer"),rO.forEach(o),R4=a(Vt,` will disrupt
the normal behavior of any such tools that rely on calling `),am=s(Vt,"CODE",{});var aO=i(am);W4=a(aO,"torch.cuda.reset_peak_memory_stats"),aO.forEach(o),G4=a(Vt," themselves."),Vt.forEach(o),j4=d(A),nm=s(A,"P",{});var nO=i(nm);M4=a(nO,"For best performance you may want to consider turning the memory profiling off for production runs."),nO.forEach(o),A.forEach(o),V4=d(w),Qo=s(w,"DIV",{class:!0});var H1=i(Qo);u(Ba.$$.fragment,H1),H4=d(H1),sm=s(H1,"P",{});var sO=i(sm);B4=a(sO,"Reformat Trainer metrics values to a human-readable format"),sO.forEach(o),H1.forEach(o),Y4=d(w),vt=s(w,"DIV",{class:!0});var vd=i(vt);u(Ya.$$.fragment,vd),Z4=d(vd),Za=s(vd,"P",{});var B1=i(Za);J4=a(B1,"Helper to get number of samples in a "),im=s(B1,"CODE",{});var iO=i(im);X4=a(iO,"DataLoader"),iO.forEach(o),K4=a(B1," by accessing its dataset."),B1.forEach(o),Q4=d(vd),$i=s(vd,"P",{});var Cq=i($i);eT=a(Cq,"Will raise an exception if the underlying dataset does not implement method "),lm=s(Cq,"CODE",{});var lO=i(lm);tT=a(lO,"__len__"),lO.forEach(o),Cq.forEach(o),vd.forEach(o),oT=d(w),bt=s(w,"DIV",{class:!0});var bd=i(bt);u(Ja.$$.fragment,bd),rT=d(bd),Xa=s(bd,"P",{});var Y1=i(Xa);aT=a(Y1,"Remove a callback from the current list of "),dm=s(Y1,"CODE",{});var dO=i(dm);nT=a(dO,"TrainerCallback"),dO.forEach(o),sT=a(Y1," and returns it."),Y1.forEach(o),iT=d(bd),Ka=s(bd,"P",{});var Z1=i(Ka);lT=a(Z1,"If the callback is not found, returns "),cm=s(Z1,"CODE",{});var cO=i(cm);dT=a(cO,"None"),cO.forEach(o),cT=a(Z1," (and no error is raised)."),Z1.forEach(o),bd.forEach(o),pT=d(w),X=s(w,"DIV",{class:!0});var Me=i(X);u(Qa.$$.fragment,Me),mT=d(Me),pm=s(Me,"P",{});var pO=i(pm);hT=a(pO,"Run prediction and returns predictions and potential metrics."),pO.forEach(o),uT=d(Me),en=s(Me,"P",{});var J1=i(en);fT=a(J1,`Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `),mm=s(J1,"CODE",{});var mO=i(mm);gT=a(mO,"evaluate()"),mO.forEach(o),_T=a(J1,"."),J1.forEach(o),vT=d(Me),u(er.$$.fragment,Me),bT=d(Me),tn=s(Me,"P",{});var X1=i(tn);yT=a(X1,"Returns: "),hm=s(X1,"EM",{});var hO=i(hm);wT=a(hO,"NamedTuple"),hO.forEach(o),ET=a(X1," A namedtuple with the following keys:"),X1.forEach(o),TT=d(Me),ao=s(Me,"UL",{});var yd=i(ao);no=s(yd,"LI",{});var wd=i(no);$T=a(wd,"predictions ("),um=s(wd,"CODE",{});var uO=i(um);kT=a(uO,"np.ndarray"),uO.forEach(o),xT=a(wd,"): The predictions on "),fm=s(wd,"CODE",{});var fO=i(fm);AT=a(fO,"test_dataset"),fO.forEach(o),DT=a(wd,"."),wd.forEach(o),qT=d(yd),so=s(yd,"LI",{});var Ed=i(so);PT=a(Ed,"label_ids ("),gm=s(Ed,"CODE",{});var gO=i(gm);ST=a(gO,"np.ndarray"),gO.forEach(o),OT=a(Ed,", "),_m=s(Ed,"EM",{});var _O=i(_m);CT=a(_O,"optional"),_O.forEach(o),IT=a(Ed,"): The labels (if the dataset contained some)."),Ed.forEach(o),UT=d(yd),io=s(yd,"LI",{});var Td=i(io);NT=a(Td,"metrics ("),vm=s(Td,"CODE",{});var vO=i(vm);zT=a(vO,"Dict[str, float]"),vO.forEach(o),LT=a(Td,", "),bm=s(Td,"EM",{});var bO=i(bm);FT=a(bO,"optional"),bO.forEach(o),RT=a(Td,`): The potential dictionary of metrics (if the dataset contained
labels).`),Td.forEach(o),yd.forEach(o),Me.forEach(o),WT=d(w),yt=s(w,"DIV",{class:!0});var $d=i(yt);u(on.$$.fragment,$d),GT=d($d),lo=s($d,"P",{});var kd=i(lo);jT=a(kd,"Prediction/evaluation loop, shared by "),ym=s(kd,"CODE",{});var yO=i(ym);MT=a(yO,"Trainer.evaluate()"),yO.forEach(o),VT=a(kd," and "),wm=s(kd,"CODE",{});var wO=i(wm);HT=a(wO,"Trainer.predict()"),wO.forEach(o),BT=a(kd,"."),kd.forEach(o),YT=d($d),Em=s($d,"P",{});var EO=i(Em);ZT=a(EO,"Works both with or without labels."),EO.forEach(o),$d.forEach(o),JT=d(w),wt=s(w,"DIV",{class:!0});var xd=i(wt);u(rn.$$.fragment,xd),XT=d(xd),co=s(xd,"P",{});var Ad=i(co);KT=a(Ad,"Perform an evaluation step on "),Tm=s(Ad,"CODE",{});var TO=i(Tm);QT=a(TO,"model"),TO.forEach(o),e3=a(Ad," using "),$m=s(Ad,"CODE",{});var $O=i($m);t3=a($O,"inputs"),$O.forEach(o),o3=a(Ad,"."),Ad.forEach(o),r3=d(xd),km=s(xd,"P",{});var kO=i(km);a3=a(kO,"Subclass and override to inject custom behavior."),kO.forEach(o),xd.forEach(o),n3=d(w),tr=s(w,"DIV",{class:!0});var K1=i(tr);u(an.$$.fragment,K1),s3=d(K1),et=s(K1,"P",{});var Br=i(et);i3=a(Br,"Upload "),xm=s(Br,"EM",{});var xO=i(xm);l3=a(xO,"self.model"),xO.forEach(o),d3=a(Br," and "),Am=s(Br,"EM",{});var AO=i(Am);c3=a(AO,"self.tokenizer"),AO.forEach(o),p3=a(Br," to the \u{1F917} model hub on the repo "),Dm=s(Br,"EM",{});var DO=i(Dm);m3=a(DO,"self.args.hub_model_id"),DO.forEach(o),h3=a(Br,"."),Br.forEach(o),K1.forEach(o),u3=d(w),or=s(w,"DIV",{class:!0});var Q1=i(or);u(nn.$$.fragment,Q1),f3=d(Q1),sn=s(Q1,"P",{});var eb=i(sn);g3=a(eb,"Remove a callback from the current list of "),qm=s(eb,"CODE",{});var qO=i(qm);_3=a(qO,"TrainerCallback"),qO.forEach(o),v3=a(eb,"."),eb.forEach(o),Q1.forEach(o),b3=d(w),Oe=s(w,"DIV",{class:!0});var Yr=i(Oe);u(ln.$$.fragment,Yr),y3=d(Yr),dn=s(Yr,"P",{});var tb=i(dn);w3=a(tb,"Save metrics into a json file for that split, e.g. "),Pm=s(tb,"CODE",{});var PO=i(Pm);E3=a(PO,"train_results.json"),PO.forEach(o),T3=a(tb,"."),tb.forEach(o),$3=d(Yr),Sm=s(Yr,"P",{});var SO=i(Sm);k3=a(SO,"Under distributed environment this is done only for a process with rank 0."),SO.forEach(o),x3=d(Yr),cn=s(Yr,"P",{});var ob=i(cn);A3=a(ob,"To understand the metrics please read the docstring of "),Om=s(ob,"CODE",{});var OO=i(Om);D3=a(OO,"log_metrics()"),OO.forEach(o),q3=a(ob,` The only difference is that raw
unformatted numbers are saved in the current method.`),ob.forEach(o),Yr.forEach(o),P3=d(w),Et=s(w,"DIV",{class:!0});var Dd=i(Et);u(pn.$$.fragment,Dd),S3=d(Dd),mn=s(Dd,"P",{});var rb=i(mn);O3=a(rb,"Will save the model, so you can reload it using "),Cm=s(rb,"CODE",{});var CO=i(Cm);C3=a(CO,"from_pretrained()"),CO.forEach(o),I3=a(rb,"."),rb.forEach(o),U3=d(Dd),Im=s(Dd,"P",{});var IO=i(Im);N3=a(IO,"Will only save from the main process."),IO.forEach(o),Dd.forEach(o),z3=d(w),Tt=s(w,"DIV",{class:!0});var qd=i(Tt);u(hn.$$.fragment,qd),L3=d(qd),Um=s(qd,"P",{});var UO=i(Um);F3=a(UO,"Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model"),UO.forEach(o),R3=d(qd),Nm=s(qd,"P",{});var NO=i(Nm);W3=a(NO,"Under distributed environment this is done only for a process with rank 0."),NO.forEach(o),qd.forEach(o),G3=d(w),rr=s(w,"DIV",{class:!0});var ab=i(rr);u(un.$$.fragment,ab),j3=d(ab),zm=s(ab,"P",{});var zO=i(zm);M3=a(zO,"Main training entry point."),zO.forEach(o),ab.forEach(o),V3=d(w),$t=s(w,"DIV",{class:!0});var Pd=i($t);u(fn.$$.fragment,Pd),H3=d(Pd),Lm=s(Pd,"P",{});var LO=i(Lm);B3=a(LO,"Perform a training step on a batch of inputs."),LO.forEach(o),Y3=d(Pd),Fm=s(Pd,"P",{});var FO=i(Fm);Z3=a(FO,"Subclass and override to inject custom behavior."),FO.forEach(o),Pd.forEach(o),w.forEach(o),Lg=d(t),po=s(t,"H2",{class:!0});var nb=i(po);ar=s(nb,"A",{id:!0,class:!0,href:!0});var RO=i(ar);Rm=s(RO,"SPAN",{});var WO=i(Rm);u(gn.$$.fragment,WO),WO.forEach(o),RO.forEach(o),J3=d(nb),Wm=s(nb,"SPAN",{});var GO=i(Wm);X3=a(GO,"Seq2SeqTrainer"),GO.forEach(o),nb.forEach(o),Fg=d(t),tt=s(t,"DIV",{class:!0});var Sd=i(tt);u(_n.$$.fragment,Sd),K3=d(Sd),Ce=s(Sd,"DIV",{class:!0});var Zr=i(Ce);u(vn.$$.fragment,Zr),Q3=d(Zr),Gm=s(Zr,"P",{});var jO=i(Gm);e6=a(jO,"Run evaluation and returns metrics."),jO.forEach(o),t6=d(Zr),bn=s(Zr,"P",{});var sb=i(bn);o6=a(sb,`The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `),jm=s(sb,"CODE",{});var MO=i(jm);r6=a(MO,"compute_metrics"),MO.forEach(o),a6=a(sb," argument)."),sb.forEach(o),n6=d(Zr),Mm=s(Zr,"P",{});var VO=i(Mm);s6=a(VO,"You can also subclass and override this method to inject custom behavior."),VO.forEach(o),Zr.forEach(o),i6=d(Sd),K=s(Sd,"DIV",{class:!0});var Ve=i(K);u(yn.$$.fragment,Ve),l6=d(Ve),Vm=s(Ve,"P",{});var HO=i(Vm);d6=a(HO,"Run prediction and returns predictions and potential metrics."),HO.forEach(o),c6=d(Ve),wn=s(Ve,"P",{});var ib=i(wn);p6=a(ib,`Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `),Hm=s(ib,"CODE",{});var BO=i(Hm);m6=a(BO,"evaluate()"),BO.forEach(o),h6=a(ib,"."),ib.forEach(o),u6=d(Ve),u(nr.$$.fragment,Ve),f6=d(Ve),En=s(Ve,"P",{});var lb=i(En);g6=a(lb,"Returns: "),Bm=s(lb,"EM",{});var YO=i(Bm);_6=a(YO,"NamedTuple"),YO.forEach(o),v6=a(lb," A namedtuple with the following keys:"),lb.forEach(o),b6=d(Ve),mo=s(Ve,"UL",{});var Od=i(mo);ho=s(Od,"LI",{});var Cd=i(ho);y6=a(Cd,"predictions ("),Ym=s(Cd,"CODE",{});var ZO=i(Ym);w6=a(ZO,"np.ndarray"),ZO.forEach(o),E6=a(Cd,"): The predictions on "),Zm=s(Cd,"CODE",{});var JO=i(Zm);T6=a(JO,"test_dataset"),JO.forEach(o),$6=a(Cd,"."),Cd.forEach(o),k6=d(Od),uo=s(Od,"LI",{});var Id=i(uo);x6=a(Id,"label_ids ("),Jm=s(Id,"CODE",{});var XO=i(Jm);A6=a(XO,"np.ndarray"),XO.forEach(o),D6=a(Id,", "),Xm=s(Id,"EM",{});var KO=i(Xm);q6=a(KO,"optional"),KO.forEach(o),P6=a(Id,"): The labels (if the dataset contained some)."),Id.forEach(o),S6=d(Od),fo=s(Od,"LI",{});var Ud=i(fo);O6=a(Ud,"metrics ("),Km=s(Ud,"CODE",{});var QO=i(Km);C6=a(QO,"Dict[str, float]"),QO.forEach(o),I6=a(Ud,", "),Qm=s(Ud,"EM",{});var eC=i(Qm);U6=a(eC,"optional"),eC.forEach(o),N6=a(Ud,`): The potential dictionary of metrics (if the dataset contained
labels).`),Ud.forEach(o),Od.forEach(o),Ve.forEach(o),Sd.forEach(o),Rg=d(t),go=s(t,"H2",{class:!0});var db=i(go);sr=s(db,"A",{id:!0,class:!0,href:!0});var tC=i(sr);eh=s(tC,"SPAN",{});var oC=i(eh);u(Tn.$$.fragment,oC),oC.forEach(o),tC.forEach(o),z6=d(db),th=s(db,"SPAN",{});var rC=i(th);L6=a(rC,"TrainingArguments"),rC.forEach(o),db.forEach(o),Wg=d(t),z=s(t,"DIV",{class:!0});var M=i(z);u($n.$$.fragment,M),F6=d(M),kn=s(M,"P",{});var cb=i(kn);R6=a(cb,"TrainingArguments is the subset of the arguments we use in our example scripts "),oh=s(cb,"STRONG",{});var aC=i(oh);W6=a(aC,`which relate to the training loop
itself`),aC.forEach(o),G6=a(cb,"."),cb.forEach(o),j6=d(M),_o=s(M,"P",{});var Nd=i(_o);M6=a(Nd,"Using "),ki=s(Nd,"A",{href:!0});var nC=i(ki);V6=a(nC,"HfArgumentParser"),nC.forEach(o),H6=a(Nd,` we can turn this class into
`),xn=s(Nd,"A",{href:!0,rel:!0});var sC=i(xn);B6=a(sC,"argparse"),sC.forEach(o),Y6=a(Nd,` arguments that can be specified on the
command line.`),Nd.forEach(o),Z6=d(M),ce=s(M,"DIV",{class:!0});var Ht=i(ce);u(An.$$.fragment,Ht),J6=d(Ht),rh=s(Ht,"P",{});var iC=i(rh);X6=a(iC,`Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.`),iC.forEach(o),K6=d(Ht),vo=s(Ht,"P",{});var zd=i(vo);Q6=a(zd,"For the main process the log level defaults to "),ah=s(zd,"CODE",{});var lC=i(ah);e$=a(lC,"logging.INFO"),lC.forEach(o),t$=a(zd," unless overridden by "),nh=s(zd,"CODE",{});var dC=i(nh);o$=a(dC,"log_level"),dC.forEach(o),r$=a(zd," argument."),zd.forEach(o),a$=d(Ht),bo=s(Ht,"P",{});var Ld=i(bo);n$=a(Ld,"For the replica processes the log level defaults to "),sh=s(Ld,"CODE",{});var cC=i(sh);s$=a(cC,"logging.WARNING"),cC.forEach(o),i$=a(Ld," unless overridden by "),ih=s(Ld,"CODE",{});var pC=i(ih);l$=a(pC,"log_level_replica"),pC.forEach(o),d$=a(Ld,`
argument.`),Ld.forEach(o),c$=d(Ht),Dn=s(Ht,"P",{});var pb=i(Dn);p$=a(pb,"The choice between the main and replica process settings is made according to the return value of "),lh=s(pb,"CODE",{});var mC=i(lh);m$=a(mC,"should_log"),mC.forEach(o),h$=a(pb,"."),pb.forEach(o),Ht.forEach(o),u$=d(M),ir=s(M,"DIV",{class:!0});var mb=i(ir);u(qn.$$.fragment,mb),f$=d(mb),dh=s(mb,"P",{});var hC=i(dh);g$=a(hC,"Get number of steps used for a linear warmup."),hC.forEach(o),mb.forEach(o),_$=d(M),kt=s(M,"DIV",{class:!0});var Fd=i(kt);u(Pn.$$.fragment,Fd),v$=d(Fd),ch=s(Fd,"P",{});var uC=i(ch);b$=a(uC,`A context manager for torch distributed environment where on needs to do something on the main process, while
blocking replicas, and when it\u2019s finished releasing the replicas.`),uC.forEach(o),y$=d(Fd),yo=s(Fd,"P",{});var Rd=i(yo);w$=a(Rd,"One such use is for "),ph=s(Rd,"CODE",{});var fC=i(ph);E$=a(fC,"datasets"),fC.forEach(o),T$=a(Rd,"\u2019s "),mh=s(Rd,"CODE",{});var gC=i(mh);$$=a(gC,"map"),gC.forEach(o),k$=a(Rd,` feature which to be efficient should be run once on the main process,
which upon completion saves a cached version of results and which then automatically gets loaded by the
replicas.`),Rd.forEach(o),Fd.forEach(o),x$=d(M),lr=s(M,"DIV",{class:!0});var hb=i(lr);u(Sn.$$.fragment,hb),A$=d(hb),On=s(hb,"P",{});var ub=i(On);D$=a(ub,"Serializes this instance while replace "),hh=s(ub,"CODE",{});var _C=i(hh);q$=a(_C,"Enum"),_C.forEach(o),P$=a(ub,` by their values (for JSON serialization support). It obfuscates
the token values by removing their value.`),ub.forEach(o),hb.forEach(o),S$=d(M),dr=s(M,"DIV",{class:!0});var fb=i(dr);u(Cn.$$.fragment,fb),O$=d(fb),uh=s(fb,"P",{});var vC=i(uh);C$=a(vC,"Serializes this instance to a JSON string."),vC.forEach(o),fb.forEach(o),I$=d(M),cr=s(M,"DIV",{class:!0});var gb=i(cr);u(In.$$.fragment,gb),U$=d(gb),fh=s(gb,"P",{});var bC=i(fh);N$=a(bC,"Sanitized serialization to use with TensorBoard\u2019s hparams"),bC.forEach(o),gb.forEach(o),M.forEach(o),Gg=d(t),wo=s(t,"H2",{class:!0});var _b=i(wo);pr=s(_b,"A",{id:!0,class:!0,href:!0});var yC=i(pr);gh=s(yC,"SPAN",{});var wC=i(gh);u(Un.$$.fragment,wC),wC.forEach(o),yC.forEach(o),z$=d(_b),_h=s(_b,"SPAN",{});var EC=i(_h);L$=a(EC,"Seq2SeqTrainingArguments"),EC.forEach(o),_b.forEach(o),jg=d(t),ae=s(t,"DIV",{class:!0});var Bt=i(ae);u(Nn.$$.fragment,Bt),F$=d(Bt),zn=s(Bt,"P",{});var vb=i(zn);R$=a(vb,"TrainingArguments is the subset of the arguments we use in our example scripts "),vh=s(vb,"STRONG",{});var TC=i(vh);W$=a(TC,`which relate to the training loop
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command line.`),Wd.forEach(o),Z$=d(Bt),ne=s(Bt,"P",{});var He=i(ne);J$=a(He,"sortish_sampler ("),bh=s(He,"CODE",{});var xC=i(bh);X$=a(xC,"bool"),xC.forEach(o),K$=a(He,", "),yh=s(He,"EM",{});var AC=i(yh);Q$=a(AC,"optional"),AC.forEach(o),ek=a(He,", defaults to "),wh=s(He,"CODE",{});var DC=i(wh);tk=a(DC,"False"),DC.forEach(o),ok=a(He,`):
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predict_with_generate (`),$h=s(O,"CODE",{});var SC=i($h);dk=a(SC,"bool"),SC.forEach(o),ck=a(O,", "),kh=s(O,"EM",{});var OC=i(kh);pk=a(OC,"optional"),OC.forEach(o),mk=a(O,", defaults to "),xh=s(O,"CODE",{});var CC=i(xh);hk=a(CC,"False"),CC.forEach(o),uk=a(O,`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
generation_max_length (`),Ah=s(O,"CODE",{});var IC=i(Ah);fk=a(IC,"int"),IC.forEach(o),gk=a(O,", "),Dh=s(O,"EM",{});var UC=i(Dh);_k=a(UC,"optional"),UC.forEach(o),vk=a(O,`):
The `),qh=s(O,"CODE",{});var NC=i(qh);bk=a(NC,"max_length"),NC.forEach(o),yk=a(O," to use on each evaluation loop when "),Ph=s(O,"CODE",{});var zC=i(Ph);wk=a(zC,"predict_with_generate=True"),zC.forEach(o),Ek=a(O,`. Will default to the
`),Sh=s(O,"CODE",{});var LC=i(Sh);Tk=a(LC,"max_length"),LC.forEach(o),$k=a(O,` value of the model configuration.
generation_num_beams (`),Oh=s(O,"CODE",{});var FC=i(Oh);kk=a(FC,"int"),FC.forEach(o),xk=a(O,", "),Ch=s(O,"EM",{});var RC=i(Ch);Ak=a(RC,"optional"),RC.forEach(o),Dk=a(O,`):
The `),Ih=s(O,"CODE",{});var WC=i(Ih);qk=a(WC,"num_beams"),WC.forEach(o),Pk=a(O," to use on each evaluation loop when "),Uh=s(O,"CODE",{});var GC=i(Uh);Sk=a(GC,"predict_with_generate=True"),GC.forEach(o),Ok=a(O,`. Will default to the
`),Nh=s(O,"CODE",{});var jC=i(Nh);Ck=a(jC,"num_beams"),jC.forEach(o),Ik=a(O," value of the model configuration."),O.forEach(o),Bt.forEach(o),Mg=d(t),To=s(t,"H2",{class:!0});var bb=i(To);mr=s(bb,"A",{id:!0,class:!0,href:!0});var MC=i(mr);zh=s(MC,"SPAN",{});var VC=i(zh);u(Fn.$$.fragment,VC),VC.forEach(o),MC.forEach(o),Uk=d(bb),Lh=s(bb,"SPAN",{});var HC=i(Lh);Nk=a(HC,"Checkpoints"),HC.forEach(o),bb.forEach(o),Vg=d(t),pe=s(t,"P",{});var Yt=i(pe);zk=a(Yt,"By default, "),Ai=s(Yt,"A",{href:!0});var BC=i(Ai);Lk=a(BC,"Trainer"),BC.forEach(o),Fk=a(Yt," will save all checkpoints in the "),Fh=s(Yt,"CODE",{});var YC=i(Fh);Rk=a(YC,"output_dir"),YC.forEach(o),Wk=a(Yt,` you set in the
`),Di=s(Yt,"A",{href:!0});var ZC=i(Di);Gk=a(ZC,"TrainingArguments"),ZC.forEach(o),jk=a(Yt," you are using. Those will go in subfolder named "),Rh=s(Yt,"CODE",{});var JC=i(Rh);Mk=a(JC,"checkpoint-xxx"),JC.forEach(o),Vk=a(Yt,` with xxx
being the step at which the training was at.`),Yt.forEach(o),Hg=d(t),hr=s(t,"P",{});var yb=i(hr);Hk=a(yb,"Resuming training from a checkpoint can be done when calling "),qi=s(yb,"A",{href:!0});var XC=i(qi);Bk=a(XC,"Trainer.train()"),XC.forEach(o),Yk=a(yb," with either:"),yb.forEach(o),Bg=d(t),ur=s(t,"UL",{});var wb=i(ur);Pi=s(wb,"LI",{});var Iq=i(Pi);Wh=s(Iq,"CODE",{});var KC=i(Wh);Zk=a(KC,"resume_from_checkpoint=True"),KC.forEach(o),Jk=a(Iq," which will resume training from the latest checkpoint"),Iq.forEach(o),Xk=d(wb),Si=s(wb,"LI",{});var Uq=i(Si);Gh=s(Uq,"CODE",{});var QC=i(Gh);Kk=a(QC,"resume_from_checkpoint=checkpoint_dir"),QC.forEach(o),Qk=a(Uq,` which will resume training from the specific checkpoint in the directory
passed.`),Uq.forEach(o),wb.forEach(o),Yg=d(t),Ie=s(t,"P",{});var Jr=i(Ie);e5=a(Jr,"In addition, you can easily save your checkpoints on the Model Hub when using "),jh=s(Jr,"CODE",{});var eI=i(jh);t5=a(eI,"push_to_hub=True"),eI.forEach(o),o5=a(Jr,`. By default, all
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
the `),Mh=s(Jr,"CODE",{});var tI=i(Mh);r5=a(tI,"hub-strategy"),tI.forEach(o),a5=a(Jr," value of your "),Oi=s(Jr,"A",{href:!0});var oI=i(Oi);n5=a(oI,"TrainingArguments"),oI.forEach(o),s5=a(Jr," to either:"),Jr.forEach(o),Zg=d(t),fr=s(t,"UL",{});var Eb=i(fr);gr=s(Eb,"LI",{});var Pg=i(gr);Vh=s(Pg,"CODE",{});var rI=i(Vh);i5=a(rI,'"checkpoint"'),rI.forEach(o),l5=a(Pg,`: the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
resume training easily with `),Hh=s(Pg,"CODE",{});var aI=i(Hh);d5=a(aI,'trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")'),aI.forEach(o),c5=a(Pg,"."),Pg.forEach(o),p5=d(Eb),Ci=s(Eb,"LI",{});var Nq=i(Ci);Bh=s(Nq,"CODE",{});var nI=i(Bh);m5=a(nI,'"all_checkpoints"'),nI.forEach(o),h5=a(Nq,`: all checkpoints are pushed like they appear in the output folder (so you will get one
checkpoint folder per folder in your final repository)`),Nq.forEach(o),Eb.forEach(o),Jg=d(t),$o=s(t,"H2",{class:!0});var Tb=i($o);_r=s(Tb,"A",{id:!0,class:!0,href:!0});var sI=i(_r);Yh=s(sI,"SPAN",{});var iI=i(Yh);u(Rn.$$.fragment,iI),iI.forEach(o),sI.forEach(o),u5=d(Tb),Zh=s(Tb,"SPAN",{});var lI=i(Zh);f5=a(lI,"Logging"),lI.forEach(o),Tb.forEach(o),Xg=d(t),Ue=s(t,"P",{});var Xr=i(Ue);g5=a(Xr,"By default "),Ii=s(Xr,"A",{href:!0});var dI=i(Ii);_5=a(dI,"Trainer"),dI.forEach(o),v5=a(Xr," will use "),Jh=s(Xr,"CODE",{});var cI=i(Jh);b5=a(cI,"logging.INFO"),cI.forEach(o),y5=a(Xr," for the main process and "),Xh=s(Xr,"CODE",{});var pI=i(Xh);w5=a(pI,"logging.WARNING"),pI.forEach(o),E5=a(Xr," for the replicas if any."),Xr.forEach(o),Kg=d(t),xt=s(t,"P",{});var Gd=i(xt);T5=a(Gd,"These defaults can be overridden to use any of the 5 "),Kh=s(Gd,"CODE",{});var mI=i(Kh);$5=a(mI,"logging"),mI.forEach(o),k5=a(Gd," levels with "),Ui=s(Gd,"A",{href:!0});var hI=i(Ui);x5=a(hI,"TrainingArguments"),hI.forEach(o),A5=a(Gd,`\u2019s
arguments:`),Gd.forEach(o),Qg=d(t),vr=s(t,"UL",{});var $b=i(vr);Ni=s($b,"LI",{});var zq=i(Ni);Qh=s(zq,"CODE",{});var uI=i(Qh);D5=a(uI,"log_level"),uI.forEach(o),q5=a(zq," - for the main process"),zq.forEach(o),P5=d($b),zi=s($b,"LI",{});var Lq=i(zi);eu=s(Lq,"CODE",{});var fI=i(eu);S5=a(fI,"log_level_replica"),fI.forEach(o),O5=a(Lq," - for the replicas"),Lq.forEach(o),$b.forEach(o),e_=d(t),Ne=s(t,"P",{});var Kr=i(Ne);C5=a(Kr,"Further, if "),Li=s(Kr,"A",{href:!0});var gI=i(Li);I5=a(gI,"TrainingArguments"),gI.forEach(o),U5=a(Kr,"\u2019s "),tu=s(Kr,"CODE",{});var _I=i(tu);N5=a(_I,"log_on_each_node"),_I.forEach(o),z5=a(Kr," is set to "),ou=s(Kr,"CODE",{});var vI=i(ou);L5=a(vI,"False"),vI.forEach(o),F5=a(Kr,` only the main node will
use the log level settings for its main process, all other nodes will use the log level settings for replicas.`),Kr.forEach(o),t_=d(t),Q=s(t,"P",{});var Be=i(Q);R5=a(Be,"Note that "),Fi=s(Be,"A",{href:!0});var bI=i(Fi);W5=a(bI,"Trainer"),bI.forEach(o),G5=a(Be," is going to set "),ru=s(Be,"CODE",{});var yI=i(ru);j5=a(yI,"transformers"),yI.forEach(o),M5=a(Be,`\u2019s log level separately for each node in its
`),au=s(Be,"CODE",{});var wI=i(au);V5=a(wI,"Trainer.__init__()"),wI.forEach(o),H5=a(Be,` So you may want to set this sooner (see the next example) if you tap into other
`),nu=s(Be,"CODE",{});var EI=i(nu);B5=a(EI,"transformers"),EI.forEach(o),Y5=a(Be," functionality before creating the "),Ri=s(Be,"A",{href:!0});var TI=i(Ri);Z5=a(TI,"Trainer"),TI.forEach(o),J5=a(Be," object."),Be.forEach(o),o_=d(t),Wi=s(t,"P",{});var $I=i(Wi);X5=a($I,"Here is an example of how this can be used in an application:"),$I.forEach(o),r_=d(t),u(Wn.$$.fragment,t),a_=d(t),Gi=s(t,"P",{});var kI=i(Gi);K5=a(kI,`And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
warnings you could run it as:`),kI.forEach(o),n_=d(t),u(Gn.$$.fragment,t),s_=d(t),ji=s(t,"P",{});var xI=i(ji);Q5=a(xI,`In the multi-node environment if you also don\u2019t want the logs to repeat for each node\u2019s main process, you will want to
change the above to:`),xI.forEach(o),i_=d(t),u(jn.$$.fragment,t),l_=d(t),Mi=s(t,"P",{});var AI=i(Mi);ex=a(AI,`and then only the main process of the first node will log at the \u201Cwarning\u201D level, and all other processes on the main
node and all processes on other nodes will log at the \u201Cerror\u201D level.`),AI.forEach(o),d_=d(t),Vi=s(t,"P",{});var DI=i(Vi);tx=a(DI,"If you need your application to be as quiet as possible you could do:"),DI.forEach(o),c_=d(t),u(Mn.$$.fragment,t),p_=d(t),br=s(t,"P",{});var kb=i(br);ox=a(kb,"(add "),su=s(kb,"CODE",{});var qI=i(su);rx=a(qI,"--log_on_each_node 0"),qI.forEach(o),ax=a(kb," if on multi-node environment)"),kb.forEach(o),m_=d(t),ko=s(t,"H2",{class:!0});var xb=i(ko);yr=s(xb,"A",{id:!0,class:!0,href:!0});var PI=i(yr);iu=s(PI,"SPAN",{});var SI=i(iu);u(Vn.$$.fragment,SI),SI.forEach(o),PI.forEach(o),nx=d(xb),lu=s(xb,"SPAN",{});var OI=i(lu);sx=a(OI,"Randomness"),OI.forEach(o),xb.forEach(o),h_=d(t),me=s(t,"P",{});var Zt=i(me);ix=a(Zt,"When resuming from a checkpoint generated by "),Hi=s(Zt,"A",{href:!0});var CI=i(Hi);lx=a(CI,"Trainer"),CI.forEach(o),dx=a(Zt,` all efforts are made to restore the
`),du=s(Zt,"EM",{});var II=i(du);cx=a(II,"python"),II.forEach(o),px=a(Zt,", "),cu=s(Zt,"EM",{});var UI=i(cu);mx=a(UI,"numpy"),UI.forEach(o),hx=a(Zt," and "),pu=s(Zt,"EM",{});var NI=i(pu);ux=a(NI,"pytorch"),NI.forEach(o),fx=a(Zt,` RNG states to the same states as they were at the moment of saving that checkpoint,
which should make the \u201Cstop and resume\u201D style of training as close as possible to non-stop training.`),Zt.forEach(o),u_=d(t),At=s(t,"P",{});var jd=i(At);gx=a(jd,`However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
determinism please refer to `),Hn=s(jd,"A",{href:!0,rel:!0});var zI=i(Hn);_x=a(zI,"Controlling sources of randomness"),zI.forEach(o),vx=a(jd,`. As explained in the document, that some of those settings
that make things deterministic (.e.g., `),mu=s(jd,"CODE",{});var LI=i(mu);bx=a(LI,"torch.backends.cudnn.deterministic"),LI.forEach(o),yx=a(jd,`) may slow things down, therefore this
can\u2019t be done by default, but you can enable those yourself if needed.`),jd.forEach(o),f_=d(t),xo=s(t,"H2",{class:!0});var Ab=i(xo);wr=s(Ab,"A",{id:!0,class:!0,href:!0});var FI=i(wr);hu=s(FI,"SPAN",{});var RI=i(hu);u(Bn.$$.fragment,RI),RI.forEach(o),FI.forEach(o),wx=d(Ab),uu=s(Ab,"SPAN",{});var WI=i(uu);Ex=a(WI,"Specific GPUs Selection"),WI.forEach(o),Ab.forEach(o),g_=d(t),Bi=s(t,"P",{});var GI=i(Bi);Tx=a(GI,"Let\u2019s discuss how you can tell your program which GPUs are to be used and in what order."),GI.forEach(o),__=d(t),Er=s(t,"P",{});var Db=i(Er);$x=a(Db,"When using "),Yn=s(Db,"A",{href:!0,rel:!0});var jI=i(Yn);fu=s(jI,"CODE",{});var MI=i(fu);kx=a(MI,"DistributedDataParallel"),MI.forEach(o),jI.forEach(o),xx=a(Db," to use only a subset of your GPUs, you simply specify the number of GPUs to use. For example, if you have 4 GPUs, but you wish to use the first 2 you can do:"),Db.forEach(o),v_=d(t),u(Zn.$$.fragment,t),b_=d(t),Dt=s(t,"P",{});var Md=i(Dt);Ax=a(Md,"if you have either "),Jn=s(Md,"A",{href:!0,rel:!0});var VI=i(Jn);gu=s(VI,"CODE",{});var HI=i(gu);Dx=a(HI,"accelerate"),HI.forEach(o),VI.forEach(o),qx=a(Md," or "),Xn=s(Md,"A",{href:!0,rel:!0});var BI=i(Xn);_u=s(BI,"CODE",{});var YI=i(_u);Px=a(YI,"deepspeed"),YI.forEach(o),BI.forEach(o),Sx=a(Md," installed you can also accomplish the same by using one of:"),Md.forEach(o),y_=d(t),u(Kn.$$.fragment,t),w_=d(t),u(Qn.$$.fragment,t),E_=d(t),Tr=s(t,"P",{});var qb=i(Tr);Ox=a(qb,"You don\u2019t need to use the Accelerate or "),Yi=s(qb,"A",{href:!0});var ZI=i(Yi);Cx=a(ZI,"the Deepspeed integration"),ZI.forEach(o),Ix=a(qb," features to use these launchers."),qb.forEach(o),T_=d(t),Zi=s(t,"P",{});var JI=i(Zi);Ux=a(JI,"Until now you were able to tell the program how many GPUs to use. Now let\u2019s discuss how to select specific GPUs and control their order."),JI.forEach(o),$_=d(t),Ji=s(t,"P",{});var XI=i(Ji);Nx=a(XI,"The following environment variables help you control which GPUs to use and their order."),XI.forEach(o),k_=d(t),Xi=s(t,"P",{});var KI=i(Xi);vu=s(KI,"STRONG",{});var QI=i(vu);bu=s(QI,"CODE",{});var eU=i(bu);zx=a(eU,"CUDA_VISIBLE_DEVICES"),eU.forEach(o),QI.forEach(o),KI.forEach(o),x_=d(t),$r=s(t,"P",{});var Pb=i($r);Lx=a(Pb,"If you have multiple GPUs and you\u2019d like to use only 1 or a few of those GPUs, set the environment variable "),yu=s(Pb,"CODE",{});var tU=i(yu);Fx=a(tU,"CUDA_VISIBLE_DEVICES"),tU.forEach(o),Rx=a(Pb," to a list of the GPUs to be used."),Pb.forEach(o),A_=d(t),Ki=s(t,"P",{});var oU=i(Ki);Wx=a(oU,"For example, let\u2019s say you have 4 GPUs: 0, 1, 2 and 3. To run only on the physical GPUs 0 and 2, you can do:"),oU.forEach(o),D_=d(t),u(es.$$.fragment,t),q_=d(t),qt=s(t,"P",{});var Vd=i(qt);Gx=a(Vd,"So now pytorch will see only 2 GPUs, where your physical GPUs 0 and 2 are mapped to "),wu=s(Vd,"CODE",{});var rU=i(wu);jx=a(rU,"cuda:0"),rU.forEach(o),Mx=a(Vd," and "),Eu=s(Vd,"CODE",{});var aU=i(Eu);Vx=a(aU,"cuda:1"),aU.forEach(o),Hx=a(Vd," correspondingly."),Vd.forEach(o),P_=d(t),Qi=s(t,"P",{});var nU=i(Qi);Bx=a(nU,"You can even change their order:"),nU.forEach(o),S_=d(t),u(ts.$$.fragment,t),O_=d(t),Pt=s(t,"P",{});var Hd=i(Pt);Yx=a(Hd,"Here your physical GPUs 0 and 2 are mapped to "),Tu=s(Hd,"CODE",{});var sU=i(Tu);Zx=a(sU,"cuda:1"),sU.forEach(o),Jx=a(Hd," and "),$u=s(Hd,"CODE",{});var iU=i($u);Xx=a(iU,"cuda:0"),iU.forEach(o),Kx=a(Hd," correspondingly."),Hd.forEach(o),C_=d(t),St=s(t,"P",{});var Bd=i(St);Qx=a(Bd,"The above examples were all for "),ku=s(Bd,"CODE",{});var lU=i(ku);eA=a(lU,"DistributedDataParallel"),lU.forEach(o),tA=a(Bd," use pattern, but the same method works for "),os=s(Bd,"A",{href:!0,rel:!0});var dU=i(os);xu=s(dU,"CODE",{});var cU=i(xu);oA=a(cU,"DataParallel"),cU.forEach(o),dU.forEach(o),rA=a(Bd," as well:"),Bd.forEach(o),I_=d(t),u(rs.$$.fragment,t),U_=d(t),el=s(t,"P",{});var pU=i(el);aA=a(pU,"To emulate an environment without GPUs simply set this environment variable to an empty value like so:"),pU.forEach(o),N_=d(t),u(as.$$.fragment,t),z_=d(t),tl=s(t,"P",{});var mU=i(tl);nA=a(mU,"As with any environment variable you can, of course, export those instead of adding these to the command line, as in:"),mU.forEach(o),L_=d(t),u(ns.$$.fragment,t),F_=d(t),ol=s(t,"P",{});var hU=i(ol);sA=a(hU,"but this approach can be confusing since you may forget you set up the environment variable earlier and not understand why the wrong GPUs are used. Therefore, it\u2019s a common practice to set the environment variable just for a specific run on the same command line as it\u2019s shown in most examples of this section."),hU.forEach(o),R_=d(t),rl=s(t,"P",{});var uU=i(rl);Au=s(uU,"STRONG",{});var fU=i(Au);Du=s(fU,"CODE",{});var gU=i(Du);iA=a(gU,"CUDA_DEVICE_ORDER"),gU.forEach(o),fU.forEach(o),uU.forEach(o),W_=d(t),kr=s(t,"P",{});var Sb=i(kr);lA=a(Sb,"There is an additional environment variable "),qu=s(Sb,"CODE",{});var _U=i(qu);dA=a(_U,"CUDA_DEVICE_ORDER"),_U.forEach(o),cA=a(Sb," that controls how the physical devices are ordered. The two choices are:"),Sb.forEach(o),G_=d(t),al=s(t,"OL",{});var vU=i(al);ss=s(vU,"LI",{});var Ob=i(ss);pA=a(Ob,"ordered by PCIe bus IDs (matches "),Pu=s(Ob,"CODE",{});var bU=i(Pu);mA=a(bU,"nvidia-smi"),bU.forEach(o),hA=a(Ob,"\u2019s order) - this is the default."),Ob.forEach(o),vU.forEach(o),j_=d(t),u(is.$$.fragment,t),M_=d(t),ls=s(t,"OL",{start:!0});var yU=i(ls);Su=s(yU,"LI",{});var wU=i(Su);uA=a(wU,"ordered by GPU compute capabilities"),wU.forEach(o),yU.forEach(o),V_=d(t),u(ds.$$.fragment,t),H_=d(t),Ot=s(t,"P",{});var Yd=i(Ot);fA=a(Yd,"Most of the time you don\u2019t need to care about this environment variable, but it\u2019s very helpful if you have a lopsided setup where you have an old and a new GPUs physically inserted in such a way so that the slow older card appears to be first. One way to fix that is to swap the cards. But if you can\u2019t swap the cards (e.g., if the cooling of the devices gets impacted) then setting "),Ou=s(Yd,"CODE",{});var EU=i(Ou);gA=a(EU,"CUDA_DEVICE_ORDER=FASTEST_FIRST"),EU.forEach(o),_A=a(Yd," will always put the newer faster card first. It\u2019ll be somewhat confusing though since "),Cu=s(Yd,"CODE",{});var TU=i(Cu);vA=a(TU,"nvidia-smi"),TU.forEach(o),bA=a(Yd," will still report them in the PCIe order."),Yd.forEach(o),B_=d(t),nl=s(t,"P",{});var $U=i(nl);yA=a($U,"The other solution to swapping the order is to use:"),$U.forEach(o),Y_=d(t),u(cs.$$.fragment,t),Z_=d(t),sl=s(t,"P",{});var kU=i(sl);wA=a(kU,"In this example we are working with just 2 GPUs, but of course the same would apply to as many GPUs as your computer has."),kU.forEach(o),J_=d(t),xr=s(t,"P",{});var Cb=i(xr);EA=a(Cb,"Also if you do set this environment variable it\u2019s the best to set it in your "),Iu=s(Cb,"CODE",{});var xU=i(Iu);TA=a(xU,"~/.bashrc"),xU.forEach(o),$A=a(Cb," file or some other startup config file and forget about it."),Cb.forEach(o),X_=d(t),Ao=s(t,"H2",{class:!0});var Ib=i(Ao);Ar=s(Ib,"A",{id:!0,class:!0,href:!0});var AU=i(Ar);Uu=s(AU,"SPAN",{});var DU=i(Uu);u(ps.$$.fragment,DU),DU.forEach(o),AU.forEach(o),kA=d(Ib),Nu=s(Ib,"SPAN",{});var qU=i(Nu);xA=a(qU,"Trainer Integrations"),qU.forEach(o),Ib.forEach(o),K_=d(t),Dr=s(t,"P",{});var Ub=i(Dr);AA=a(Ub,"The "),il=s(Ub,"A",{href:!0});var PU=i(il);DA=a(PU,"Trainer"),PU.forEach(o),qA=a(Ub,` has been extended to support libraries that may dramatically improve your training
time and fit much bigger models.`),Ub.forEach(o),Q_=d(t),ze=s(t,"P",{});var Qr=i(ze);PA=a(Qr,"Currently it supports third party solutions, "),ms=s(Qr,"A",{href:!0,rel:!0});var SU=i(ms);SA=a(SU,"DeepSpeed"),SU.forEach(o),OA=a(Qr," and "),hs=s(Qr,"A",{href:!0,rel:!0});var OU=i(hs);CA=a(OU,"FairScale"),OU.forEach(o),IA=a(Qr,", which implement parts of the paper "),us=s(Qr,"A",{href:!0,rel:!0});var CU=i(us);UA=a(CU,`ZeRO: Memory Optimizations
Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He`),CU.forEach(o),NA=a(Qr,"."),Qr.forEach(o),ev=d(t),ll=s(t,"P",{});var IU=i(ll);zA=a(IU,"This provided support is new and experimental as of this writing."),IU.forEach(o),tv=d(t),dl=s(t,"A",{id:!0}),i(dl).forEach(o),ov=d(t),Do=s(t,"H3",{class:!0});var Nb=i(Do);qr=s(Nb,"A",{id:!0,class:!0,href:!0});var UU=i(qr);zu=s(UU,"SPAN",{});var NU=i(zu);u(fs.$$.fragment,NU),NU.forEach(o),UU.forEach(o),LA=d(Nb),Lu=s(Nb,"SPAN",{});var zU=i(Lu);FA=a(zU,"CUDA Extension Installation Notes"),zU.forEach(o),Nb.forEach(o),rv=d(t),cl=s(t,"P",{});var LU=i(cl);RA=a(LU,"As of this writing, both FairScale and Deepspeed require compilation of CUDA C++ code, before they can be used."),LU.forEach(o),av=d(t),Ct=s(t,"P",{});var Zd=i(Ct);WA=a(Zd,"While all installation issues should be dealt with through the corresponding GitHub Issues of "),gs=s(Zd,"A",{href:!0,rel:!0});var FU=i(gs);GA=a(FU,"FairScale"),FU.forEach(o),jA=a(Zd," and "),_s=s(Zd,"A",{href:!0,rel:!0});var RU=i(_s);MA=a(RU,"Deepspeed"),RU.forEach(o),VA=a(Zd,`, there are a few common issues that one may encounter while building
any PyTorch extension that needs to build CUDA extensions.`),Zd.forEach(o),nv=d(t),pl=s(t,"P",{});var WU=i(pl);HA=a(WU,"Therefore, if you encounter a CUDA-related build issue while doing one of the following or both:"),WU.forEach(o),sv=d(t),u(vs.$$.fragment,t),iv=d(t),ml=s(t,"P",{});var GU=i(ml);BA=a(GU,"please, read the following notes first."),GU.forEach(o),lv=d(t),It=s(t,"P",{});var Jd=i(It);YA=a(Jd,"In these notes we give examples for what to do when "),Fu=s(Jd,"CODE",{});var jU=i(Fu);ZA=a(jU,"pytorch"),jU.forEach(o),JA=a(Jd," has been built with CUDA "),Ru=s(Jd,"CODE",{});var MU=i(Ru);XA=a(MU,"10.2"),MU.forEach(o),KA=a(Jd,`. If your situation is
different remember to adjust the version number to the one you are after.`),Jd.forEach(o),dv=d(t),qo=s(t,"H4",{class:!0});var zb=i(qo);Pr=s(zb,"A",{id:!0,class:!0,href:!0});var VU=i(Pr);Wu=s(VU,"SPAN",{});var HU=i(Wu);u(bs.$$.fragment,HU),HU.forEach(o),VU.forEach(o),QA=d(zb),Gu=s(zb,"SPAN",{});var BU=i(Gu);e9=a(BU,"Possible problem #1"),BU.forEach(o),zb.forEach(o),cv=d(t),hl=s(t,"P",{});var YU=i(hl);t9=a(YU,`While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA
installed system-wide.`),YU.forEach(o),pv=d(t),Le=s(t,"P",{});var ea=i(Le);o9=a(ea,"For example, if you installed "),ju=s(ea,"CODE",{});var ZU=i(ju);r9=a(ZU,"pytorch"),ZU.forEach(o),a9=a(ea," with "),Mu=s(ea,"CODE",{});var JU=i(Mu);n9=a(JU,"cudatoolkit==10.2"),JU.forEach(o),s9=a(ea,` in the Python environment, you also need to have
CUDA `),Vu=s(ea,"CODE",{});var XU=i(Vu);i9=a(XU,"10.2"),XU.forEach(o),l9=a(ea," installed system-wide."),ea.forEach(o),mv=d(t),Ut=s(t,"P",{});var Xd=i(Ut);d9=a(Xd,"The exact location may vary from system to system, but "),Hu=s(Xd,"CODE",{});var KU=i(Hu);c9=a(KU,"/usr/local/cuda-10.2"),KU.forEach(o),p9=a(Xd,` is the most common location on many
Unix systems. When CUDA is correctly set up and added to the `),Bu=s(Xd,"CODE",{});var QU=i(Bu);m9=a(QU,"PATH"),QU.forEach(o),h9=a(Xd,` environment variable, one can find the
installation location by doing:`),Xd.forEach(o),hv=d(t),u(ys.$$.fragment,t),uv=d(t),Sr=s(t,"P",{});var Lb=i(Sr);u9=a(Lb,`If you don\u2019t have CUDA installed system-wide, install it first. You will find the instructions by using your favorite
search engine. For example, if you\u2019re on Ubuntu you may want to search for: `),ws=s(Lb,"A",{href:!0,rel:!0});var eN=i(ws);f9=a(eN,"ubuntu cuda 10.2 install"),eN.forEach(o),g9=a(Lb,"."),Lb.forEach(o),fv=d(t),Po=s(t,"H4",{class:!0});var Fb=i(Po);Or=s(Fb,"A",{id:!0,class:!0,href:!0});var tN=i(Or);Yu=s(tN,"SPAN",{});var oN=i(Yu);u(Es.$$.fragment,oN),oN.forEach(o),tN.forEach(o),_9=d(Fb),Zu=s(Fb,"SPAN",{});var rN=i(Zu);v9=a(rN,"Possible problem #2"),rN.forEach(o),Fb.forEach(o),gv=d(t),ul=s(t,"P",{});var aN=i(ul);b9=a(aN,`Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you
may have:`),aN.forEach(o),_v=d(t),u(Ts.$$.fragment,t),vv=d(t),Nt=s(t,"P",{});var Kd=i(Nt);y9=a(Kd,"Now, in this situation you need to make sure that your "),Ju=s(Kd,"CODE",{});var nN=i(Ju);w9=a(nN,"PATH"),nN.forEach(o),E9=a(Kd," and "),Xu=s(Kd,"CODE",{});var sN=i(Xu);T9=a(sN,"LD_LIBRARY_PATH"),sN.forEach(o),$9=a(Kd,` environment variables contain
the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the
last version was installed. If you encounter the problem, where the package build fails because it can\u2019t find the right
CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned
environment variables.`),Kd.forEach(o),bv=d(t),fl=s(t,"P",{});var iN=i(fl);k9=a(iN,"First, you may look at their contents:"),iN.forEach(o),yv=d(t),u($s.$$.fragment,t),wv=d(t),gl=s(t,"P",{});var lN=i(gl);x9=a(lN,"so you get an idea of what is inside."),lN.forEach(o),Ev=d(t),Cr=s(t,"P",{});var Rb=i(Cr);A9=a(Rb,"It\u2019s possible that "),Ku=s(Rb,"CODE",{});var dN=i(Ku);D9=a(dN,"LD_LIBRARY_PATH"),dN.forEach(o),q9=a(Rb," is empty."),Rb.forEach(o),Tv=d(t),ot=s(t,"P",{});var Ks=i(ot);Qu=s(Ks,"CODE",{});var cN=i(Qu);P9=a(cN,"PATH"),cN.forEach(o),S9=a(Ks," lists the locations of where executables can be found and "),ef=s(Ks,"CODE",{});var pN=i(ef);O9=a(pN,"LD_LIBRARY_PATH"),pN.forEach(o),C9=a(Ks,` is for where shared libraries
are to looked for. In both cases, earlier entries have priority over the later ones. `),tf=s(Ks,"CODE",{});var mN=i(tf);I9=a(mN,":"),mN.forEach(o),U9=a(Ks,` is used to separate multiple
entries.`),Ks.forEach(o),$v=d(t),_l=s(t,"P",{});var hN=i(_l);N9=a(hN,`Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by
doing:`),hN.forEach(o),kv=d(t),u(ks.$$.fragment,t),xv=d(t),vl=s(t,"P",{});var uN=i(vl);z9=a(uN,"Note that we aren\u2019t overwriting the existing values, but prepending instead."),uN.forEach(o),Av=d(t),Fe=s(t,"P",{});var ta=i(Fe);L9=a(ta,`Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do
exist. `),of=s(ta,"CODE",{});var fN=i(of);F9=a(fN,"lib64"),fN.forEach(o),R9=a(ta," sub-directory is where the various CUDA "),rf=s(ta,"CODE",{});var gN=i(rf);W9=a(gN,".so"),gN.forEach(o),G9=a(ta," objects, like "),af=s(ta,"CODE",{});var _N=i(af);j9=a(_N,"libcudart.so"),_N.forEach(o),M9=a(ta,` reside, it\u2019s unlikely
that your system will have it named differently, but if it is adjust it to reflect your reality.`),ta.forEach(o),Dv=d(t),So=s(t,"H4",{class:!0});var Wb=i(So);Ir=s(Wb,"A",{id:!0,class:!0,href:!0});var vN=i(Ir);nf=s(vN,"SPAN",{});var bN=i(nf);u(xs.$$.fragment,bN),bN.forEach(o),vN.forEach(o),V9=d(Wb),sf=s(Wb,"SPAN",{});var yN=i(sf);H9=a(yN,"Possible problem #3"),yN.forEach(o),Wb.forEach(o),qv=d(t),zt=s(t,"P",{});var Qd=i(zt);B9=a(Qd,"Some older CUDA versions may refuse to build with newer compilers. For example, you my have "),lf=s(Qd,"CODE",{});var wN=i(lf);Y9=a(wN,"gcc-9"),wN.forEach(o),Z9=a(Qd,` but it wants
`),df=s(Qd,"CODE",{});var EN=i(df);J9=a(EN,"gcc-7"),EN.forEach(o),X9=a(Qd,"."),Qd.forEach(o),Pv=d(t),bl=s(t,"P",{});var TN=i(bl);K9=a(TN,"There are various ways to go about it."),TN.forEach(o),Sv=d(t),yl=s(t,"P",{});var $N=i(yl);Q9=a($N,"If you can install the latest CUDA toolkit it typically should support the newer compiler."),$N.forEach(o),Ov=d(t),Ur=s(t,"P",{});var Gb=i(Ur);e8=a(Gb,`Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may
already have it but it\u2019s not the default one, so the build system can\u2019t see it. If you have `),cf=s(Gb,"CODE",{});var kN=i(cf);t8=a(kN,"gcc-7"),kN.forEach(o),o8=a(Gb,` installed but the
build system complains it can\u2019t find it, the following might do the trick:`),Gb.forEach(o),Cv=d(t),u(As.$$.fragment,t),Iv=d(t),j=s(t,"P",{});var ue=i(j);r8=a(ue,"Here, we are making a symlink to "),pf=s(ue,"CODE",{});var xN=i(pf);a8=a(xN,"gcc-7"),xN.forEach(o),n8=a(ue," from "),mf=s(ue,"CODE",{});var AN=i(mf);s8=a(AN,"/usr/local/cuda-10.2/bin/gcc"),AN.forEach(o),i8=a(ue,` and since
`),hf=s(ue,"CODE",{});var DN=i(hf);l8=a(DN,"/usr/local/cuda-10.2/bin/"),DN.forEach(o),d8=a(ue," should be in the "),uf=s(ue,"CODE",{});var qN=i(uf);c8=a(qN,"PATH"),qN.forEach(o),p8=a(ue,` environment variable (see the previous problem\u2019s solution), it
should find `),ff=s(ue,"CODE",{});var PN=i(ff);m8=a(PN,"gcc-7"),PN.forEach(o),h8=a(ue," (and "),gf=s(ue,"CODE",{});var SN=i(gf);u8=a(SN,"g++7"),SN.forEach(o),f8=a(ue,") and then the build will succeed."),ue.forEach(o),Uv=d(t),wl=s(t,"P",{});var ON=i(wl);g8=a(ON,"As always make sure to edit the paths in the example to match your situation."),ON.forEach(o),Nv=d(t),Oo=s(t,"H3",{class:!0});var jb=i(Oo);Nr=s(jb,"A",{id:!0,class:!0,href:!0});var CN=i(Nr);_f=s(CN,"SPAN",{});var IN=i(_f);u(Ds.$$.fragment,IN),IN.forEach(o),CN.forEach(o),_8=d(jb),vf=s(jb,"SPAN",{});var UN=i(vf);v8=a(UN,"FairScale"),UN.forEach(o),jb.forEach(o),zv=d(t),Re=s(t,"P",{});var oa=i(Re);b8=a(oa,"By integrating "),qs=s(oa,"A",{href:!0,rel:!0});var NN=i(qs);y8=a(NN,"FairScale"),NN.forEach(o),w8=a(oa," the "),El=s(oa,"A",{href:!0});var zN=i(El);E8=a(zN,"Trainer"),zN.forEach(o),T8=a(oa,`
provides support for the following features from `),Ps=s(oa,"A",{href:!0,rel:!0});var LN=i(Ps);$8=a(LN,"the ZeRO paper"),LN.forEach(o),k8=a(oa,":"),oa.forEach(o),Lv=d(t),We=s(t,"OL",{});var ra=i(We);bf=s(ra,"LI",{});var FN=i(bf);x8=a(FN,"Optimizer State Sharding"),FN.forEach(o),A8=d(ra),yf=s(ra,"LI",{});var RN=i(yf);D8=a(RN,"Gradient Sharding"),RN.forEach(o),q8=d(ra),wf=s(ra,"LI",{});var WN=i(wf);P8=a(WN,"Model Parameters Sharding (new and very experimental)"),WN.forEach(o),S8=d(ra),Ef=s(ra,"LI",{});var GN=i(Ef);O8=a(GN,"CPU offload (new and very experimental)"),GN.forEach(o),ra.forEach(o),Fv=d(t),Tl=s(t,"P",{});var jN=i(Tl);C8=a(jN,"You will need at least two GPUs to use this feature."),jN.forEach(o),Rv=d(t),Ss=s(t,"P",{});var Fq=i(Ss);Tf=s(Fq,"STRONG",{});var MN=i(Tf);I8=a(MN,"Installation"),MN.forEach(o),U8=a(Fq,":"),Fq.forEach(o),Wv=d(t),$l=s(t,"P",{});var VN=i($l);N8=a(VN,"Install the library via pypi:"),VN.forEach(o),Gv=d(t),u(Os.$$.fragment,t),jv=d(t),Lt=s(t,"P",{});var ec=i(Lt);z8=a(ec,"or via "),$f=s(ec,"CODE",{});var HN=i($f);L8=a(HN,"transformers"),HN.forEach(o),F8=a(ec,"\u2019 "),kf=s(ec,"CODE",{});var BN=i(kf);R8=a(BN,"extras"),BN.forEach(o),W8=a(ec,":"),ec.forEach(o),Mv=d(t),u(Cs.$$.fragment,t),Vv=d(t),Ft=s(t,"P",{});var tc=i(Ft);G8=a(tc,"(available starting from "),xf=s(tc,"CODE",{});var YN=i(xf);j8=a(YN,"transformers==4.6.0"),YN.forEach(o),M8=a(tc,") or find more details on "),Is=s(tc,"A",{href:!0,rel:!0});var ZN=i(Is);V8=a(ZN,"the FairScale\u2019s GitHub page"),ZN.forEach(o),H8=a(tc,"."),tc.forEach(o),Hv=d(t),zr=s(t,"P",{});var Mb=i(zr);B8=a(Mb,"If you\u2019re still struggling with the build, first make sure to read "),kl=s(Mb,"A",{href:!0});var JN=i(kl);Y8=a(JN,"CUDA Extension Installation Notes"),JN.forEach(o),Z8=a(Mb,"."),Mb.forEach(o),Bv=d(t),xl=s(t,"P",{});var XN=i(xl);J8=a(XN,"If it\u2019s still not resolved the build issue, here are a few more ideas."),XN.forEach(o),Yv=d(t),Us=s(t,"P",{});var Rq=i(Us);Af=s(Rq,"CODE",{});var KN=i(Af);X8=a(KN,"fairscale"),KN.forEach(o),K8=a(Rq,` seems to have an issue with the recently introduced by pip build isolation feature. If you have a problem
with it, you may want to try one of:`),Rq.forEach(o),Zv=d(t),u(Ns.$$.fragment,t),Jv=d(t),Al=s(t,"P",{});var QN=i(Al);Q8=a(QN,"or:"),QN.forEach(o),Xv=d(t),u(zs.$$.fragment,t),Kv=d(t),Ls=s(t,"P",{});var Wq=i(Ls);Df=s(Wq,"CODE",{});var ez=i(Df);eD=a(ez,"fairscale"),ez.forEach(o),tD=a(Wq," also has issues with building against pytorch-nightly, so if you use it you may have to try one of:"),Wq.forEach(o),Qv=d(t),u(Fs.$$.fragment,t),e1=d(t),Dl=s(t,"P",{});var tz=i(Dl);oD=a(tz,"or:"),tz.forEach(o),t1=d(t),u(Rs.$$.fragment,t),o1=d(t),ql=s(t,"P",{});var oz=i(ql);rD=a(oz,"Of course, adjust the urls to match the cuda version you use."),oz.forEach(o),r1=d(t),Lr=s(t,"P",{});var Vb=i(Lr);aD=a(Vb,`If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of
`),Ws=s(Vb,"A",{href:!0,rel:!0});var rz=i(Ws);nD=a(rz,"FairScale"),rz.forEach(o),sD=a(Vb,"."),Vb.forEach(o),a1=d(t),Gs=s(t,"P",{});var Gq=i(Gs);qf=s(Gq,"STRONG",{});var az=i(qf);iD=a(az,"Usage"),az.forEach(o),lD=a(Gq,":"),Gq.forEach(o),n1=d(t),Rt=s(t,"P",{});var oc=i(Rt);dD=a(oc,"To use the first version of Sharded data-parallelism, add "),Pf=s(oc,"CODE",{});var nz=i(Pf);cD=a(nz,"--sharded_ddp simple"),nz.forEach(o),pD=a(oc,` to the command line arguments, and
make sure you have added the distributed launcher `),Sf=s(oc,"CODE",{});var sz=i(Sf);mD=a(sz,"-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE"),sz.forEach(o),hD=a(oc," if you haven\u2019t been using it already."),oc.forEach(o),s1=d(t),Fr=s(t,"P",{});var Hb=i(Fr);uD=a(Hb,"For example here is how you could use it for "),Of=s(Hb,"CODE",{});var iz=i(Of);fD=a(iz,"run_translation.py"),iz.forEach(o),gD=a(Hb," with 2 GPUs:"),Hb.forEach(o),i1=d(t),u(js.$$.fragment,t),l1=d(t),Pl=s(t,"P",{});var lz=i(Pl);_D=a(lz,"Notes:"),lz.forEach(o),d1=d(t),Ge=s(t,"UL",{});var aa=i(Ge);Cf=s(aa,"LI",{});var dz=i(Cf);vD=a(dz,"This feature requires distributed training (so multiple GPUs)."),dz.forEach(o),bD=d(aa),If=s(aa,"LI",{});var cz=i(If);yD=a(cz,"It is not implemented for TPUs."),cz.forEach(o),wD=d(aa),Ms=s(aa,"LI",{});var Bb=i(Ms);ED=a(Bb,"It works with "),Uf=s(Bb,"CODE",{});var pz=i(Uf);TD=a(pz,"--fp16"),pz.forEach(o),$D=a(Bb," too, to make things even faster."),Bb.forEach(o),kD=d(aa),Vs=s(aa,"LI",{});var Yb=i(Vs);xD=a(Yb,"One of the main benefits of enabling "),Nf=s(Yb,"CODE",{});var mz=i(Nf);AD=a(mz,"--sharded_ddp simple"),mz.forEach(o),DD=a(Yb,` is that it uses a lot less GPU memory, so you should be
able to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
significantly shorter training time.`),Yb.forEach(o),aa.forEach(o),c1=d(t),Hs=s(t,"OL",{start:!0});var hz=i(Hs);rt=s(hz,"LI",{});var na=i(rt);qD=a(na,"To use the second version of Sharded data-parallelism, add "),zf=s(na,"CODE",{});var uz=i(zf);PD=a(uz,"--sharded_ddp zero_dp_2"),uz.forEach(o),SD=a(na," or "),Lf=s(na,"CODE",{});var fz=i(Lf);OD=a(fz,"--sharded_ddp zero_dp_3"),fz.forEach(o),CD=a(na," to the command line arguments, and make sure you have added the distributed launcher "),Ff=s(na,"CODE",{});var gz=i(Ff);ID=a(gz,"-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE"),gz.forEach(o),UD=a(na," if you haven\u2019t been using it already."),na.forEach(o),hz.forEach(o),p1=d(t),Rr=s(t,"P",{});var Zb=i(Rr);ND=a(Zb,"For example here is how you could use it for "),Rf=s(Zb,"CODE",{});var _z=i(Rf);zD=a(_z,"run_translation.py"),_z.forEach(o),LD=a(Zb," with 2 GPUs:"),Zb.forEach(o),m1=d(t),u(Bs.$$.fragment,t),h1=d(t),Co=s(t,"P",{});var Sg=i(Co);Wf=s(Sg,"CODE",{});var vz=i(Wf);FD=a(vz,"zero_dp_2"),vz.forEach(o),RD=a(Sg," is an optimized version of the simple wrapper, while "),Gf=s(Sg,"CODE",{});var bz=i(Gf);WD=a(bz,"zero_dp_3"),bz.forEach(o),GD=a(Sg,` fully shards model weights,
gradients and optimizer states.`),Sg.forEach(o),u1=d(t),Wt=s(t,"P",{});var rc=i(Wt);jD=a(rc,"Both are compatible with adding "),jf=s(rc,"CODE",{});var yz=i(jf);MD=a(yz,"cpu_offload"),yz.forEach(o),VD=a(rc," to enable ZeRO-offload (activate it like this: "),Mf=s(rc,"CODE",{});var wz=i(Mf);HD=a(wz,'--sharded_ddp "zero_dp_2 cpu_offload"'),wz.forEach(o),BD=a(rc,")."),rc.forEach(o),f1=d(t),Sl=s(t,"P",{});var Ez=i(Sl);YD=a(Ez,"Notes:"),Ez.forEach(o),g1=d(t),he=s(t,"UL",{});var Jt=i(he);Vf=s(Jt,"LI",{});var Tz=i(Vf);ZD=a(Tz,"This feature requires distributed training (so multiple GPUs)."),Tz.forEach(o),JD=d(Jt),Hf=s(Jt,"LI",{});var $z=i(Hf);XD=a($z,"It is not implemented for TPUs."),$z.forEach(o),KD=d(Jt),Ys=s(Jt,"LI",{});var Jb=i(Ys);QD=a(Jb,"It works with "),Bf=s(Jb,"CODE",{});var kz=i(Bf);e7=a(kz,"--fp16"),kz.forEach(o),t7=a(Jb," too, to make things even faster."),Jb.forEach(o),o7=d(Jt),Io=s(Jt,"LI",{});var ac=i(Io);r7=a(ac,"The "),Yf=s(ac,"CODE",{});var xz=i(Yf);a7=a(xz,"cpu_offload"),xz.forEach(o),n7=a(ac," additional option requires "),Zf=s(ac,"CODE",{});var Az=i(Zf);s7=a(Az,"--fp16"),Az.forEach(o),i7=a(ac,"."),ac.forEach(o),l7=d(Jt),Jf=s(Jt,"LI",{});var Dz=i(Jf);d7=a(Dz,`This is an area of active development, so make sure you have a source install of fairscale to use this feature as
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A=new ao({}),so=new ao({}),io=new v({props:{name:"class transformers.PreTrainedModel",anchor:"transformers.PreTrainedModel",parameters:[{name:"config",val:": PretrainedConfig"},{name:"*inputs",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L423"}}),co=new v({props:{name:"push_to_hub",anchor:"transformers.file_utils.PushToHubMixin.push_to_hub",parameters:[{name:"repo_path_or_name",val:": typing.Optional[str] = None"},{name:"repo_url",val:": typing.Optional[str] = None"},{name:"use_temp_dir",val:": bool = False"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"organization",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"**model_card_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842",parametersDescription:[{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name",description:`<strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your model in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.`,name:"repo_path_or_name"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_url",description:`<strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.`,name:"repo_url"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir",description:`<strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.`,name:"use_temp_dir"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add model"</code>.`,name:"commit_message"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.organization",description:`<strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your model (you must be a member of this organization).`,name:"organization"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).`,name:"private"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token",description:`<strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.`,name:"use_auth_token"}],returnDescription:`
<p>The url of the commit of your model in the given repository.</p>
`,returnType:`
<p><code>str</code></p>
`}}),ho=new Q({props:{code:`from transformers import AutoModel
model = AutoModel.from_pretrained("bert-base-cased")
# Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
model.push_to_hub("my-finetuned-bert")
# Push the model to your namespace with the name "my-finetuned-bert" with no local clone.
model.push_to_hub("my-finetuned-bert", use_temp_dir=True)
# Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
model.push_to_hub("my-finetuned-bert", organization="huggingface")
# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
model.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
model = AutoModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" with no local clone.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)`}}),fo=new v({props:{name:"from_pretrained",anchor:"transformers.PreTrainedModel.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike, NoneType]"},{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1091",parametersDescription:[{anchor:"transformers.PreTrainedModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
<li>A path or url to a model folder containing a <em>flax checkpoint file</em> in <em>.msgpack</em> format (e.g,
<code>./flax_model/</code> containing <code>flax_model.msgpack</code>). In this case, <code>from_flax</code> should be set to
<code>True</code>.</li>
<li><code>None</code> if you are both providing the configuration and state dictionary (resp. with keyword
arguments <code>config</code> and <code>state_dict</code>).</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.PreTrainedModel.from_pretrained.model_args",description:`<strong>model_args</strong> (sequence of positional arguments, <em>optional</em>) —
All remaining positional arguments will be passed to the underlying model’s <code>__init__</code> method.`,name:"model_args"},{anchor:"transformers.PreTrainedModel.from_pretrained.config",description:`<strong>config</strong> (<code>Union[PretrainedConfig, str, os.PathLike]</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>an instance of a class derived from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>,</li>
<li>a string or path valid as input to <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>.</li>
</ul>`,name:"config"}]}}),st=new fl({props:{$$slots:{default:[aw]},$$scope:{ctx:S}}}),it=new fl({props:{$$slots:{default:[sw]},$$scope:{ctx:S}}}),_o=new Q({props:{code:`from transformers import BertConfig, BertModel
# Download model and configuration from huggingface.co and cache.
model = BertModel.from_pretrained("bert-base-uncased")
# Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
model = BertModel.from_pretrained("./test/saved_model/")
# Update configuration during loading.
model = BertModel.from_pretrained("bert-base-uncased", output_attentions=True)
assert model.config.output_attentions == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
config = BertConfig.from_json_file("./tf_model/my_tf_model_config.json")
model = BertModel.from_pretrained("./tf_model/my_tf_checkpoint.ckpt.index", from_tf=True, config=config)
# Loading from a Flax checkpoint file instead of a PyTorch model (slower)
model = BertModel.from_pretrained("bert-base-uncased", from_flax=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, BertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Update configuration during loading.</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> model.config.output_attentions == <span class="hljs-literal">True</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>config = BertConfig.from_json_file(<span class="hljs-string">"./tf_model/my_tf_model_config.json"</span>)
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"./tf_model/my_tf_checkpoint.ckpt.index"</span>, from_tf=<span class="hljs-literal">True</span>, config=config)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a Flax checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, from_flax=<span class="hljs-literal">True</span>)`}}),bo=new v({props:{name:"get_input_embeddings",anchor:"transformers.PreTrainedModel.get_input_embeddings",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L575",returnDescription:`
<p>A torch module mapping vocabulary to hidden states.</p>
`,returnType:`
<p><code>nn.Module</code></p>
`}}),vo=new v({props:{name:"get_output_embeddings",anchor:"transformers.PreTrainedModel.get_output_embeddings",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L601",returnDescription:`
<p>A torch module mapping hidden states to vocabulary.</p>
`,returnType:`
<p><code>nn.Module</code></p>
`}}),yo=new v({props:{name:"gradient_checkpointing_disable",anchor:"transformers.PreTrainedModel.gradient_checkpointing_disable",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L980"}}),$o=new v({props:{name:"gradient_checkpointing_enable",anchor:"transformers.PreTrainedModel.gradient_checkpointing_enable",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L969"}}),wo=new v({props:{name:"init_weights",anchor:"transformers.PreTrainedModel.init_weights",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L936"}}),To=new v({props:{name:"post_init",anchor:"transformers.PreTrainedModel.post_init",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L494"}}),xo=new v({props:{name:"prune_heads",anchor:"transformers.PreTrainedModel.prune_heads",parameters:[{name:"heads_to_prune",val:": typing.Dict[int, typing.List[int]]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L952",parametersDescription:[{anchor:"transformers.PreTrainedModel.prune_heads.heads_to_prune",description:`<strong>heads_to_prune</strong> (<code>Dict[int, List[int]]</code>) —
Dictionary with keys being selected layer indices (<code>int</code>) and associated values being the list of heads
to prune in said layer (list of <code>int</code>). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on
layer 1 and heads 2 and 3 on layer 2.`,name:"heads_to_prune"}]}}),Po=new v({props:{name:"register_for_auto_class",anchor:"transformers.PreTrainedModel.register_for_auto_class",parameters:[{name:"auto_class",val:" = 'AutoModel'"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1817",parametersDescription:[{anchor:"transformers.PreTrainedModel.register_for_auto_class.auto_class",description:`<strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"AutoModel"</code>) —
The auto class to register this new model with.`,name:"auto_class"}]}}),ht=new fl({props:{warning:"{true}",$$slots:{default:[iw]},$$scope:{ctx:S}}}),Eo=new v({props:{name:"resize_token_embeddings",anchor:"transformers.PreTrainedModel.resize_token_embeddings",parameters:[{name:"new_num_tokens",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L729",parametersDescription:[{anchor:"transformers.PreTrainedModel.resize_token_embeddings.new_num_tokens",description:`<strong>new_num_tokens</strong> (<code>int</code>, <em>optional</em>) —
The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
vectors at the end. Reducing the size will remove vectors from the end. If not provided or <code>None</code>, just
returns a pointer to the input tokens <code>torch.nn.Embedding</code> module of the model without doing anything.`,name:"new_num_tokens"}],returnDescription:`
<p>Pointer to the input tokens Embeddings Module of the model.</p>
`,returnType:`
<p><code>torch.nn.Embedding</code></p>
`}}),jo=new v({props:{name:"save_pretrained",anchor:"transformers.PreTrainedModel.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"save_config",val:": bool = True"},{name:"state_dict",val:": typing.Optional[dict] = None"},{name:"save_function",val:": typing.Callable = <function save at 0x7f461a422820>"},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1000",parametersDescription:[{anchor:"transformers.PreTrainedModel.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory to which to save. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"transformers.PreTrainedModel.save_pretrained.save_config",description:`<strong>save_config</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to save the config of the model. Useful when in distributed training like TPUs and need
to call this function on all processes. In this case, set <code>save_config=True</code> only on the main process
to avoid race conditions.`,name:"save_config"},{anchor:"transformers.PreTrainedModel.save_pretrained.state_dict",description:`<strong>state_dict</strong> (nested dictionary of <code>torch.Tensor</code>) —
The state dictionary of the model to save. Will default to <code>self.state_dict()</code>, but can be used to only
save parts of the model or if special precautions need to be taken when recovering the state dictionary
of a model (like when using model parallelism).`,name:"state_dict"},{anchor:"transformers.PreTrainedModel.save_pretrained.save_function",description:`<strong>save_function</strong> (<code>Callable</code>) —
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
need to replace <code>torch.save</code> by another method.`,name:"save_function"},{anchor:"transformers.PreTrainedModel.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"push_to_hub"}]}}),Fo=new v({props:{name:"set_input_embeddings",anchor:"transformers.PreTrainedModel.set_input_embeddings",parameters:[{name:"value",val:": Module"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L588",parametersDescription:[{anchor:"transformers.PreTrainedModel.set_input_embeddings.value",description:"<strong>value</strong> (<code>nn.Module</code>) — A module mapping vocabulary to hidden states.",name:"value"}]}}),qo=new v({props:{name:"tie_weights",anchor:"transformers.PreTrainedModel.tie_weights",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L616"}}),Co=new ao({}),Io=new Q({props:{code:'model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)',highlighted:'model = T5ForConditionalGeneration.from_pretrained(<span class="hljs-string">"t5"</span>, torch_dtype=torch.float16)'}}),Lo=new Q({props:{code:'model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")',highlighted:'model = T5ForConditionalGeneration.from_pretrained(<span class="hljs-string">"t5"</span>, torch_dtype=<span class="hljs-string">"auto"</span>)'}}),zo=new Q({props:{code:`config = T5Config.from_pretrained("t5")
model = AutoModel.from_config(config)`,highlighted:`config = T5Config.from_pretrained(<span class="hljs-string">"t5"</span>)
model = AutoModel.from_config(config)`}}),Oo=new ao({}),Uo=new v({props:{name:"class transformers.modeling_utils.ModuleUtilsMixin",anchor:"transformers.modeling_utils.ModuleUtilsMixin",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L151"}}),Ho=new v({props:{name:"add_memory_hooks",anchor:"transformers.modeling_utils.ModuleUtilsMixin.add_memory_hooks",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L182"}}),Xo=new v({props:{name:"estimate_tokens",anchor:"transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens",parameters:[{name:"input_dict",val:": typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L378",parametersDescription:[{anchor:"transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens.inputs",description:"<strong>inputs</strong> (<code>dict</code>) — The model inputs.",name:"inputs"}],returnDescription:`
<p>The total number of tokens.</p>
`,returnType:`
<p><code>int</code></p>
`}}),Bo=new v({props:{name:"floating_point_ops",anchor:"transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops",parameters:[{name:"input_dict",val:": typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]"},{name:"exclude_embeddings",val:": bool = True"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L396",parametersDescription:[{anchor:"transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.batch_size",description:`<strong>batch_size</strong> (<code>int</code>) —
The batch size for the forward pass.`,name:"batch_size"},{anchor:"transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.sequence_length",description:`<strong>sequence_length</strong> (<code>int</code>) —
The number of tokens in each line of the batch.`,name:"sequence_length"},{anchor:"transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.exclude_embeddings",description:`<strong>exclude_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to count embedding and softmax operations.`,name:"exclude_embeddings"}],returnDescription:`
<p>The number of floating-point operations.</p>
`,returnType:`
<p><code>int</code></p>
`}}),So=new v({props:{name:"get_extended_attention_mask",anchor:"transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask",parameters:[{name:"attention_mask",val:": Tensor"},{name:"input_shape",val:": typing.Tuple[int]"},{name:"device",val:": <property object at 0x7f45b4efb950>"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L271",parametersDescription:[{anchor:"transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code>) —
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.`,name:"attention_mask"},{anchor:"transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask.input_shape",description:`<strong>input_shape</strong> (<code>Tuple[int]</code>) —
The shape of the input to the model.
device — (<code>torch.device</code>):
The device of the input to the model.`,name:"input_shape"}],returnDescription:`
<p><code>torch.Tensor</code> The extended attention mask, with a the same dtype as <code>attention_mask.dtype</code>.</p>
`}}),Wo=new v({props:{name:"get_head_mask",anchor:"transformers.modeling_utils.ModuleUtilsMixin.get_head_mask",parameters:[{name:"head_mask",val:": typing.Optional[torch.Tensor]"},{name:"num_hidden_layers",val:": int"},{name:"is_attention_chunked",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L314",parametersDescription:[{anchor:"transformers.modeling_utils.ModuleUtilsMixin.get_head_mask.head_mask",description:`<strong>head_mask</strong> (<code>torch.Tensor</code> with shape <code>[num_heads]</code> or <code>[num_hidden_layers x num_heads]</code>, <em>optional</em>) —
The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).`,name:"head_mask"},{anchor:"transformers.modeling_utils.ModuleUtilsMixin.get_head_mask.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>) —
The number of hidden layers in the model.
is_attention_chunked — (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether or not the attentions scores are computed by chunks or not.`,name:"num_hidden_layers"}],returnDescription:`
<p><code>torch.Tensor</code> with shape <code>[num_hidden_layers x batch x num_heads x seq_length x seq_length]</code> or list with
<code>[None]</code> for each layer.</p>
`}}),Go=new v({props:{name:"invert_attention_mask",anchor:"transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask",parameters:[{name:"encoder_attention_mask",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L218",parametersDescription:[{anchor:"transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask.encoder_attention_mask",description:"<strong>encoder_attention_mask</strong> (<code>torch.Tensor</code>) — An attention mask.",name:"encoder_attention_mask"}],returnDescription:`
<p>The inverted attention mask.</p>
`,returnType:`
<p><code>torch.Tensor</code></p>
`}}),Ro=new v({props:{name:"num_parameters",anchor:"transformers.modeling_utils.ModuleUtilsMixin.num_parameters",parameters:[{name:"only_trainable",val:": bool = False"},{name:"exclude_embeddings",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L352",parametersDescription:[{anchor:"transformers.modeling_utils.ModuleUtilsMixin.num_parameters.only_trainable",description:`<strong>only_trainable</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return only the number of trainable parameters`,name:"only_trainable"},{anchor:"transformers.modeling_utils.ModuleUtilsMixin.num_parameters.exclude_embeddings",description:`<strong>exclude_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return only the number of non-embeddings parameters`,name:"exclude_embeddings"}],returnDescription:`
<p>The number of parameters.</p>
`,returnType:`
<p><code>int</code></p>
`}}),Yo=new v({props:{name:"reset_memory_hooks_state",anchor:"transformers.modeling_utils.ModuleUtilsMixin.reset_memory_hooks_state",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L194"}}),Ko=new ao({}),Jo=new v({props:{name:"class transformers.TFPreTrainedModel",anchor:"transformers.TFPreTrainedModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L686"}}),Zo=new v({props:{name:"push_to_hub",anchor:"transformers.file_utils.PushToHubMixin.push_to_hub",parameters:[{name:"repo_path_or_name",val:": typing.Optional[str] = None"},{name:"repo_url",val:": typing.Optional[str] = None"},{name:"use_temp_dir",val:": bool = False"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"organization",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"**model_card_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842",parametersDescription:[{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name",description:`<strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your model in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.`,name:"repo_path_or_name"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_url",description:`<strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.`,name:"repo_url"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir",description:`<strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.`,name:"use_temp_dir"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add model"</code>.`,name:"commit_message"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.organization",description:`<strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your model (you must be a member of this organization).`,name:"organization"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).`,name:"private"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token",description:`<strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.`,name:"use_auth_token"}],returnDescription:`
<p>The url of the commit of your model in the given repository.</p>
`,returnType:`
<p><code>str</code></p>
`}}),tn=new Q({props:{code:`from transformers import TFAutoModel
model = TFAutoModel.from_pretrained("bert-base-cased")
# Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
model.push_to_hub("my-finetuned-bert")
# Push the model to your namespace with the name "my-finetuned-bert" with no local clone.
model.push_to_hub("my-finetuned-bert", use_temp_dir=True)
# Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
model.push_to_hub("my-finetuned-bert", organization="huggingface")
# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
model.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel
model = TFAutoModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" with no local clone.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)`}}),on=new v({props:{name:"compile",anchor:"transformers.TFPreTrainedModel.compile",parameters:[{name:"optimizer",val:" = 'rmsprop'"},{name:"loss",val:" = 'passthrough'"},{name:"metrics",val:" = None"},{name:"loss_weights",val:" = None"},{name:"weighted_metrics",val:" = None"},{name:"run_eagerly",val:" = None"},{name:"steps_per_execution",val:" = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L877"}}),nn=new v({props:{name:"from_pretrained",anchor:"transformers.TFPreTrainedModel.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1439",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
<li><code>None</code> if you are both providing the configuration and state dictionary (resp. with keyword
arguments <code>config</code> and <code>state_dict</code>).</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFPreTrainedModel.from_pretrained.model_args",description:`<strong>model_args</strong> (sequence of positional arguments, <em>optional</em>) —
All remaining positional arguments will be passed to the underlying model’s <code>__init__</code> method.`,name:"model_args"},{anchor:"transformers.TFPreTrainedModel.from_pretrained.config",description:`<strong>config</strong> (<code>Union[PretrainedConfig, str]</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>an instance of a class derived from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>,</li>
<li>a string valid as input to <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>.</li>
</ul>`,name:"config"}]}}),jt=new fl({props:{$$slots:{default:[lw]},$$scope:{ctx:S}}}),sn=new Q({props:{code:`from transformers import BertConfig, TFBertModel
# Download model and configuration from huggingface.co and cache.
model = TFBertModel.from_pretrained("bert-base-uncased")
# Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
model = TFBertModel.from_pretrained("./test/saved_model/")
# Update configuration during loading.
model = TFBertModel.from_pretrained("bert-base-uncased", output_attentions=True)
assert model.config.output_attentions == True
# Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable).
config = BertConfig.from_json_file("./pt_model/my_pt_model_config.json")
model = TFBertModel.from_pretrained("./pt_model/my_pytorch_model.bin", from_pt=True, config=config)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, TFBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Update configuration during loading.</span>
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> model.config.output_attentions == <span class="hljs-literal">True</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>config = BertConfig.from_json_file(<span class="hljs-string">"./pt_model/my_pt_model_config.json"</span>)
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"./pt_model/my_pytorch_model.bin"</span>, from_pt=<span class="hljs-literal">True</span>, config=config)`}}),ln=new v({props:{name:"get_bias",anchor:"transformers.TFPreTrainedModel.get_bias",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1123",returnDescription:`
<p>The weights representing the bias, None if not an LM model.</p>
`,returnType:`
<p><code>tf.Variable</code></p>
`}}),dn=new v({props:{name:"get_input_embeddings",anchor:"transformers.TFPreTrainedModel.get_input_embeddings",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L796",returnDescription:`
<p>The embeddings layer mapping vocabulary to hidden states.</p>
`,returnType:`
<p><code>tf.Variable</code></p>
`}}),mn=new v({props:{name:"get_lm_head",anchor:"transformers.TFPreTrainedModel.get_lm_head",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1156",returnDescription:`
<p>The LM head layer if the model has one, None if not.</p>
`,returnType:`
<p><code>tf.keras.layers.Layer</code></p>
`}}),cn=new v({props:{name:"get_output_embeddings",anchor:"transformers.TFPreTrainedModel.get_output_embeddings",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1063",returnDescription:`
<p>The new weights mapping vocabulary to hidden states.</p>
`,returnType:`
<p><code>tf.Variable</code></p>
`}}),pn=new v({props:{name:"get_output_layer_with_bias",anchor:"transformers.TFPreTrainedModel.get_output_layer_with_bias",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1100",returnDescription:`
<p>The layer that handles the bias, None if not an LM model.</p>
`,returnType:`
<p><code>tf.keras.layers.Layer</code></p>
`}}),hn=new v({props:{name:"get_prefix_bias_name",anchor:"transformers.TFPreTrainedModel.get_prefix_bias_name",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1113",returnDescription:`
<p>The _prefix name of the bias.</p>
`,returnType:`
<p><code>str</code></p>
`}}),fn=new v({props:{name:"load_repo_checkpoint",anchor:"transformers.TFPreTrainedModel.load_repo_checkpoint",parameters:[{name:"repo_path_or_name",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L823",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.load_repo_checkpoint.repo_path_or_name",description:`<strong>repo_path_or_name</strong> (<code>str</code>) —
Can either be a repository name for your {object} in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder).`,name:"repo_path_or_name"}],returnDescription:`
<p>A dictionary of extra metadata from the checkpoint, most commonly an \u201Cepoch\u201D count.</p>
`,returnType:`
<p><code>dict</code></p>
`}}),un=new v({props:{name:"prune_heads",anchor:"transformers.TFPreTrainedModel.prune_heads",parameters:[{name:"heads_to_prune",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1365",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.prune_heads.heads_to_prune",description:`<strong>heads_to_prune</strong> (<code>Dict[int, List[int]]</code>) —
Dictionary with keys being selected layer indices (<code>int</code>) and associated values being the list of heads
to prune in said layer (list of <code>int</code>). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on
layer 1 and heads 2 and 3 on layer 2.`,name:"heads_to_prune"}]}}),gn=new v({props:{name:"resize_token_embeddings",anchor:"transformers.TFPreTrainedModel.resize_token_embeddings",parameters:[{name:"new_num_tokens",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1165",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.resize_token_embeddings.new_num_tokens",description:`<strong>new_num_tokens</strong> (<code>int</code>, <em>optional</em>) —
The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
vectors at the end. Reducing the size will remove vectors from the end. If not provided or <code>None</code>, just
returns a pointer to the input tokens <code>tf.Variable</code> module of the model without doing anything.`,name:"new_num_tokens"}],returnDescription:`
<p>Pointer to the input tokens Embeddings Module of the model.</p>
`,returnType:`
<p><code>tf.Variable</code></p>
`}}),vn=new v({props:{name:"save_pretrained",anchor:"transformers.TFPreTrainedModel.save_pretrained",parameters:[{name:"save_directory",val:""},{name:"saved_model",val:" = False"},{name:"version",val:" = 1"},{name:"push_to_hub",val:" = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1377",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code>) —
Directory to which to save. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"transformers.TFPreTrainedModel.save_pretrained.saved_model",description:`<strong>saved_model</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If the model has to be saved in saved model format as well or not.`,name:"saved_model"},{anchor:"transformers.TFPreTrainedModel.save_pretrained.version",description:`<strong>version</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The version of the saved model. A saved model needs to be versioned in order to be properly loaded by
TensorFlow Serving as detailed in the official documentation
<a href="https://www.tensorflow.org/tfx/serving/serving_basic" rel="nofollow">https://www.tensorflow.org/tfx/serving/serving_basic</a>`,name:"version"},{anchor:"transformers.TFPreTrainedModel.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"push_to_hub"}]}}),$n=new v({props:{name:"serving",anchor:"transformers.TFPreTrainedModel.serving",parameters:[{name:"inputs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L765",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.serving.inputs",description:`<strong>inputs</strong> (<code>Dict[str, tf.Tensor]</code>) —
The input of the saved model as a dictionary of tensors.`,name:"inputs"}]}}),wn=new v({props:{name:"serving_output",anchor:"transformers.TFPreTrainedModel.serving_output",parameters:[{name:"output",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L786",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.serving_output.output",description:`<strong>output</strong> (<code>TFBaseModelOutput</code>) —
The output returned by the model.`,name:"output"}]}}),Tn=new v({props:{name:"set_bias",anchor:"transformers.TFPreTrainedModel.set_bias",parameters:[{name:"value",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1140",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.set_bias.value",description:`<strong>value</strong> (<code>Dict[tf.Variable]</code>) —
All the new bias attached to an LM head.`,name:"value"}]}}),xn=new v({props:{name:"set_input_embeddings",anchor:"transformers.TFPreTrainedModel.set_input_embeddings",parameters:[{name:"value",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1043",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.set_input_embeddings.value",description:`<strong>value</strong> (<code>tf.Variable</code>) —
The new weights mapping hidden states to vocabulary.`,name:"value"}]}}),Pn=new v({props:{name:"set_output_embeddings",anchor:"transformers.TFPreTrainedModel.set_output_embeddings",parameters:[{name:"value",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1083",parametersDescription:[{anchor:"transformers.TFPreTrainedModel.set_output_embeddings.value",description:`<strong>value</strong> (<code>tf.Variable</code>) —
The new weights mapping hidden states to vocabulary.`,name:"value"}]}}),En=new v({props:{name:"test_step",anchor:"transformers.TFPreTrainedModel.test_step",parameters:[{name:"data",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L977"}}),Mn=new v({props:{name:"train_step",anchor:"transformers.TFPreTrainedModel.train_step",parameters:[{name:"data",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L926"}}),kn=new ao({}),jn=new v({props:{name:"class transformers.modeling_tf_utils.TFModelUtilsMixin",anchor:"transformers.modeling_tf_utils.TFModelUtilsMixin",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L83"}}),Fn=new v({props:{name:"num_parameters",anchor:"transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters",parameters:[{name:"only_trainable",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L88",parametersDescription:[{anchor:"transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters.only_trainable",description:`<strong>only_trainable</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return only the number of trainable parameters`,name:"only_trainable"}],returnDescription:`
<p>The number of parameters.</p>
`,returnType:`
<p><code>int</code></p>
`}}),qn=new ao({}),An=new v({props:{name:"class transformers.FlaxPreTrainedModel",anchor:"transformers.FlaxPreTrainedModel",parameters:[{name:"config",val:": PretrainedConfig"},{name:"module",val:": Module"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L72"}}),Cn=new v({props:{name:"push_to_hub",anchor:"transformers.file_utils.PushToHubMixin.push_to_hub",parameters:[{name:"repo_path_or_name",val:": typing.Optional[str] = None"},{name:"repo_url",val:": typing.Optional[str] = None"},{name:"use_temp_dir",val:": bool = False"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"organization",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"**model_card_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842",parametersDescription:[{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name",description:`<strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your model in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.`,name:"repo_path_or_name"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_url",description:`<strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.`,name:"repo_url"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir",description:`<strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.`,name:"use_temp_dir"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add model"</code>.`,name:"commit_message"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.organization",description:`<strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your model (you must be a member of this organization).`,name:"organization"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).`,name:"private"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token",description:`<strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.`,name:"use_auth_token"}],returnDescription:`
<p>The url of the commit of your model in the given repository.</p>
`,returnType:`
<p><code>str</code></p>
`}}),Ln=new Q({props:{code:`from transformers import FlaxAutoModel
model = FlaxAutoModel.from_pretrained("bert-base-cased")
# Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
model.push_to_hub("my-finetuned-bert")
# Push the model to your namespace with the name "my-finetuned-bert" with no local clone.
model.push_to_hub("my-finetuned-bert", use_temp_dir=True)
# Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
model.push_to_hub("my-finetuned-bert", organization="huggingface")
# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
model.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxAutoModel
model = FlaxAutoModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" with no local clone.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)`}}),zn=new v({props:{name:"from_pretrained",anchor:"transformers.FlaxPreTrainedModel.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{name:"dtype",val:": dtype = <class 'jax._src.numpy.lax_numpy.float32'>"},{name:"*model_args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L296",parametersDescription:[{anchor:"transformers.FlaxPreTrainedModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>pt index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In this case,
<code>from_pt</code> should be set to <code>True</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxPreTrainedModel.from_pretrained.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) —
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"},{anchor:"transformers.FlaxPreTrainedModel.from_pretrained.model_args",description:`<strong>model_args</strong> (sequence of positional arguments, <em>optional</em>) —
All remaining positional arguments will be passed to the underlying model’s <code>__init__</code> method.`,name:"model_args"},{anchor:"transformers.FlaxPreTrainedModel.from_pretrained.config",description:`<strong>config</strong> (<code>Union[PretrainedConfig, str, os.PathLike]</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>an instance of a class derived from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>,</li>
<li>a string or path valid as input to <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>.</li>
</ul>`,name:"config"}]}}),Nn=new Q({props:{code:`from transformers import BertConfig, FlaxBertModel
# Download model and configuration from huggingface.co and cache.
model = FlaxBertModel.from_pretrained("bert-base-cased")
# Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
model = FlaxBertModel.from_pretrained("./test/saved_model/")
# Loading from a PyTorch checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
config = BertConfig.from_json_file("./pt_model/config.json")
model = FlaxBertModel.from_pretrained("./pt_model/pytorch_model.bin", from_pt=True, config=config)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>config = BertConfig.from_json_file(<span class="hljs-string">"./pt_model/config.json"</span>)
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"./pt_model/pytorch_model.bin"</span>, from_pt=<span class="hljs-literal">True</span>, config=config)`}}),Hn=new v({props:{name:"register_for_auto_class",anchor:"transformers.FlaxPreTrainedModel.register_for_auto_class",parameters:[{name:"auto_class",val:" = 'FlaxAutoModel'"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L716",parametersDescription:[{anchor:"transformers.FlaxPreTrainedModel.register_for_auto_class.auto_class",description:`<strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"FlaxAutoModel"</code>) —
The auto class to register this new model with.`,name:"auto_class"}]}}),Yt=new fl({props:{warning:"{true}",$$slots:{default:[dw]},$$scope:{ctx:S}}}),Xn=new v({props:{name:"save_pretrained",anchor:"transformers.FlaxPreTrainedModel.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"params",val:" = None"},{name:"push_to_hub",val:" = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L659",parametersDescription:[{anchor:"transformers.FlaxPreTrainedModel.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory to which to save. Will be created if it doesn’t exist.`,name:"save_directory"},{anchor:"transformers.FlaxPreTrainedModel.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"push_to_hub"}]}}),Vn=new v({props:{name:"to_bf16",anchor:"transformers.FlaxPreTrainedModel.to_bf16",parameters:[{name:"params",val:": typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict]"},{name:"mask",val:": typing.Any = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L191",parametersDescription:[{anchor:"transformers.FlaxPreTrainedModel.to_bf16.params",description:`<strong>params</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> of model parameters.`,name:"params"},{anchor:"transformers.FlaxPreTrainedModel.to_bf16.mask",description:`<strong>mask</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> with same structure as the <code>params</code> tree. The leaves should be booleans, <code>True</code> for params
you want to cast, and should be <code>False</code> for those you want to skip.`,name:"mask"}]}}),Sn=new Q({props:{code:`from transformers import FlaxBertModel
# load model
model = FlaxBertModel.from_pretrained("bert-base-cased")
# By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision
model.params = model.to_bf16(model.params)
# If you want don't want to cast certain parameters (for example layer norm bias and scale)
# then pass the mask as follows
from flax import traverse_util
model = FlaxBertModel.from_pretrained("bert-base-cased")
flat_params = traverse_util.flatten_dict(model.params)
mask = {
path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
for path in flat_params
}
mask = traverse_util.unflatten_dict(mask)
model.params = model.to_bf16(model.params, mask)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># load model</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision</span>
<span class="hljs-meta">>>> </span>model.params = model.to_bf16(model.params)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># If you want don't want to cast certain parameters (for example layer norm bias and scale)</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># then pass the mask as follows</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> flax <span class="hljs-keyword">import</span> traverse_util
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span>flat_params = traverse_util.flatten_dict(model.params)
<span class="hljs-meta">>>> </span>mask = {
<span class="hljs-meta">... </span> path: (path[-<span class="hljs-number">2</span>] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"bias"</span>) <span class="hljs-keyword">and</span> path[-<span class="hljs-number">2</span>:] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"scale"</span>))
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> flat_params
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span>mask = traverse_util.unflatten_dict(mask)
<span class="hljs-meta">>>> </span>model.params = model.to_bf16(model.params, mask)`}}),Wn=new v({props:{name:"to_fp16",anchor:"transformers.FlaxPreTrainedModel.to_fp16",parameters:[{name:"params",val:": typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict]"},{name:"mask",val:": typing.Any = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L257",parametersDescription:[{anchor:"transformers.FlaxPreTrainedModel.to_fp16.params",description:`<strong>params</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> of model parameters.`,name:"params"},{anchor:"transformers.FlaxPreTrainedModel.to_fp16.mask",description:`<strong>mask</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> with same structure as the <code>params</code> tree. The leaves should be booleans, <code>True</code> for params
you want to cast, and should be <code>False</code> for those you want to skip`,name:"mask"}]}}),Gn=new Q({props:{code:`from transformers import FlaxBertModel
# load model
model = FlaxBertModel.from_pretrained("bert-base-cased")
# By default, the model params will be in fp32, to cast these to float16
model.params = model.to_fp16(model.params)
# If you want don't want to cast certain parameters (for example layer norm bias and scale)
# then pass the mask as follows
from flax import traverse_util
model = FlaxBertModel.from_pretrained("bert-base-cased")
flat_params = traverse_util.flatten_dict(model.params)
mask = {
path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
for path in flat_params
}
mask = traverse_util.unflatten_dict(mask)
model.params = model.to_fp16(model.params, mask)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># load model</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># By default, the model params will be in fp32, to cast these to float16</span>
<span class="hljs-meta">>>> </span>model.params = model.to_fp16(model.params)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># If you want don't want to cast certain parameters (for example layer norm bias and scale)</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># then pass the mask as follows</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> flax <span class="hljs-keyword">import</span> traverse_util
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span>flat_params = traverse_util.flatten_dict(model.params)
<span class="hljs-meta">>>> </span>mask = {
<span class="hljs-meta">... </span> path: (path[-<span class="hljs-number">2</span>] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"bias"</span>) <span class="hljs-keyword">and</span> path[-<span class="hljs-number">2</span>:] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"scale"</span>))
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> flat_params
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span>mask = traverse_util.unflatten_dict(mask)
<span class="hljs-meta">>>> </span>model.params = model.to_fp16(model.params, mask)`}}),Rn=new v({props:{name:"to_fp32",anchor:"transformers.FlaxPreTrainedModel.to_fp32",parameters:[{name:"params",val:": typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict]"},{name:"mask",val:": typing.Any = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L230",parametersDescription:[{anchor:"transformers.FlaxPreTrainedModel.to_fp32.params",description:`<strong>params</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> of model parameters.`,name:"params"},{anchor:"transformers.FlaxPreTrainedModel.to_fp32.mask",description:`<strong>mask</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> with same structure as the <code>params</code> tree. The leaves should be booleans, <code>True</code> for params
you want to cast, and should be <code>False</code> for those you want to skip`,name:"mask"}]}}),Yn=new Q({props:{code:`from transformers import FlaxBertModel
# Download model and configuration from huggingface.co
model = FlaxBertModel.from_pretrained("bert-base-cased")
# By default, the model params will be in fp32, to illustrate the use of this method,
# we'll first cast to fp16 and back to fp32
model.params = model.to_f16(model.params)
# now cast back to fp32
model.params = model.to_fp32(model.params)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># By default, the model params will be in fp32, to illustrate the use of this method,</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># we'll first cast to fp16 and back to fp32</span>
<span class="hljs-meta">>>> </span>model.params = model.to_f16(model.params)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># now cast back to fp32</span>
<span class="hljs-meta">>>> </span>model.params = model.to_fp32(model.params)`}}),Kn=new ao({}),Jn=new v({props:{name:"class transformers.file_utils.PushToHubMixin",anchor:"transformers.file_utils.PushToHubMixin",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2837"}}),Qn=new v({props:{name:"push_to_hub",anchor:"transformers.file_utils.PushToHubMixin.push_to_hub",parameters:[{name:"repo_path_or_name",val:": typing.Optional[str] = None"},{name:"repo_url",val:": typing.Optional[str] = None"},{name:"use_temp_dir",val:": bool = False"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"organization",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"**model_card_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842",parametersDescription:[{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name",description:`<strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your {object} in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.`,name:"repo_path_or_name"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_url",description:`<strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.`,name:"repo_url"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir",description:`<strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.`,name:"use_temp_dir"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add {object}"</code>.`,name:"commit_message"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.organization",description:`<strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your {object} (you must be a member of this organization).`,name:"organization"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).`,name:"private"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token",description:`<strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.`,name:"use_auth_token"}],returnDescription:`
<p>The url of the commit of your {object} in the given repository.</p>
`,returnType:`
<p><code>str</code></p>
`}}),er=new Q({props:{code:`from transformers import {object_class}
{object} = {object_class}.from_pretrained("bert-base-cased")
# Push the {object} to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
{object}.push_to_hub("my-finetuned-bert")
# Push the {object} to your namespace with the name "my-finetuned-bert" with no local clone.
{object}.push_to_hub("my-finetuned-bert", use_temp_dir=True)
# Push the {object} to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
{object}.push_to_hub("my-finetuned-bert", organization="huggingface")
# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
{object}.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> {object_class}
{<span class="hljs-built_in">object</span>} = {object_class}.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the {object} to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the {object} to your namespace with the name "my-finetuned-bert" with no local clone.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the {object} to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)`}}),{c(){w=o("meta"),k=l(),x=o("h1"),E=o("a"),W=o("span"),c(A.$$.fragment),L=l(),de=o("span"),Jd=s("Models"),ul=l(),Z=o("p"),Qd=s("The base classes "),sr=o("a"),Zd=s("PreTrainedModel"),em=s(", "),ir=o("a"),tm=s("TFPreTrainedModel"),om=s(`, and
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weights are discarded.`),Gl.forEach(t),np=d(I),p(st.$$.fragment,I),rp=d(I),p(it.$$.fragment,I),ap=d(I),La=n(I,"P",{});var b1=r(La);sp=i(b1,"Examples:"),b1.forEach(t),ip=d(I),p(_o.$$.fragment,I),I.forEach(t),lp=d(P),lt=n(P,"DIV",{class:!0});var Rl=r(lt);p(bo.$$.fragment,Rl),dp=d(Rl),za=n(Rl,"P",{});var v1=r(za);mp=i(v1,"Returns the model\u2019s input embeddings."),v1.forEach(t),Rl.forEach(t),cp=d(P),dt=n(P,"DIV",{class:!0});var Yl=r(dt);p(vo.$$.fragment,Yl),pp=d(Yl),Oa=n(Yl,"P",{});var y1=r(Oa);hp=i(y1,"Returns the model\u2019s output embeddings."),y1.forEach(t),Yl.forEach(t),fp=d(P),be=n(P,"DIV",{class:!0});var Cr=r(be);p(yo.$$.fragment,Cr),up=d(Cr),Ua=n(Cr,"P",{});var $1=r(Ua);gp=i($1,"Deactivates gradient checkpointing for the current model."),$1.forEach(t),_p=d(Cr),Na=n(Cr,"P",{});var w1=r(Na);bp=i(w1,`Note that in other frameworks this feature can be referred to as \u201Cactivation checkpointing\u201D or \u201Ccheckpoint
activations\u201D.`),w1.forEach(t),Cr.forEach(t),vp=d(P),ve=n(P,"DIV",{class:!0});var Ir=r(ve);p($o.$$.fragment,Ir),yp=d(Ir),Ha=n(Ir,"P",{});var T1=r(Ha);$p=i(T1,"Activates gradient checkpointing for the current model."),T1.forEach(t),wp=d(Ir),Xa=n(Ir,"P",{});var x1=r(Xa);Tp=i(x1,`Note that in other frameworks this feature can be referred to as \u201Cactivation checkpointing\u201D or \u201Ccheckpoint
activations\u201D.`),x1.forEach(t),Ir.forEach(t),xp=d(P),mt=n(P,"DIV",{class:!0});var Kl=r(mt);p(wo.$$.fragment,Kl),Pp=d(Kl),Ba=n(Kl,"P",{});var P1=r(Ba);Ep=i(P1,"If needed prunes and maybe initializes weights."),P1.forEach(t),Kl.forEach(t),Mp=d(P),ct=n(P,"DIV",{class:!0});var Jl=r(ct);p(To.$$.fragment,Jl),kp=d(Jl),Va=n(Jl,"P",{});var E1=r(Va);jp=i(E1,`A method executed at the end of each Transformer model initialization, to execute code that needs the model\u2019s
modules properly initialized (such as weight initialization).`),E1.forEach(t),Jl.forEach(t),Dp=d(P),pt=n(P,"DIV",{class:!0});var Ql=r(pt);p(xo.$$.fragment,Ql),Fp=d(Ql),Sa=n(Ql,"P",{});var M1=r(Sa);qp=i(M1,"Prunes heads of the base model."),M1.forEach(t),Ql.forEach(t),Ap=d(P),ye=n(P,"DIV",{class:!0});var Lr=r(ye);p(Po.$$.fragment,Lr),Cp=d(Lr),Wa=n(Lr,"P",{});var k1=r(Wa);Ip=i(k1,`Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.`),k1.forEach(t),Lp=d(Lr),p(ht.$$.fragment,Lr),Lr.forEach(t),zp=d(P),$e=n(P,"DIV",{class:!0});var zr=r($e);p(Eo.$$.fragment,zr),Op=d(zr),Mo=n(zr,"P",{});var Zl=r(Mo);Up=i(Zl,"Resizes input token embeddings matrix of the model if "),Ga=n(Zl,"CODE",{});var j1=r(Ga);Np=i(j1,"new_num_tokens != config.vocab_size"),j1.forEach(t),Hp=i(Zl,"."),Zl.forEach(t),Xp=d(zr),ko=n(zr,"P",{});var ed=r(ko);Bp=i(ed,"Takes care of tying weights embeddings afterwards if the model class has a "),Ra=n(ed,"CODE",{});var D1=r(Ra);Vp=i(D1,"tie_weights()"),D1.forEach(t),Sp=i(ed," method."),ed.forEach(t),zr.forEach(t),Wp=d(P),ft=n(P,"DIV",{class:!0});var td=r(ft);p(jo.$$.fragment,td),Gp=d(td),Do=n(td,"P",{});var od=r(Do);Rp=i(od,`Save a model and its configuration file to a directory, so that it can be re-loaded using the
`),Ya=n(od,"CODE",{});var F1=r(Ya);Yp=i(F1,"[from_pretrained()](/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel.from_pretrained)"),F1.forEach(t),Kp=i(od," class method."),od.forEach(t),td.forEach(t),Jp=d(P),ut=n(P,"DIV",{class:!0});var nd=r(ut);p(Fo.$$.fragment,nd),Qp=d(nd),Ka=n(nd,"P",{});var q1=r(Ka);Zp=i(q1,"Set model\u2019s input embeddings."),q1.forEach(t),nd.forEach(t),eh=d(P),we=n(P,"DIV",{class:!0});var Or=r(we);p(qo.$$.fragment,Or),th=d(Or),Ja=n(Or,"P",{});var A1=r(Ja);oh=i(A1,"Tie the weights between the input embeddings and the output embeddings."),A1.forEach(t),nh=d(Or),Ao=n(Or,"P",{});var rd=r(Ao);rh=i(rd,"If the "),Qa=n(rd,"CODE",{});var C1=r(Qa);ah=i(C1,"torchscript"),C1.forEach(t),sh=i(rd,` flag is set in the configuration, can\u2019t handle parameter sharing so we are cloning the
weights instead.`),rd.forEach(t),Or.forEach(t),P.forEach(t),$l=d(a),yr=n(a,"A",{id:!0}),r(yr).forEach(t),wl=d(a),Ne=n(a,"H3",{class:!0});var ad=r(Ne);gt=n(ad,"A",{id:!0,class:!0,href:!0});var I1=r(gt);Za=n(I1,"SPAN",{});var L1=r(Za);p(Co.$$.fragment,L1),L1.forEach(t),I1.forEach(t),ih=d(ad),es=n(ad,"SPAN",{});var z1=r(es);lh=i(z1,"Model Instantiation dtype"),z1.forEach(t),ad.forEach(t),Tl=d(a),te=n(a,"P",{});var Zt=r(te);dh=i(Zt,"Under Pytorch a model normally gets instantiated with "),ts=n(Zt,"CODE",{});var O1=r(ts);mh=i(O1,"torch.float32"),O1.forEach(t),ch=i(Zt,` format. This can be an issue if one tries to
load a model whose weights are in fp16, since it\u2019d require twice as much memory. To overcome this limitation, you can
either explicitly pass the desired `),os=n(Zt,"CODE",{});var U1=r(os);ph=i(U1,"dtype"),U1.forEach(t),hh=i(Zt," using "),ns=n(Zt,"CODE",{});var N1=r(ns);fh=i(N1,"torch_dtype"),N1.forEach(t),uh=i(Zt," argument:"),Zt.forEach(t),xl=d(a),p(Io.$$.fragment,a),Pl=d(a),Te=n(a,"P",{});var Ur=r(Te);gh=i(Ur,"or, if you want the model to always load in the most optimal memory pattern, you can use the special value "),rs=n(Ur,"CODE",{});var H1=r(rs);_h=i(H1,'"auto"'),H1.forEach(t),bh=i(Ur,`,
and then `),as=n(Ur,"CODE",{});var X1=r(as);vh=i(X1,"dtype"),X1.forEach(t),yh=i(Ur," will be automatically derived from the model\u2019s weights:"),Ur.forEach(t),El=d(a),p(Lo.$$.fragment,a),Ml=d(a),_t=n(a,"P",{});var sd=r(_t);$h=i(sd,"Models instantiated from scratch can also be told which "),ss=n(sd,"CODE",{});var B1=r(ss);wh=i(B1,"dtype"),B1.forEach(t),Th=i(sd," to use with:"),sd.forEach(t),kl=d(a),p(zo.$$.fragment,a),jl=d(a),$r=n(a,"P",{});var V1=r($r);xh=i(V1,"Due to Pytorch design, this functionality is only available for floating dtypes."),V1.forEach(t),Dl=d(a),He=n(a,"H2",{class:!0});var id=r(He);bt=n(id,"A",{id:!0,class:!0,href:!0});var S1=r(bt);is=n(S1,"SPAN",{});var W1=r(is);p(Oo.$$.fragment,W1),W1.forEach(t),S1.forEach(t),Ph=d(id),ls=n(id,"SPAN",{});var G1=r(ls);Eh=i(G1,"ModuleUtilsMixin"),G1.forEach(t),id.forEach(t),Fl=d(a),j=n(a,"DIV",{class:!0});var q=r(j);p(Uo.$$.fragment,q),Mh=d(q),No=n(q,"P",{});var ld=r(No);kh=i(ld,"A few utilities for "),ds=n(ld,"CODE",{});var R1=r(ds);jh=i(R1,"torch.nn.Modules"),R1.forEach(t),Dh=i(ld,", to be used as a mixin."),ld.forEach(t),Fh=d(q),xe=n(q,"DIV",{class:!0});var Nr=r(xe);p(Ho.$$.fragment,Nr),qh=d(Nr),ms=n(Nr,"P",{});var Y1=r(ms);Ah=i(Y1,"Add a memory hook before and after each sub-module forward pass to record increase in memory consumption."),Y1.forEach(t),Ch=d(Nr),Xe=n(Nr,"P",{});var Hr=r(Xe);Ih=i(Hr,"Increase in memory consumption is stored in a "),cs=n(Hr,"CODE",{});var K1=r(cs);Lh=i(K1,"mem_rss_diff"),K1.forEach(t),zh=i(Hr,` attribute for each module and can be reset to zero
with `),ps=n(Hr,"CODE",{});var J1=r(ps);Oh=i(J1,"model.reset_memory_hooks_state()"),J1.forEach(t),Uh=i(Hr,"."),Hr.forEach(t),Nr.forEach(t),Nh=d(q),vt=n(q,"DIV",{class:!0});var dd=r(vt);p(Xo.$$.fragment,dd),Hh=d(dd),hs=n(dd,"P",{});var Q1=r(hs);Xh=i(Q1,"Helper function to estimate the total number of tokens from the model inputs."),Q1.forEach(t),dd.forEach(t),Bh=d(q),yt=n(q,"DIV",{class:!0});var md=r(yt);p(Bo.$$.fragment,md),Vh=d(md),Be=n(md,"P",{});var Xr=r(Be);Sh=i(Xr,`Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a
batch with this transformer model. Default approximation neglects the quadratic dependency on the number of
tokens (valid if `),fs=n(Xr,"CODE",{});var Z1=r(fs);Wh=i(Z1,"12 * d_model << sequence_length"),Z1.forEach(t),Gh=i(Xr,") as laid out in "),Vo=n(Xr,"A",{href:!0,rel:!0});var ey=r(Vo);Rh=i(ey,`this
paper`),ey.forEach(t),Yh=i(Xr,` section 2.1. Should be overridden for transformers with parameter
re-use e.g. Albert or Universal Transformers, or if doing long-range modeling with very high sequence lengths.`),Xr.forEach(t),md.forEach(t),Kh=d(q),$t=n(q,"DIV",{class:!0});var cd=r($t);p(So.$$.fragment,cd),Jh=d(cd),us=n(cd,"P",{});var ty=r(us);Qh=i(ty,"Makes broadcastable attention and causal masks so that future and masked tokens are ignored."),ty.forEach(t),cd.forEach(t),Zh=d(q),wt=n(q,"DIV",{class:!0});var pd=r(wt);p(Wo.$$.fragment,pd),ef=d(pd),gs=n(pd,"P",{});var oy=r(gs);tf=i(oy,"Prepare the head mask if needed."),oy.forEach(t),pd.forEach(t),of=d(q),Tt=n(q,"DIV",{class:!0});var hd=r(Tt);p(Go.$$.fragment,hd),nf=d(hd),_s=n(hd,"P",{});var ny=r(_s);rf=i(ny,"Invert an attention mask (e.g., switches 0. and 1.)."),ny.forEach(t),hd.forEach(t),af=d(q),xt=n(q,"DIV",{class:!0});var fd=r(xt);p(Ro.$$.fragment,fd),sf=d(fd),bs=n(fd,"P",{});var ry=r(bs);lf=i(ry,"Get number of (optionally, trainable or non-embeddings) parameters in the module."),ry.forEach(t),fd.forEach(t),df=d(q),Pt=n(q,"DIV",{class:!0});var ud=r(Pt);p(Yo.$$.fragment,ud),mf=d(ud),Ve=n(ud,"P",{});var Br=r(Ve);cf=i(Br,"Reset the "),vs=n(Br,"CODE",{});var ay=r(vs);pf=i(ay,"mem_rss_diff"),ay.forEach(t),hf=i(Br," attribute of each module (see "),wr=n(Br,"A",{href:!0});var sy=r(wr);ff=i(sy,"add_memory_hooks()"),sy.forEach(t),uf=i(Br,")."),Br.forEach(t),ud.forEach(t),q.forEach(t),ql=d(a),Se=n(a,"H2",{class:!0});var gd=r(Se);Et=n(gd,"A",{id:!0,class:!0,href:!0});var iy=r(Et);ys=n(iy,"SPAN",{});var ly=r(ys);p(Ko.$$.fragment,ly),ly.forEach(t),iy.forEach(t),gf=d(gd),$s=n(gd,"SPAN",{});var dy=r($s);_f=i(dy,"TFPreTrainedModel"),dy.forEach(t),gd.forEach(t),Al=d(a),b=n(a,"DIV",{class:!0});var $=r(b);p(Jo.$$.fragment,$),bf=d($),ws=n($,"P",{});var my=r(ws);vf=i(my,"Base class for all TF models."),my.forEach(t),yf=d($),Tr=n($,"P",{});var bv=r(Tr);xr=n(bv,"A",{href:!0});var cy=r(xr);$f=i(cy,"TFPreTrainedModel"),cy.forEach(t),wf=i(bv,` takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models as well as a few methods common to all models to:`),bv.forEach(t),Tf=d($),Qo=n($,"UL",{});var _d=r(Qo);Ts=n(_d,"LI",{});var py=r(Ts);xf=i(py,"resize the input embeddings,"),py.forEach(t),Pf=d(_d),xs=n(_d,"LI",{});var hy=r(xs);Ef=i(hy,"prune heads in the self-attention heads."),hy.forEach(t),_d.forEach(t),Mf=d($),Ps=n($,"P",{});var fy=r(Ps);kf=i(fy,"Class attributes (overridden by derived classes):"),fy.forEach(t),jf=d($),We=n($,"UL",{});var Vr=r(We);Pe=n(Vr,"LI",{});var rr=r(Pe);Es=n(rr,"STRONG",{});var uy=r(Es);Df=i(uy,"config_class"),uy.forEach(t),Ff=i(rr," ("),Pr=n(rr,"A",{href:!0});var gy=r(Pr);qf=i(gy,"PretrainedConfig"),gy.forEach(t),Af=i(rr,") \u2014 A subclass of "),Er=n(rr,"A",{href:!0});var _y=r(Er);Cf=i(_y,"PretrainedConfig"),_y.forEach(t),If=i(rr,` to use as configuration class
for this model architecture.`),rr.forEach(t),Lf=d(Vr),Mt=n(Vr,"LI",{});var pl=r(Mt);Ms=n(pl,"STRONG",{});var by=r(Ms);zf=i(by,"base_model_prefix"),by.forEach(t),Of=i(pl," ("),ks=n(pl,"CODE",{});var vy=r(ks);Uf=i(vy,"str"),vy.forEach(t),Nf=i(pl,`) \u2014 A string indicating the attribute associated to the base model in derived
classes of the same architecture adding modules on top of the base model.`),pl.forEach(t),Hf=d(Vr),N=n(Vr,"LI",{});var fe=r(N);js=n(fe,"STRONG",{});var yy=r(js);Xf=i(yy,"main_input_name"),yy.forEach(t),Bf=i(fe," ("),Ds=n(fe,"CODE",{});var $y=r(Ds);Vf=i($y,"str"),$y.forEach(t),Sf=i(fe,") \u2014 The name of the principal input to the model (often "),Fs=n(fe,"CODE",{});var wy=r(Fs);Wf=i(wy,"input_ids"),wy.forEach(t),Gf=i(fe,` for NLP
models, `),qs=n(fe,"CODE",{});var Ty=r(qs);Rf=i(Ty,"pixel_values"),Ty.forEach(t),Yf=i(fe," for vision models and "),As=n(fe,"CODE",{});var xy=r(As);Kf=i(xy,"input_values"),xy.forEach(t),Jf=i(fe," for speech models)."),fe.forEach(t),Vr.forEach(t),Qf=d($),oe=n($,"DIV",{class:!0});var eo=r(oe);p(Zo.$$.fragment,eo),Zf=d(eo),en=n(eo,"P",{});var bd=r(en);eu=i(bd,`Upload the model checkpoint to the \u{1F917} Model Hub while synchronizing a local clone of the repo in
`),Cs=n(bd,"CODE",{});var Py=r(Cs);tu=i(Py,"repo_path_or_name"),Py.forEach(t),ou=i(bd,"."),bd.forEach(t),nu=d(eo),Is=n(eo,"P",{});var Ey=r(Is);ru=i(Ey,"Examples:"),Ey.forEach(t),au=d(eo),p(tn.$$.fragment,eo),eo.forEach(t),su=d($),kt=n($,"DIV",{class:!0});var vd=r(kt);p(on.$$.fragment,vd),iu=d(vd),Ls=n(vd,"P",{});var My=r(Ls);lu=i(My,`This is a thin wrapper that sets the model\u2019s loss output head as the loss if the user does not specify a loss
function themselves.`),My.forEach(t),vd.forEach(t),du=d($),C=n($,"DIV",{class:!0});var V=r(C);p(nn.$$.fragment,V),mu=d(V),zs=n(V,"P",{});var ky=r(zs);cu=i(ky,"Instantiate a pretrained TF 2.0 model from a pre-trained model configuration."),ky.forEach(t),pu=d(V),rn=n(V,"P",{});var yd=r(rn);hu=i(yd,"The warning "),Os=n(yd,"EM",{});var jy=r(Os);fu=i(jy,"Weights from XXX not initialized from pretrained model"),jy.forEach(t),uu=i(yd,` means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.`),yd.forEach(t),gu=d(V),an=n(V,"P",{});var $d=r(an);_u=i($d,"The warning "),Us=n($d,"EM",{});var Dy=r(Us);bu=i(Dy,"Weights from XXX not used in YYY"),Dy.forEach(t),vu=i($d,` means that the layer XXX is not used by YYY, therefore those
weights are discarded.`),$d.forEach(t),yu=d(V),p(jt.$$.fragment,V),$u=d(V),Ns=n(V,"P",{});var Fy=r(Ns);wu=i(Fy,"Examples:"),Fy.forEach(t),Tu=d(V),p(sn.$$.fragment,V),V.forEach(t),xu=d($),Dt=n($,"DIV",{class:!0});var wd=r(Dt);p(ln.$$.fragment,wd),Pu=d(wd),Hs=n(wd,"P",{});var qy=r(Hs);Eu=i(qy,"Dict of bias attached to an LM head. The key represents the name of the bias attribute."),qy.forEach(t),wd.forEach(t),Mu=d($),Ft=n($,"DIV",{class:!0});var Td=r(Ft);p(dn.$$.fragment,Td),ku=d(Td),Xs=n(Td,"P",{});var Ay=r(Xs);ju=i(Ay,"Returns the model\u2019s input embeddings layer."),Ay.forEach(t),Td.forEach(t),Du=d($),qt=n($,"DIV",{class:!0});var xd=r(qt);p(mn.$$.fragment,xd),Fu=d(xd),Bs=n(xd,"P",{});var Cy=r(Bs);qu=i(Cy,"The LM Head layer. This method must be overwritten by all the models that have a lm head."),Cy.forEach(t),xd.forEach(t),Au=d($),At=n($,"DIV",{class:!0});var Pd=r(At);p(cn.$$.fragment,Pd),Cu=d(Pd),Vs=n(Pd,"P",{});var Iy=r(Vs);Iu=i(Iy,"Returns the model\u2019s output embeddings"),Iy.forEach(t),Pd.forEach(t),Lu=d($),Ct=n($,"DIV",{class:!0});var Ed=r(Ct);p(pn.$$.fragment,Ed),zu=d(Ed),Ss=n(Ed,"P",{});var Ly=r(Ss);Ou=i(Ly,`Get the layer that handles a bias attribute in case the model has an LM head with weights tied to the
embeddings`),Ly.forEach(t),Ed.forEach(t),Uu=d($),It=n($,"DIV",{class:!0});var Md=r(It);p(hn.$$.fragment,Md),Nu=d(Md),Ws=n(Md,"P",{});var zy=r(Ws);Hu=i(zy,"Get the concatenated _prefix name of the bias from the model name to the parent layer"),zy.forEach(t),Md.forEach(t),Xu=d($),Lt=n($,"DIV",{class:!0});var kd=r(Lt);p(fn.$$.fragment,kd),Bu=d(kd),Gs=n(kd,"P",{});var Oy=r(Gs);Vu=i(Oy,`Loads a saved checkpoint (model weights and optimizer state) from a repo. Returns the current epoch count when
the checkpoint was made.`),Oy.forEach(t),kd.forEach(t),Su=d($),zt=n($,"DIV",{class:!0});var jd=r(zt);p(un.$$.fragment,jd),Wu=d(jd),Rs=n(jd,"P",{});var Uy=r(Rs);Gu=i(Uy,"Prunes heads of the base model."),Uy.forEach(t),jd.forEach(t),Ru=d($),Ee=n($,"DIV",{class:!0});var Sr=r(Ee);p(gn.$$.fragment,Sr),Yu=d(Sr),_n=n(Sr,"P",{});var Dd=r(_n);Ku=i(Dd,"Resizes input token embeddings matrix of the model if "),Ys=n(Dd,"CODE",{});var Ny=r(Ys);Ju=i(Ny,"new_num_tokens != config.vocab_size"),Ny.forEach(t),Qu=i(Dd,"."),Dd.forEach(t),Zu=d(Sr),bn=n(Sr,"P",{});var Fd=r(bn);eg=i(Fd,"Takes care of tying weights embeddings afterwards if the model class has a "),Ks=n(Fd,"CODE",{});var Hy=r(Ks);tg=i(Hy,"tie_weights()"),Hy.forEach(t),og=i(Fd," method."),Fd.forEach(t),Sr.forEach(t),ng=d($),Ot=n($,"DIV",{class:!0});var qd=r(Ot);p(vn.$$.fragment,qd),rg=d(qd),yn=n(qd,"P",{});var Ad=r(yn);ag=i(Ad,`Save a model and its configuration file to a directory, so that it can be re-loaded using the
`),Mr=n(Ad,"A",{href:!0});var Xy=r(Mr);sg=i(Xy,"from_pretrained()"),Xy.forEach(t),ig=i(Ad," class method."),Ad.forEach(t),qd.forEach(t),lg=d($),Ut=n($,"DIV",{class:!0});var Cd=r(Ut);p($n.$$.fragment,Cd),dg=d(Cd),Js=n(Cd,"P",{});var By=r(Js);mg=i(By,"Method used for serving the model."),By.forEach(t),Cd.forEach(t),cg=d($),Nt=n($,"DIV",{class:!0});var Id=r(Nt);p(wn.$$.fragment,Id),pg=d(Id),Qs=n(Id,"P",{});var Vy=r(Qs);hg=i(Vy,"Prepare the output of the saved model. Each model must implement this function."),Vy.forEach(t),Id.forEach(t),fg=d($),Ht=n($,"DIV",{class:!0});var Ld=r(Ht);p(Tn.$$.fragment,Ld),ug=d(Ld),Zs=n(Ld,"P",{});var Sy=r(Zs);gg=i(Sy,"Set all the bias in the LM head."),Sy.forEach(t),Ld.forEach(t),_g=d($),Xt=n($,"DIV",{class:!0});var zd=r(Xt);p(xn.$$.fragment,zd),bg=d(zd),ei=n(zd,"P",{});var Wy=r(ei);vg=i(Wy,"Set model\u2019s input embeddings"),Wy.forEach(t),zd.forEach(t),yg=d($),Bt=n($,"DIV",{class:!0});var Od=r(Bt);p(Pn.$$.fragment,Od),$g=d(Od),ti=n(Od,"P",{});var Gy=r(ti);wg=i(Gy,"Set model\u2019s output embeddings"),Gy.forEach(t),Od.forEach(t),Tg=d($),Vt=n($,"DIV",{class:!0});var Ud=r(Vt);p(En.$$.fragment,Ud),xg=d(Ud),oi=n(Ud,"P",{});var Ry=r(oi);Pg=i(Ry,"A modification of Keras\u2019s default test_step that cleans up the printed metrics when we use a dummy loss."),Ry.forEach(t),Ud.forEach(t),Eg=d($),ne=n($,"DIV",{class:!0});var to=r(ne);p(Mn.$$.fragment,to),Mg=d(to),R=n(to,"P",{});var De=r(R);kg=i(De,"A modification of Keras\u2019s default "),ni=n(De,"CODE",{});var Yy=r(ni);jg=i(Yy,"train_step"),Yy.forEach(t),Dg=i(De,` that cleans up the printed metrics when we use a dummy loss. If
a user specifies a loss at model compile time, this function behaves as the original Keras `),ri=n(De,"CODE",{});var Ky=r(ri);Fg=i(Ky,"train_step"),Ky.forEach(t),qg=i(De,`. In
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specified metric must improve to satisfy early stopping conditions. \``,name:"early_stopping_threshold(float,"}]}}),Et=new T({props:{name:"class transformers.integrations.TensorBoardCallback",anchor:"transformers.integrations.TensorBoardCallback",parameters:[{name:"tb_writer",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L446",parametersDescription:[{anchor:"transformers.integrations.TensorBoardCallback.tb_writer",description:`<strong>tb_writer</strong> (<code>SummaryWriter</code>, <em>optional</em>) —
The writer to use. Will instantiate one if not set.`,name:"tb_writer"}]}}),$t=new T({props:{name:"class transformers.integrations.WandbCallback",anchor:"transformers.integrations.WandbCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L534"}}),Ct=new T({props:{name:"setup",anchor:"transformers.integrations.WandbCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L551"}}),Dt=new T({props:{name:"class transformers.integrations.MLflowCallback",anchor:"transformers.integrations.MLflowCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L759"}}),St=new T({props:{name:"setup",anchor:"transformers.integrations.MLflowCallback.setup",parameters:[{name:"args",val:""},{name:"state",val:""},{name:"model",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L776"}}),Pt=new T({props:{name:"class transformers.integrations.AzureMLCallback",anchor:"transformers.integrations.AzureMLCallback",parameters:[{name:"azureml_run",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L736"}}),It=new T({props:{name:"class transformers.integrations.CodeCarbonCallback",anchor:"transformers.integrations.CodeCarbonCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L919"}}),xt=new Pn({}),Ft=new T({props:{name:"class transformers.TrainerCallback",anchor:"transformers.TrainerCallback",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L159",parametersDescription:[{anchor:"transformers.TrainerCallback.args",description:`<strong>args</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>) —
The training arguments used to instantiate the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"args"},{anchor:"transformers.TrainerCallback.state",description:`<strong>state</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerState">TrainerState</a>) —
The current state of the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>.`,name:"state"},{anchor:"transformers.TrainerCallback.control",description:`<strong>control</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) —
The object that is returned to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> and can be used to make some decisions.`,name:"control"},{anchor:"transformers.TrainerCallback.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <code>torch.nn.Module</code>) —
The model being trained.`,name:"model"},{anchor:"transformers.TrainerCallback.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer used for encoding the data.`,name:"tokenizer"},{anchor:"transformers.TrainerCallback.optimizer",description:`<strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) —
The optimizer used for the training steps.`,name:"optimizer"},{anchor:"transformers.TrainerCallback.lr_scheduler",description:`<strong>lr_scheduler</strong> (<code>torch.optim.lr_scheduler.LambdaLR</code>) —
The scheduler used for setting the learning rate.`,name:"lr_scheduler"},{anchor:"transformers.TrainerCallback.train_dataloader",description:`<strong>train_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) —
The current dataloader used for training.`,name:"train_dataloader"},{anchor:"transformers.TrainerCallback.eval_dataloader",description:`<strong>eval_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) —
The current dataloader used for training.`,name:"eval_dataloader"},{anchor:"transformers.TrainerCallback.metrics",description:`<strong>metrics</strong> (<code>Dict[str, float]</code>) —
The metrics computed by the last evaluation phase.</p>
<p>Those are only accessible in the event <code>on_evaluate</code>.`,name:"metrics"},{anchor:"transformers.TrainerCallback.logs",description:`<strong>logs</strong> (<code>Dict[str, float]</code>) —
The values to log.</p>
<p>Those are only accessible in the event <code>on_log</code>.`,name:"logs"}]}}),Nt=new Wh({props:{code:`class PrinterCallback(TrainerCallback):
def on_log(self, args, state, control, logs=None, **kwargs):
_ = logs.pop("total_flos", None)
if state.is_local_process_zero:
print(logs)`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">PrinterCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">on_log</span>(<span class="hljs-params">self, args, state, control, logs=<span class="hljs-literal">None</span>, **kwargs</span>):
_ = logs.pop(<span class="hljs-string">"total_flos"</span>, <span class="hljs-literal">None</span>)
<span class="hljs-keyword">if</span> state.is_local_process_zero:
<span class="hljs-built_in">print</span>(logs)`}}),Wt=new T({props:{name:"on_epoch_begin",anchor:"transformers.TrainerCallback.on_epoch_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L227"}}),zt=new T({props:{name:"on_epoch_end",anchor:"transformers.TrainerCallback.on_epoch_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L233"}}),Bt=new T({props:{name:"on_evaluate",anchor:"transformers.TrainerCallback.on_evaluate",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L259"}}),Vt=new T({props:{name:"on_init_end",anchor:"transformers.TrainerCallback.on_init_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L209"}}),Rt=new T({props:{name:"on_log",anchor:"transformers.TrainerCallback.on_log",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L271"}}),Ht=new T({props:{name:"on_prediction_step",anchor:"transformers.TrainerCallback.on_prediction_step",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L277"}}),Ut=new T({props:{name:"on_save",anchor:"transformers.TrainerCallback.on_save",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L265"}}),Gt=new T({props:{name:"on_step_begin",anchor:"transformers.TrainerCallback.on_step_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L239"}}),Jt=new T({props:{name:"on_step_end",anchor:"transformers.TrainerCallback.on_step_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L252"}}),Yt=new T({props:{name:"on_substep_end",anchor:"transformers.TrainerCallback.on_substep_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L246"}}),Xt=new T({props:{name:"on_train_begin",anchor:"transformers.TrainerCallback.on_train_begin",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L215"}}),Kt=new T({props:{name:"on_train_end",anchor:"transformers.TrainerCallback.on_train_end",parameters:[{name:"args",val:": TrainingArguments"},{name:"state",val:": TrainerState"},{name:"control",val:": TrainerControl"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L221"}}),Qt=new Wh({props:{code:`class MyCallback(TrainerCallback):
"A callback that prints a message at the beginning of training"
def on_train_begin(self, args, state, control, **kwargs):
print("Starting training")
trainer = Trainer(
model,
args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=[MyCallback], # We can either pass the callback class this way or an instance of it (MyCallback())
)`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
<span class="hljs-string">"A callback that prints a message at the beginning of training"</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">on_train_begin</span>(<span class="hljs-params">self, args, state, control, **kwargs</span>):
<span class="hljs-built_in">print</span>(<span class="hljs-string">"Starting training"</span>)
trainer = Trainer(
model,
args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=[MyCallback], <span class="hljs-comment"># We can either pass the callback class this way or an instance of it (MyCallback())</span>
)`}}),Zt=new Wh({props:{code:`trainer = Trainer(...)
trainer.add_callback(MyCallback)
# Alternatively, we can pass an instance of the callback class
trainer.add_callback(MyCallback())`,highlighted:`trainer = Trainer(...)
trainer.add_callback(MyCallback)
<span class="hljs-comment"># Alternatively, we can pass an instance of the callback class</span>
trainer.add_callback(MyCallback())`}}),ea=new Pn({}),ta=new T({props:{name:"class transformers.TrainerState",anchor:"transformers.TrainerState",parameters:[{name:"epoch",val:": typing.Optional[float] = None"},{name:"global_step",val:": int = 0"},{name:"max_steps",val:": int = 0"},{name:"num_train_epochs",val:": int = 0"},{name:"total_flos",val:": float = 0"},{name:"log_history",val:": typing.List[typing.Dict[str, float]] = None"},{name:"best_metric",val:": typing.Optional[float] = None"},{name:"best_model_checkpoint",val:": typing.Optional[str] = None"},{name:"is_local_process_zero",val:": bool = True"},{name:"is_world_process_zero",val:": bool = True"},{name:"is_hyper_param_search",val:": bool = False"},{name:"trial_name",val:": str = None"},{name:"trial_params",val:": typing.Dict[str, typing.Union[str, float, int, bool]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L35",parametersDescription:[{anchor:"transformers.TrainerState.epoch",description:`<strong>epoch</strong> (<code>float</code>, <em>optional</em>) —
Only set during training, will represent the epoch the training is at (the decimal part being the
percentage of the current epoch completed).`,name:"epoch"},{anchor:"transformers.TrainerState.global_step",description:`<strong>global_step</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
During training, represents the number of update steps completed.`,name:"global_step"},{anchor:"transformers.TrainerState.max_steps",description:`<strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
The number of update steps to do during the current training.`,name:"max_steps"},{anchor:"transformers.TrainerState.total_flos",description:`<strong>total_flos</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The total number of floating operations done by the model since the beginning of training (stored as floats
to avoid overflow).`,name:"total_flos"},{anchor:"transformers.TrainerState.log_history",description:`<strong>log_history</strong> (<code>List[Dict[str, float]]</code>, <em>optional</em>) —
The list of logs done since the beginning of training.`,name:"log_history"},{anchor:"transformers.TrainerState.best_metric",description:`<strong>best_metric</strong> (<code>float</code>, <em>optional</em>) —
When tracking the best model, the value of the best metric encountered so far.`,name:"best_metric"},{anchor:"transformers.TrainerState.best_model_checkpoint",description:`<strong>best_model_checkpoint</strong> (<code>str</code>, <em>optional</em>) —
When tracking the best model, the value of the name of the checkpoint for the best model encountered so
far.`,name:"best_model_checkpoint"},{anchor:"transformers.TrainerState.is_local_process_zero",description:`<strong>is_local_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on
several machines) main process.`,name:"is_local_process_zero"},{anchor:"transformers.TrainerState.is_world_process_zero",description:`<strong>is_world_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be <code>True</code> for one process).`,name:"is_world_process_zero"},{anchor:"transformers.TrainerState.is_hyper_param_search",description:`<strong>is_hyper_param_search</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether we are in the process of a hyper parameter search using Trainer.hyperparameter_search. This will
impact the way data will be logged in TensorBoard.`,name:"is_hyper_param_search"}]}}),He=new ip({props:{$$slots:{default:[cp]},$$scope:{ctx:hr}}}),aa=new T({props:{name:"load_from_json",anchor:"transformers.TrainerState.load_from_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L101"}}),na=new T({props:{name:"save_to_json",anchor:"transformers.TrainerState.save_to_json",parameters:[{name:"json_path",val:": str"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L95"}}),oa=new Pn({}),la=new T({props:{name:"class transformers.TrainerControl",anchor:"transformers.TrainerControl",parameters:[{name:"should_training_stop",val:": bool = False"},{name:"should_epoch_stop",val:": bool = False"},{name:"should_save",val:": bool = False"},{name:"should_evaluate",val:": bool = False"},{name:"should_log",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L110",parametersDescription:[{anchor:"transformers.TrainerControl.should_training_stop",description:`<strong>should_training_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the training should be interrupted.</p>
<p>If <code>True</code>, this variable will not be set back to <code>False</code>. The training will just stop.`,name:"should_training_stop"},{anchor:"transformers.TrainerControl.should_epoch_stop",description:`<strong>should_epoch_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the current epoch should be interrupted.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next epoch.`,name:"should_epoch_stop"},{anchor:"transformers.TrainerControl.should_save",description:`<strong>should_save</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should be saved at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_save"},{anchor:"transformers.TrainerControl.should_evaluate",description:`<strong>should_evaluate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should be evaluated at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_evaluate"},{anchor:"transformers.TrainerControl.should_log",description:`<strong>should_log</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the logs should be reported at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.`,name:"should_log"}]}}),{c(){w=s("meta"),Y=c(),D=s("h1"),P=s("a"),Z=s("span"),_(M.$$.fragment),et=c(),ee=s("span"),te=r("Callbacks"),x=c(),q=s("p"),fa=r(`Callbacks are objects that can customize the behavior of the training loop in the PyTorch
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COMET_PROJECT_NAME (`),ur=s("code"),El=r("str"),kl=r(", "),vr=s("em"),$l=r("optional"),Tl=r(`):
Comet project name for experiments
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\u201CFALSE\u201D. Defaults to \u201CTRUE\u201D.`),Ml=c(),ft=s("p"),xl=r(`For a number of configurable items in the environment, see
`),ht=s("a"),Fl=r("here"),jl=r("."),Wn=c(),ne=s("div"),_(mt.$$.fragment),Nl=c(),dt=s("p"),Wl=r("A "),xa=s("a"),zl=r("TrainerCallback"),Bl=r(" that handles the default flow of the training loop for logs, evaluation and checkpoints."),zn=c(),se=s("div"),_(pt.$$.fragment),Vl=c(),gt=s("p"),ql=r("A bare "),Fa=s("a"),Rl=r("TrainerCallback"),Hl=r(" that just prints the logs."),Bn=c(),oe=s("div"),_(_t.$$.fragment),Ul=c(),ut=s("p"),Gl=r("A "),ja=s("a"),Jl=r("TrainerCallback"),Yl=r(" that displays the progress of training or evaluation."),Vn=c(),H=s("div"),_(vt.$$.fragment),Xl=c(),bt=s("p"),Kl=r("A "),Na=s("a"),Ql=r("TrainerCallback"),Zl=r(" that handles early stopping."),ei=c(),U=s("p"),ti=r("This callback depends on "),Wa=s("a"),ai=r("TrainingArguments"),ri=r(" argument "),Cr=s("em"),ni=r("load_best_model_at_end"),si=r(` functionality to set best_metric
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`),At=s("a"),Ai=r("here"),Di=r(`. You can also override the following environment
variables:`),Li=c(),d=s("p"),Si=r(`Environment:
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Whether or not to log model as artifact at the end of training. Use along with
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Set this to a custom string to store results in a different project.
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You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
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`),La=o(B,"A",{href:!0});var fm=l(La);Uo=n(fm,"TrainingArguments"),fm.forEach(t),Go=n(B," used to instantiate the "),Sa=o(B,"A",{href:!0});var hm=l(Sa);Jo=n(hm,"Trainer"),hm.forEach(t),Yo=n(B,`, can access that
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Whether to create an online, offline experiment or disable Comet logging. Can be \u201COFFLINE\u201D, \u201CONLINE\u201D,
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COMET_PROJECT_NAME (`),ur=o(A,"CODE",{});var Tm=l(ur);El=n(Tm,"str"),Tm.forEach(t),kl=n(A,", "),vr=o(A,"EM",{});var Cm=l(vr);$l=n(Cm,"optional"),Cm.forEach(t),Tl=n(A,`):
Comet project name for experiments
COMET_OFFLINE_DIRECTORY (`),br=o(A,"CODE",{});var wm=l(br);Cl=n(wm,"str"),wm.forEach(t),wl=n(A,", "),Er=o(A,"EM",{});var ym=l(Er);yl=n(ym,"optional"),ym.forEach(t),Al=n(A,`):
Folder to use for saving offline experiments when `),kr=o(A,"CODE",{});var Am=l(kr);Dl=n(Am,"COMET_MODE"),Am.forEach(t),Ll=n(A,` is \u201COFFLINE\u201D
COMET_LOG_ASSETS (`),$r=o(A,"CODE",{});var Dm=l($r);Sl=n(Dm,"str"),Dm.forEach(t),Pl=n(A,", "),Tr=o(A,"EM",{});var Lm=l(Tr);Ol=n(Lm,"optional"),Lm.forEach(t),Il=n(A,`):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be \u201CTRUE\u201D, or
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in `),za=o(Ke,"A",{href:!0});var jm=l(za);oi=n(jm,"TrainerState"),jm.forEach(t),li=n(Ke,"."),Ke.forEach(t),tr.forEach(t),qn=f(a),le=o(a,"DIV",{class:!0});var us=l(le);u(Et.$$.fragment,us),ii=f(us),ie=o(us,"P",{});var ar=l(ie);ci=n(ar,"A "),Ba=o(ar,"A",{href:!0});var Nm=l(Ba);fi=n(Nm,"TrainerCallback"),Nm.forEach(t),hi=n(ar," that sends the logs to "),kt=o(ar,"A",{href:!0,rel:!0});var Wm=l(kt);mi=n(Wm,"TensorBoard"),Wm.forEach(t),di=n(ar,"."),ar.forEach(t),us.forEach(t),Rn=f(a),G=o(a,"DIV",{class:!0});var rr=l(G);u($t.$$.fragment,rr),pi=f(rr),ce=o(rr,"P",{});var nr=l(ce);gi=n(nr,"A "),Va=o(nr,"A",{href:!0});var zm=l(Va);_i=n(zm,"TrainerCallback"),zm.forEach(t),ui=n(nr," that sends the logs to "),Tt=o(nr,"A",{href:!0,rel:!0});var Bm=l(Tt);vi=n(Bm,"Weight and Biases"),Bm.forEach(t),bi=n(nr,"."),nr.forEach(t),Ei=f(rr),W=o(rr,"DIV",{class:!0});var Qe=l(W);u(Ct.$$.fragment,Qe),ki=f(Qe),wt=o(Qe,"P",{});var vs=l(wt);$i=n(vs,"Setup the optional Weights & Biases ("),wr=o(vs,"EM",{});var Vm=l(wr);Ti=n(Vm,"wandb"),Vm.forEach(t),Ci=n(vs,") integration."),vs.forEach(t),wi=f(Qe),yt=o(Qe,"P",{});var bs=l(yt);yi=n(bs,`One can subclass and override this method to customize the setup if needed. Find more information
`),At=o(bs,"A",{href:!0,rel:!0});var qm=l(At);Ai=n(qm,"here"),qm.forEach(t),Di=n(bs,`. You can also override the following environment
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Whether or not to log model as artifact at the end of training. Use along with
`),Lr=o(g,"EM",{});var Gm=l(Lr);ji=n(Gm,"TrainingArguments.load_best_model_at_end"),Gm.forEach(t),Ni=n(g,` to upload best model.
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log gradients and parameters.
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Set this to a custom string to store results in a different project.
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without a remote storage will just copy the files to your artifact location.`),K.forEach(t),Ze.forEach(t),sr.forEach(t),Un=f(a),me=o(a,"DIV",{class:!0});var Es=l(me);u(Pt.$$.fragment,Es),zc=f(Es),de=o(Es,"P",{});var ir=l(de);Bc=n(ir,"A "),Ha=o(ir,"A",{href:!0});var vd=l(Ha);Vc=n(vd,"TrainerCallback"),vd.forEach(t),qc=n(ir," that sends the logs to "),Ot=o(ir,"A",{href:!0,rel:!0});var bd=l(Ot);Rc=n(bd,"AzureML"),bd.forEach(t),Hc=n(ir,"."),ir.forEach(t),Es.forEach(t),Gn=f(a),pe=o(a,"DIV",{class:!0});var ks=l(pe);u(It.$$.fragment,ks),Uc=f(ks),Mt=o(ks,"P",{});var $s=l(Mt);Gc=n($s,"A "),Ua=o($s,"A",{href:!0});var Ed=l(Ua);Jc=n(Ed,"TrainerCallback"),Ed.forEach(t),Yc=n($s," that tracks the CO2 emission of training."),$s.forEach(t),ks.forEach(t),Jn=f(a),ge=o(a,"H2",{class:!0});var Ts=l(ge);Le=o(Ts,"A",{id:!0,class:!0,href:!0});var kd=l(Le);Kr=o(kd,"SPAN",{});var $d=l(Kr);u(xt.$$.fragment,$d),$d.forEach(t),kd.forEach(t),Xc=f(Ts),Qr=o(Ts,"SPAN",{});var Td=l(Qr);Kc=n(Td,"TrainerCallback"),Td.forEach(t),Ts.forEach(t),Yn=f(a),p=o(a,"DIV",{class:!0});var $=l(p);u(Ft.$$.fragment,$),Qc=f($),Zr=o($,"P",{});var Cd=l(Zr);Zc=n(Cd,`A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:`),Cd.forEach(t),ef=f($),jt=o($,"P",{});var Cs=l(jt);tf=n(Cs,"The "),en=o(Cs,"CODE",{});var wd=l(en);af=n(wd,"control"),wd.forEach(t),rf=n(Cs,` object is the only one that can be changed by the callback, in which case the event that changes it
should return the modified version.`),Cs.forEach(t),nf=f($),O=o($,"P",{});var V=l(O);sf=n(V,"The argument "),tn=o(V,"CODE",{});var yd=l(tn);of=n(yd,"args"),yd.forEach(t),lf=n(V,", "),an=o(V,"CODE",{});var Ad=l(an);cf=n(Ad,"state"),Ad.forEach(t),ff=n(V," and "),rn=o(V,"CODE",{});var Dd=l(rn);hf=n(Dd,"control"),Dd.forEach(t),mf=n(V," are positionals for all events, all the others are grouped in "),nn=o(V,"CODE",{});var Ld=l(nn);df=n(Ld,"kwargs"),Ld.forEach(t),pf=n(V,`.
You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple `),sn=o(V,"CODE",{});var Sd=l(sn);gf=n(Sd,"PrinterCallback"),Sd.forEach(t),_f=n(V,"."),V.forEach(t),uf=f($),on=o($,"P",{});var Pd=l(on);vf=n(Pd,"Example:"),Pd.forEach(t),bf=f($),u(Nt.$$.fragment,$),Ef=f($),Se=o($,"DIV",{class:!0});var ws=l(Se);u(Wt.$$.fragment,ws),kf=f(ws),ln=o(ws,"P",{});var Od=l(ln);$f=n(Od,"Event called at the beginning of an epoch."),Od.forEach(t),ws.forEach(t),Tf=f($),Pe=o($,"DIV",{class:!0});var ys=l(Pe);u(zt.$$.fragment,ys),Cf=f(ys),cn=o(ys,"P",{});var Id=l(cn);wf=n(Id,"Event called at the end of an epoch."),Id.forEach(t),ys.forEach(t),yf=f($),Oe=o($,"DIV",{class:!0});var As=l(Oe);u(Bt.$$.fragment,As),Af=f(As),fn=o(As,"P",{});var Md=l(fn);Df=n(Md,"Event called after an evaluation phase."),Md.forEach(t),As.forEach(t),Lf=f($),Ie=o($,"DIV",{class:!0});var Ds=l(Ie);u(Vt.$$.fragment,Ds),Sf=f(Ds),qt=o(Ds,"P",{});var Ls=l(qt);Pf=n(Ls,"Event called at the end of the initialization of the "),Ga=o(Ls,"A",{href:!0});var xd=l(Ga);Of=n(xd,"Trainer"),xd.forEach(t),If=n(Ls,"."),Ls.forEach(t),Ds.forEach(t),Mf=f($),Me=o($,"DIV",{class:!0});var Ss=l(Me);u(Rt.$$.fragment,Ss),xf=f(Ss),hn=o(Ss,"P",{});var Fd=l(hn);Ff=n(Fd,"Event called after logging the last logs."),Fd.forEach(t),Ss.forEach(t),jf=f($),xe=o($,"DIV",{class:!0});var Ps=l(xe);u(Ht.$$.fragment,Ps),Nf=f(Ps),mn=o(Ps,"P",{});var jd=l(mn);Wf=n(jd,"Event called after a prediction step."),jd.forEach(t),Ps.forEach(t),zf=f($),Fe=o($,"DIV",{class:!0});var Os=l(Fe);u(Ut.$$.fragment,Os),Bf=f(Os),dn=o(Os,"P",{});var Nd=l(dn);Vf=n(Nd,"Event called after a checkpoint save."),Nd.forEach(t),Os.forEach(t),qf=f($),je=o($,"DIV",{class:!0});var Is=l(je);u(Gt.$$.fragment,Is),Rf=f(Is),pn=o(Is,"P",{});var Wd=l(pn);Hf=n(Wd,`Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.`),Wd.forEach(t),Is.forEach(t),Uf=f($),Ne=o($,"DIV",{class:!0});var Ms=l(Ne);u(Jt.$$.fragment,Ms),Gf=f(Ms),gn=o(Ms,"P",{});var zd=l(gn);Jf=n(zd,`Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.`),zd.forEach(t),Ms.forEach(t),Yf=f($),We=o($,"DIV",{class:!0});var xs=l(We);u(Yt.$$.fragment,xs),Xf=f(xs),_n=o(xs,"P",{});var Bd=l(_n);Kf=n(Bd,"Event called at the end of an substep during gradient accumulation."),Bd.forEach(t),xs.forEach(t),Qf=f($),ze=o($,"DIV",{class:!0});var Fs=l(ze);u(Xt.$$.fragment,Fs),Zf=f(Fs),un=o(Fs,"P",{});var Vd=l(un);eh=n(Vd,"Event called at the beginning of training."),Vd.forEach(t),Fs.forEach(t),th=f($),Be=o($,"DIV",{class:!0});var js=l(Be);u(Kt.$$.fragment,js),ah=f(js),vn=o(js,"P",{});var qd=l(vn);rh=n(qd,"Event called at the end of training."),qd.forEach(t),js.forEach(t),$.forEach(t),Xn=f(a),Ve=o(a,"P",{});var Ns=l(Ve);nh=n(Ns,"Here is an example of how to register a custom callback with the PyTorch "),Ja=o(Ns,"A",{href:!0});var Rd=l(Ja);sh=n(Rd,"Trainer"),Rd.forEach(t),oh=n(Ns,":"),Ns.forEach(t),Kn=f(a),u(Qt.$$.fragment,a),Qn=f(a),qe=o(a,"P",{});var Ws=l(qe);lh=n(Ws,"Another way to register a callback is to call "),bn=o(Ws,"CODE",{});var Hd=l(bn);ih=n(Hd,"trainer.add_callback()"),Hd.forEach(t),ch=n(Ws," as follows:"),Ws.forEach(t),Zn=f(a),u(Zt.$$.fragment,a),es=f(a),_e=o(a,"H2",{class:!0});var zs=l(_e);Re=o(zs,"A",{id:!0,class:!0,href:!0});var Ud=l(Re);En=o(Ud,"SPAN",{});var Gd=l(En);u(ea.$$.fragment,Gd),Gd.forEach(t),Ud.forEach(t),fh=f(zs),kn=o(zs,"SPAN",{});var Jd=l(kn);hh=n(Jd,"TrainerState"),Jd.forEach(t),zs.forEach(t),ts=f(a),I=o(a,"DIV",{class:!0});var Q=l(I);u(ta.$$.fragment,Q),mh=f(Q),ue=o(Q,"P",{});var cr=l(ue);dh=n(cr,"A class containing the "),Ya=o(cr,"A",{href:!0});var Yd=l(Ya);ph=n(Yd,"Trainer"),Yd.forEach(t),gh=n(cr,` inner state that will be saved along the model and optimizer when checkpointing
and passed to the `),Xa=o(cr,"A",{href:!0});var Xd=l(Xa);_h=n(Xd,"TrainerCallback"),Xd.forEach(t),uh=n(cr,"."),cr.forEach(t),vh=f(Q),u(He.$$.fragment,Q),bh=f(Q),Ue=o(Q,"DIV",{class:!0});var Bs=l(Ue);u(aa.$$.fragment,Bs),Eh=f(Bs),ra=o(Bs,"P",{});var Vs=l(ra);kh=n(Vs,"Create an instance from the content of "),$n=o(Vs,"CODE",{});var Kd=l($n);$h=n(Kd,"json_path"),Kd.forEach(t),Th=n(Vs,"."),Vs.forEach(t),Bs.forEach(t),Ch=f(Q),Ge=o(Q,"DIV",{class:!0});var qs=l(Ge);u(na.$$.fragment,qs),wh=f(qs),sa=o(qs,"P",{});var Rs=l(sa);yh=n(Rs,"Save the content of this instance in JSON format inside "),Tn=o(Rs,"CODE",{});var Qd=l(Tn);Ah=n(Qd,"json_path"),Qd.forEach(t),Dh=n(Rs,"."),Rs.forEach(t),qs.forEach(t),Q.forEach(t),as=f(a),ve=o(a,"H2",{class:!0});var Hs=l(ve);Je=o(Hs,"A",{id:!0,class:!0,href:!0});var Zd=l(Je);Cn=o(Zd,"SPAN",{});var ep=l(Cn);u(oa.$$.fragment,ep),ep.forEach(t),Zd.forEach(t),Lh=f(Hs),wn=o(Hs,"SPAN",{});var tp=l(wn);Sh=n(tp,"TrainerControl"),tp.forEach(t),Hs.forEach(t),rs=f(a),be=o(a,"DIV",{class:!0});var Us=l(be);u(la.$$.fragment,Us),Ph=f(Us),Ee=o(Us,"P",{});var fr=l(Ee);Oh=n(fr,"A class that handles the "),Ka=o(fr,"A",{href:!0});var ap=l(Ka);Ih=n(ap,"Trainer"),ap.forEach(t),Mh=n(fr," control flow. This class is used by the "),Qa=o(fr,"A",{href:!0});var rp=l(Qa);xh=n(rp,"TrainerCallback"),rp.forEach(t),Fh=n(fr,` to activate some
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The external learning rate.`,name:"lr"},{anchor:"transformers.Adafactor.eps",description:`<strong>eps</strong> (<code>Tuple[float, float]</code>, <em>optional</em>, defaults to (1e-30, 1e-3)) —
Regularization constants for square gradient and parameter scale respectively`,name:"eps"},{anchor:"transformers.Adafactor.clip_threshold",description:`<strong>clip_threshold</strong> (<code>float</code>, <em>optional</em>, defaults 1.0) —
Threshold of root mean square of final gradient update`,name:"clip_threshold"},{anchor:"transformers.Adafactor.decay_rate",description:`<strong>decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to -0.8) —
Coefficient used to compute running averages of square`,name:"decay_rate"},{anchor:"transformers.Adafactor.beta1",description:`<strong>beta1</strong> (<code>float</code>, <em>optional</em>) —
Coefficient used for computing running averages of gradient`,name:"beta1"},{anchor:"transformers.Adafactor.weight_decay",description:`<strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
Weight decay (L2 penalty)`,name:"weight_decay"},{anchor:"transformers.Adafactor.scale_parameter",description:`<strong>scale_parameter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If True, learning rate is scaled by root mean square`,name:"scale_parameter"},{anchor:"transformers.Adafactor.relative_step",description:`<strong>relative_step</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If True, time-dependent learning rate is computed instead of external learning rate`,name:"relative_step"},{anchor:"transformers.Adafactor.warmup_init",description:`<strong>warmup_init</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Time-dependent learning rate computation depends on whether warm-up initialization is being used`,name:"warmup_init"}]}}),ke=new Na({props:{code:"Adafactor(model.parameters(), scale_parameter=False, relative_step=False, warmup_init=False, lr=1e-3)",highlighted:'Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">False</span>, relative_step=<span class="hljs-literal">False</span>, warmup_init=<span class="hljs-literal">False</span>, lr=<span class="hljs-number">1e-3</span>)'}}),Ne=new Na({props:{code:"Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)",highlighted:'Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">True</span>, relative_step=<span class="hljs-literal">True</span>, warmup_init=<span class="hljs-literal">True</span>, lr=<span class="hljs-literal">None</span>)'}}),Fe=new Na({props:{code:`from transformers.optimization import Adafactor, AdafactorSchedule
optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Trainer(..., optimizers=(optimizer, lr_scheduler))`,highlighted:`<span class="hljs-keyword">from</span> transformers.optimization <span class="hljs-keyword">import</span> Adafactor, AdafactorSchedule
optimizer = Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">True</span>, relative_step=<span class="hljs-literal">True</span>, warmup_init=<span class="hljs-literal">True</span>, lr=<span class="hljs-literal">None</span>)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Trainer(..., optimizers=(optimizer, lr_scheduler))`}}),Ce=new Na({props:{code:`# replace AdamW with Adafactor
optimizer = Adafactor(
model.parameters(),
lr=1e-3,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
decay_rate=-0.8,
beta1=None,
weight_decay=0.0,
relative_step=False,
scale_parameter=False,
warmup_init=False,
)`,highlighted:`<span class="hljs-comment"># replace AdamW with Adafactor</span>
optimizer = Adafactor(
model.parameters(),
lr=<span class="hljs-number">1e-3</span>,
eps=(<span class="hljs-number">1e-30</span>, <span class="hljs-number">1e-3</span>),
clip_threshold=<span class="hljs-number">1.0</span>,
decay_rate=-<span class="hljs-number">0.8</span>,
beta1=<span class="hljs-literal">None</span>,
weight_decay=<span class="hljs-number">0.0</span>,
relative_step=<span class="hljs-literal">False</span>,
scale_parameter=<span class="hljs-literal">False</span>,
warmup_init=<span class="hljs-literal">False</span>,
)`}}),Oe=new $({props:{name:"step",anchor:"transformers.Adafactor.step",parameters:[{name:"closure",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L531",parametersDescription:[{anchor:"transformers.Adafactor.step.closure",description:`<strong>closure</strong> (callable, optional) — A closure that reevaluates the model
and returns the loss.`,name:"closure"}]}}),Re=new Z({}),je=new $({props:{name:"class transformers.AdamWeightDecay",anchor:"transformers.AdamWeightDecay",parameters:[{name:"learning_rate",val:": typing.Union[float, keras.optimizer_v2.learning_rate_schedule.LearningRateSchedule] = 0.001"},{name:"beta_1",val:": float = 0.9"},{name:"beta_2",val:": float = 0.999"},{name:"epsilon",val:": float = 1e-07"},{name:"amsgrad",val:": bool = False"},{name:"weight_decay_rate",val:": float = 0.0"},{name:"include_in_weight_decay",val:": typing.Optional[typing.List[str]] = None"},{name:"exclude_from_weight_decay",val:": typing.Optional[typing.List[str]] = None"},{name:"name",val:": str = 'AdamWeightDecay'"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L152",parametersDescription:[{anchor:"transformers.AdamWeightDecay.learning_rate",description:`<strong>learning_rate</strong> (<code>Union[float, tf.keras.optimizers.schedules.LearningRateSchedule]</code>, <em>optional</em>, defaults to 1e-3) —
The learning rate to use or a schedule.`,name:"learning_rate"},{anchor:"transformers.AdamWeightDecay.beta_1",description:`<strong>beta_1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates.`,name:"beta_1"},{anchor:"transformers.AdamWeightDecay.beta_2",description:`<strong>beta_2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates.`,name:"beta_2"},{anchor:"transformers.AdamWeightDecay.epsilon",description:`<strong>epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-7) —
The epsilon parameter in Adam, which is a small constant for numerical stability.`,name:"epsilon"},{anchor:"transformers.AdamWeightDecay.amsgrad",description:`<strong>amsgrad</strong> (<code>bool</code>, <em>optional</em>, default to <code>False</code>) —
Whether to apply AMSGrad variant of this algorithm or not, see <a href="https://arxiv.org/abs/1904.09237" rel="nofollow">On the Convergence of Adam and
Beyond</a>.`,name:"amsgrad"},{anchor:"transformers.AdamWeightDecay.weight_decay_rate",description:`<strong>weight_decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to apply.`,name:"weight_decay_rate"},{anchor:"transformers.AdamWeightDecay.include_in_weight_decay",description:`<strong>include_in_weight_decay</strong> (<code>List[str]</code>, <em>optional</em>) —
List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
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List of the parameter names (or re patterns) to exclude from applying weight decay to. If a
<code>include_in_weight_decay</code> is passed, the names in it will supersede this list.`,name:"exclude_from_weight_decay"},{anchor:"transformers.AdamWeightDecay.name",description:`<strong>name</strong> (<code>str</code>, <em>optional</em>, defaults to ‘AdamWeightDecay’) —
Optional name for the operations created when applying gradients.
kwargs —
Keyword arguments. Allowed to be {<code>clipnorm</code>, <code>clipvalue</code>, <code>lr</code>, <code>decay</code>}. <code>clipnorm</code> is clip gradients by
norm; <code>clipvalue</code> is clip gradients by value, <code>decay</code> is included for backward compatibility to allow time
inverse decay of learning rate. <code>lr</code> is included for backward compatibility, recommended to use
<code>learning_rate</code> instead.`,name:"name"}]}}),Ue=new $({props:{name:"from_config",anchor:"transformers.AdamWeightDecay.from_config",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L209"}}),Ge=new $({props:{name:"transformers.create_optimizer",anchor:"transformers.create_optimizer",parameters:[{name:"init_lr",val:": float"},{name:"num_train_steps",val:": int"},{name:"num_warmup_steps",val:": int"},{name:"min_lr_ratio",val:": float = 0.0"},{name:"adam_beta1",val:": float = 0.9"},{name:"adam_beta2",val:": float = 0.999"},{name:"adam_epsilon",val:": float = 1e-08"},{name:"weight_decay_rate",val:": float = 0.0"},{name:"power",val:": float = 1.0"},{name:"include_in_weight_decay",val:": typing.Optional[typing.List[str]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L82",parametersDescription:[{anchor:"transformers.create_optimizer.init_lr",description:`<strong>init_lr</strong> (<code>float</code>) —
The desired learning rate at the end of the warmup phase.`,name:"init_lr"},{anchor:"transformers.create_optimizer.num_train_steps",description:`<strong>num_train_steps</strong> (<code>int</code>) —
The total number of training steps.`,name:"num_train_steps"},{anchor:"transformers.create_optimizer.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>) —
The number of warmup steps.`,name:"num_warmup_steps"},{anchor:"transformers.create_optimizer.min_lr_ratio",description:`<strong>min_lr_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The final learning rate at the end of the linear decay will be <code>init_lr * min_lr_ratio</code>.`,name:"min_lr_ratio"},{anchor:"transformers.create_optimizer.adam_beta1",description:`<strong>adam_beta1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 to use in Adam.`,name:"adam_beta1"},{anchor:"transformers.create_optimizer.adam_beta2",description:`<strong>adam_beta2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 to use in Adam.`,name:"adam_beta2"},{anchor:"transformers.create_optimizer.adam_epsilon",description:`<strong>adam_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-8) —
The epsilon to use in Adam.`,name:"adam_epsilon"},{anchor:"transformers.create_optimizer.weight_decay_rate",description:`<strong>weight_decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to use.`,name:"weight_decay_rate"},{anchor:"transformers.create_optimizer.power",description:`<strong>power</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The power to use for PolynomialDecay.`,name:"power"},{anchor:"transformers.create_optimizer.include_in_weight_decay",description:`<strong>include_in_weight_decay</strong> (<code>List[str]</code>, <em>optional</em>) —
List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
applied to all parameters except bias and layer norm parameters.`,name:"include_in_weight_decay"}]}}),Ve=new Z({}),Me=new Z({}),He=new $({props:{name:"class transformers.SchedulerType",anchor:"transformers.SchedulerType",parameters:[{name:"value",val:""},{name:"names",val:" = None"},{name:"module",val:" = None"},{name:"qualname",val:" = None"},{name:"type",val:" = None"},{name:"start",val:" = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_utils.py#L301"}}),Be=new $({props:{name:"transformers.get_scheduler",anchor:"transformers.get_scheduler",parameters:[{name:"name",val:": typing.Union[str, transformers.trainer_utils.SchedulerType]"},{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": typing.Optional[int] = None"},{name:"num_training_steps",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L233",parametersDescription:[{anchor:"transformers.get_scheduler.name",description:`<strong>name</strong> (<code>str</code> or <code>SchedulerType</code>) —
The name of the scheduler to use.`,name:"name"},{anchor:"transformers.get_scheduler.optimizer",description:`<strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) —
The optimizer that will be used during training.`,name:"optimizer"},{anchor:"transformers.get_scheduler.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>, <em>optional</em>) —
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it’s unset and the scheduler type requires it.`,name:"num_warmup_steps"},{anchor:"transformers.get_scheduler.num_training_steps",description:`<strong>num_training_steps</strong> (\`int“, <em>optional</em>) —
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it’s unset and the scheduler type requires it.`,name:"num_training_steps"}]}}),Je=new $({props:{name:"transformers.get_constant_schedule",anchor:"transformers.get_constant_schedule",parameters:[{name:"optimizer",val:": Optimizer"},{name:"last_epoch",val:": int = -1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L34",parametersDescription:[{anchor:"transformers.get_constant_schedule.optimizer",description:`<strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.`,name:"optimizer"},{anchor:"transformers.get_constant_schedule.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.`,name:"last_epoch"}],returnDescription:`
<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),Ke=new $({props:{name:"transformers.get_constant_schedule_with_warmup",anchor:"transformers.get_constant_schedule_with_warmup",parameters:[{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": int"},{name:"last_epoch",val:": int = -1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L50",parametersDescription:[{anchor:"transformers.get_constant_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.`,name:"optimizer"},{anchor:"transformers.get_constant_schedule_with_warmup.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.`,name:"num_warmup_steps"},{anchor:"transformers.get_constant_schedule_with_warmup.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.`,name:"last_epoch"}],returnDescription:`
<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),Xe=new $({props:{name:"transformers.get_cosine_schedule_with_warmup",anchor:"transformers.get_cosine_schedule_with_warmup",parameters:[{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": int"},{name:"num_training_steps",val:": int"},{name:"num_cycles",val:": float = 0.5"},{name:"last_epoch",val:": int = -1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L104",parametersDescription:[{anchor:"transformers.get_cosine_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.`,name:"optimizer"},{anchor:"transformers.get_cosine_schedule_with_warmup.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.`,name:"num_warmup_steps"},{anchor:"transformers.get_cosine_schedule_with_warmup.num_training_steps",description:`<strong>num_training_steps</strong> (<code>int</code>) —
The total number of training steps.`,name:"num_training_steps"},{anchor:"transformers.get_cosine_schedule_with_warmup.num_cycles",description:`<strong>num_cycles</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) —
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).`,name:"num_cycles"},{anchor:"transformers.get_cosine_schedule_with_warmup.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.`,name:"last_epoch"}],returnDescription:`
<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),Ze=new $({props:{name:"transformers.get_cosine_with_hard_restarts_schedule_with_warmup",anchor:"transformers.get_cosine_with_hard_restarts_schedule_with_warmup",parameters:[{name:"optimizer",val:": Optimizer"},{name:"num_warmup_steps",val:": int"},{name:"num_training_steps",val:": int"},{name:"num_cycles",val:": int = 1"},{name:"last_epoch",val:": int = -1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L138",parametersDescription:[{anchor:"transformers.get_cosine_with_hard_restarts_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.`,name:"optimizer"},{anchor:"transformers.get_cosine_with_hard_restarts_schedule_with_warmup.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>) —
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The number of hard restarts to use.`,name:"num_cycles"},{anchor:"transformers.get_cosine_with_hard_restarts_schedule_with_warmup.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
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<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),tt=new $({props:{name:"transformers.get_linear_schedule_with_warmup",anchor:"transformers.get_linear_schedule_with_warmup",parameters:[{name:"optimizer",val:""},{name:"num_warmup_steps",val:""},{name:"num_training_steps",val:""},{name:"last_epoch",val:" = -1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L75",parametersDescription:[{anchor:"transformers.get_linear_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.`,name:"optimizer"},{anchor:"transformers.get_linear_schedule_with_warmup.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.`,name:"num_warmup_steps"},{anchor:"transformers.get_linear_schedule_with_warmup.num_training_steps",description:`<strong>num_training_steps</strong> (<code>int</code>) —
The total number of training steps.`,name:"num_training_steps"},{anchor:"transformers.get_linear_schedule_with_warmup.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.`,name:"last_epoch"}],returnDescription:`
<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),at=new $({props:{name:"transformers.get_polynomial_decay_schedule_with_warmup",anchor:"transformers.get_polynomial_decay_schedule_with_warmup",parameters:[{name:"optimizer",val:""},{name:"num_warmup_steps",val:""},{name:"num_training_steps",val:""},{name:"lr_end",val:" = 1e-07"},{name:"power",val:" = 1.0"},{name:"last_epoch",val:" = -1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L173",parametersDescription:[{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.optimizer",description:`<strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.`,name:"optimizer"},{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.num_warmup_steps",description:`<strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.`,name:"num_warmup_steps"},{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.num_training_steps",description:`<strong>num_training_steps</strong> (<code>int</code>) —
The total number of training steps.`,name:"num_training_steps"},{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.lr_end",description:`<strong>lr_end</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-7) —
The end LR.`,name:"lr_end"},{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.power",description:`<strong>power</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Power factor.`,name:"power"},{anchor:"transformers.get_polynomial_decay_schedule_with_warmup.last_epoch",description:`<strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.`,name:"last_epoch"}],returnDescription:`
<p><code>torch.optim.lr_scheduler.LambdaLR</code> with the appropriate schedule.</p>
`}}),st=new Z({}),it=new $({props:{name:"class transformers.WarmUp",anchor:"transformers.WarmUp",parameters:[{name:"initial_learning_rate",val:": float"},{name:"decay_schedule_fn",val:": typing.Callable"},{name:"warmup_steps",val:": int"},{name:"power",val:": float = 1.0"},{name:"name",val:": str = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L24",parametersDescription:[{anchor:"transformers.WarmUp.initial_learning_rate",description:`<strong>initial_learning_rate</strong> (<code>float</code>) —
The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end
of the warmup).`,name:"initial_learning_rate"},{anchor:"transformers.WarmUp.decay_schedule_fn",description:`<strong>decay_schedule_fn</strong> (<code>Callable</code>) —
The schedule function to apply after the warmup for the rest of training.`,name:"decay_schedule_fn"},{anchor:"transformers.WarmUp.warmup_steps",description:`<strong>warmup_steps</strong> (<code>int</code>) —
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`),q=a(E,"CODE",{});var K=o(q);Q=l(K,"save_pretrained"),K.forEach(r),M=l(E,`. Please refer to the docstrings of the methods
above for more information.`),E.forEach(r),this.h()},h(){f(b,"href","/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained")},m(L,E){d(L,m,E),t(m,k),t(m,b),t(b,x),t(m,N),t(m,q),t(q,Q),t(m,M)},d(L){L&&r(m)}}}function Ic(B){let m,k,b,x,N,q,Q,M,L,E,V,K,Ls,ie,Y,Za,Ut,eo,to,Ft,ro,so,ao,_r,oo,Ds,Z,pe,gr,Qe,no,vr,lo,Ts,Ht,io,Ns,Gt,po,Ms,S,Ue,co,$r,fo,mo,G,Fe,ho,br,uo,_o,ce,go,z,He,vo,Ge,$o,xr,bo,xo,Eo,Er,wo,yo,Re,Po,R,We,qo,Xe,ko,wr,Io,So,Ao,fe,Lo,W,Je,Do,Be,To,Rt,No,Mo,jo,de,js,ee,me,yr,Ke,Vo,Pr,zo,Vs,T,Co,Wt,Oo,Qo,Xt,Uo,Fo,Jt,Ho,Go,Bt,Ro,Wo,zs,P,Ye,Xo,qr,Jo,Bo,he,Ze,Ko,et,Yo,Kt,Zo,en,tn,ue,tt,rn,kr,sn,an,_e,rt,on,Ir,nn,ln,ge,st,pn,at,cn,Yt,fn,dn,mn,ve,ot,hn,nt,un,Zt,_n,gn,vn,$e,lt,$n,Sr,bn,Cs,U,it,xn,Ar,En,wn,be,pt,yn,Lr,Pn,Os,F,ct,qn,Dr,kn,In,xe,ft,Sn,Tr,An,Qs,te,Ee,Nr,dt,Ln,Mr,Dn,Us,we,mt,Tn,Nn,ht,Mn,Fs,er,jn,Hs,tr,Vn,Gs,w,jr,Vr,zn,Cn,zr,Cr,On,Qn,Or,Qr,Un,Fn,Ur,Fr,Hn,Gn,Hr,Gr,Rn,Wn,Rr,Wr,Xn,Jn,Xr,Jr,Bn,Kn,Br,Kr,Yn,Zn,Yr,Zr,el,Rs,ye,tl,rr,rl,sl,Ws,re,ut,al,sr,ol,es,nl,Xs,se,Pe,ts,_t,ll,rs,il,Js,ae,gt,pl,cl,vt,ss,fl,dl,Bs,$t,ml,bt,hl,Ks,ar,ul,Ys,or,as,os,_l,Zs,nr,gl,ea,qe,vl,xt,$l,bl,ta,oe,ke,ns,Et,xl,ls,El,ra,H,wt,wl,yl,yt,Pl,ql,Pt,kl,Il,sa,lr,Sl,aa,ne,Ie,is,qt,Al,ps,Ll,oa,ir,Dl,na,Se,cs,fs,Tl,Nl,ds,ms,Ml,la,kt,jl,hs,Vl,ia,D,It,zl,us,Cl,Ol,Ae,St,Ql,_s,Ul,Fl,C,At,Hl,Lt,Gl,gs,Rl,Wl,Xl,vs,Jl,Bl,Dt,Kl,Le,Tt,Yl,$s,Zl,pa,De,ei,bs,ti,ri,ca,j,Nt,si,xs,ai,oi,Es,ni,li,Mt,fa,Te,ii,ws,pi,ci,da,le,Ne,ys,jt,fi,Ps,di,ma,pr,mi,ha,Vt,ua,Me,hi,qs,ui,_i,_a,zt,ga,je,gi,Ct,vi,$i,va;return q=new Oe({}),Qe=new Oe({}),Ue=new y({props:{name:"class transformers.ProcessorMixin",anchor:"transformers.ProcessorMixin",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L44"}}),Fe=new y({props:{name:"from_pretrained",anchor:"transformers.ProcessorMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L157",parametersDescription:[{anchor:"transformers.ProcessorMixin.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.
**kwargs —
Additional keyword arguments passed along to both
<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a> and
<code>from_pretrained</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"}]}}),ce=new wi({props:{$$slots:{default:[Pc]},$$scope:{ctx:B}}}),He=new y({props:{name:"push_to_hub",anchor:"transformers.file_utils.PushToHubMixin.push_to_hub",parameters:[{name:"repo_path_or_name",val:": typing.Optional[str] = None"},{name:"repo_url",val:": typing.Optional[str] = None"},{name:"use_temp_dir",val:": bool = False"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"organization",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"**model_card_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842",parametersDescription:[{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name",description:`<strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your processor in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.`,name:"repo_path_or_name"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_url",description:`<strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.`,name:"repo_url"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir",description:`<strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.`,name:"use_temp_dir"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add processor"</code>.`,name:"commit_message"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.organization",description:`<strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your processor (you must be a member of this organization).`,name:"organization"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).`,name:"private"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token",description:`<strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.`,name:"use_auth_token"}],returnDescription:`
<p>The url of the commit of your processor in the given repository.</p>
`,returnType:`
<p><code>str</code></p>
`}}),Re=new As({props:{code:`from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained("bert-base-cased")
# Push the processor to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
processor.push_to_hub("my-finetuned-bert")
# Push the processor to your namespace with the name "my-finetuned-bert" with no local clone.
processor.push_to_hub("my-finetuned-bert", use_temp_dir=True)
# Push the processor to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
processor.push_to_hub("my-finetuned-bert", organization="huggingface")
# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
processor.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
processor = AutoProcessor.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the processor to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the processor to your namespace with the name "my-finetuned-bert" with no local clone.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the processor to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)`}}),We=new y({props:{name:"register_for_auto_class",anchor:"transformers.ProcessorMixin.register_for_auto_class",parameters:[{name:"auto_class",val:" = 'AutoProcessor'"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L190",parametersDescription:[{anchor:"transformers.ProcessorMixin.register_for_auto_class.auto_class",description:`<strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"AutoProcessor"</code>) —
The auto class to register this new feature extractor with.`,name:"auto_class"}]}}),fe=new wi({props:{warning:"{true}",$$slots:{default:[qc]},$$scope:{ctx:B}}}),Je=new y({props:{name:"save_pretrained",anchor:"transformers.ProcessorMixin.save_pretrained",parameters:[{name:"save_directory",val:""},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L95",parametersDescription:[{anchor:"transformers.ProcessorMixin.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).`,name:"save_directory"},{anchor:"transformers.ProcessorMixin.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your processor to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"push_to_hub"}]}}),de=new wi({props:{$$slots:{default:[kc]},$$scope:{ctx:B}}}),Ke=new Oe({}),Ye=new y({props:{name:"class transformers.DataProcessor",anchor:"transformers.DataProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L81"}}),Ze=new y({props:{name:"get_dev_examples",anchor:"transformers.DataProcessor.get_dev_examples",parameters:[{name:"data_dir",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L98"}}),tt=new y({props:{name:"get_example_from_tensor_dict",anchor:"transformers.DataProcessor.get_example_from_tensor_dict",parameters:[{name:"tensor_dict",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L84"}}),rt=new y({props:{name:"get_labels",anchor:"transformers.DataProcessor.get_labels",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L106"}}),st=new y({props:{name:"get_test_examples",anchor:"transformers.DataProcessor.get_test_examples",parameters:[{name:"data_dir",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L102"}}),ot=new y({props:{name:"get_train_examples",anchor:"transformers.DataProcessor.get_train_examples",parameters:[{name:"data_dir",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L94"}}),lt=new y({props:{name:"tfds_map",anchor:"transformers.DataProcessor.tfds_map",parameters:[{name:"example",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L110"}}),it=new y({props:{name:"class transformers.InputExample",anchor:"transformers.InputExample",parameters:[{name:"guid",val:": str"},{name:"text_a",val:": str"},{name:"text_b",val:": typing.Optional[str] = None"},{name:"label",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L31"}}),pt=new y({props:{name:"to_json_string",anchor:"transformers.InputExample.to_json_string",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L50"}}),ct=new y({props:{name:"class transformers.InputFeatures",anchor:"transformers.InputFeatures",parameters:[{name:"input_ids",val:": typing.List[int]"},{name:"attention_mask",val:": typing.Optional[typing.List[int]] = None"},{name:"token_type_ids",val:": typing.Optional[typing.List[int]] = None"},{name:"label",val:": typing.Union[int, float, NoneType] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L56"}}),ft=new y({props:{name:"to_json_string",anchor:"transformers.InputFeatures.to_json_string",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L76"}}),dt=new Oe({}),ut=new y({props:{name:"transformers.glue_convert_examples_to_features",anchor:"transformers.glue_convert_examples_to_features",parameters:[{name:"examples",val:": typing.Union[typing.List[transformers.data.processors.utils.InputExample], ForwardRef('tf.data.Dataset')]"},{name:"tokenizer",val:": PreTrainedTokenizer"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"task",val:" = None"},{name:"label_list",val:" = None"},{name:"output_mode",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/glue.py#L42",returnDescription:`
<p>If the <code>examples</code> input is a <code>tf.data.Dataset</code>, will return a <code>tf.data.Dataset</code> containing the task-specific
features. If the input is a list of <code>InputExamples</code>, will return a list of task-specific <code>InputFeatures</code> which
can be fed to the model.</p>
`}}),_t=new Oe({}),Et=new Oe({}),qt=new Oe({}),It=new y({props:{name:"class transformers.data.processors.squad.SquadProcessor",anchor:"transformers.data.processors.squad.SquadProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L543"}}),St=new y({props:{name:"get_dev_examples",anchor:"transformers.data.processors.squad.SquadProcessor.get_dev_examples",parameters:[{name:"data_dir",val:""},{name:"filename",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L631"}}),At=new y({props:{name:"get_examples_from_dataset",anchor:"transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset",parameters:[{name:"dataset",val:""},{name:"evaluate",val:" = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L576",returnDescription:`
<p>List of SquadExample</p>
`}}),Dt=new As({props:{code:`import tensorflow_datasets as tfds
dataset = tfds.load("squad")
training_examples = get_examples_from_dataset(dataset, evaluate=False)
evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow_datasets <span class="hljs-keyword">as</span> tfds
<span class="hljs-meta">>>> </span>dataset = tfds.load(<span class="hljs-string">"squad"</span>)
<span class="hljs-meta">>>> </span>training_examples = get_examples_from_dataset(dataset, evaluate=<span class="hljs-literal">False</span>)
<span class="hljs-meta">>>> </span>evaluation_examples = get_examples_from_dataset(dataset, evaluate=<span class="hljs-literal">True</span>)`}}),Tt=new y({props:{name:"get_train_examples",anchor:"transformers.data.processors.squad.SquadProcessor.get_train_examples",parameters:[{name:"data_dir",val:""},{name:"filename",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L609"}}),Nt=new y({props:{name:"transformers.squad_convert_examples_to_features",anchor:"transformers.squad_convert_examples_to_features",parameters:[{name:"examples",val:""},{name:"tokenizer",val:""},{name:"max_seq_length",val:""},{name:"doc_stride",val:""},{name:"max_query_length",val:""},{name:"is_training",val:""},{name:"padding_strategy",val:" = 'max_length'"},{name:"return_dataset",val:" = False"},{name:"threads",val:" = 1"},{name:"tqdm_enabled",val:" = True"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L318"}}),Mt=new As({props:{code:`processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=not evaluate,
)`,highlighted:`processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=<span class="hljs-keyword">not</span> evaluate,
)`}}),jt=new Oe({}),Vt=new As({props:{code:`# Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
# Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)`,highlighted:`<span class="hljs-comment"># Loading a V2 processor</span>
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
<span class="hljs-comment"># Loading a V1 processor</span>
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=<span class="hljs-keyword">not</span> evaluate,
)`}}),zt=new As({props:{code:`# tensorflow_datasets only handle Squad V1.
tfds_examples = tfds.load("squad")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)`,highlighted:`<span class="hljs-comment"># tensorflow_datasets only handle Squad V1.</span>
tfds_examples = tfds.load(<span class="hljs-string">"squad"</span>)
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=<span class="hljs-keyword">not</span> evaluate,
)`}}),{c(){m=s("meta"),k=i(),b=s("h1"),x=s("a"),N=s("span"),h(q.$$.fragment),Q=i(),M=s("span"),L=n("Processors"),E=i(),V=s("p"),K=n("Processors can mean two different things in the Transformers library:"),Ls=i(),ie=s("ul"),Y=s("li"),Za=n("the objects that pre-process inputs for multi-modal models such as "),Ut=s("a"),eo=n("Wav2Vec2"),to=n(` (speech and text)
or `),Ft=s("a"),ro=n("CLIP"),so=n(" (text and vision)"),ao=i(),_r=s("li"),oo=n("deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD."),Ds=i(),Z=s("h2"),pe=s("a"),gr=s("span"),h(Qe.$$.fragment),no=i(),vr=s("span"),lo=n("Multi-modal processors"),Ts=i(),Ht=s("p"),io=n(`Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text,
vision and audio). This is handled by objects called processors, which group tokenizers (for the text modality) and
feature extractors (for vision and audio).`),Ns=i(),Gt=s("p"),po=n("Those processors inherit from the following base class that implements the saving and loading functionality:"),Ms=i(),S=s("div"),h(Ue.$$.fragment),co=i(),$r=s("p"),fo=n("This is a mixin used to provide saving/loading functionality for all processor classes."),mo=i(),G=s("div"),h(Fe.$$.fragment),ho=i(),br=s("p"),uo=n("Instantiate a processor associated with a pretrained model."),_o=i(),h(ce.$$.fragment),go=i(),z=s("div"),h(He.$$.fragment),vo=i(),Ge=s("p"),$o=n(`Upload the processor files to the \u{1F917} Model Hub while synchronizing a local clone of the repo in
`),xr=s("code"),bo=n("repo_path_or_name"),xo=n("."),Eo=i(),Er=s("p"),wo=n("Examples:"),yo=i(),h(Re.$$.fragment),Po=i(),R=s("div"),h(We.$$.fragment),qo=i(),Xe=s("p"),ko=n(`Register this class with a given auto class. This should only be used for custom feature extractors as the ones
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`),Wt=s("a"),Oo=n("DataProcessor"),Qo=n(`. The processor returns a list of
`),Xt=s("a"),Uo=n("InputExample"),Fo=n(`. These
`),Jt=s("a"),Ho=n("InputExample"),Go=n(` can be converted to
`),Bt=s("a"),Ro=n("InputFeatures"),Wo=n(" in order to be fed to the model."),zs=i(),P=s("div"),h(Ye.$$.fragment),Xo=i(),qr=s("p"),Jo=n("Base class for data converters for sequence classification data sets."),Bo=i(),he=s("div"),h(Ze.$$.fragment),Ko=i(),et=s("p"),Yo=n("Gets a collection of "),Kt=s("a"),Zo=n("InputExample"),en=n(" for the dev set."),tn=i(),ue=s("div"),h(tt.$$.fragment),rn=i(),kr=s("p"),sn=n("Gets an example from a dict with tensorflow tensors."),an=i(),_e=s("div"),h(rt.$$.fragment),on=i(),Ir=s("p"),nn=n("Gets the list of labels for this data set."),ln=i(),ge=s("div"),h(st.$$.fragment),pn=i(),at=s("p"),cn=n("Gets a collection of "),Yt=s("a"),fn=n("InputExample"),dn=n(" for the test set."),mn=i(),ve=s("div"),h(ot.$$.fragment),hn=i(),nt=s("p"),un=n("Gets a collection of "),Zt=s("a"),_n=n("InputExample"),gn=n(" for the train set."),vn=i(),$e=s("div"),h(lt.$$.fragment),$n=i(),Sr=s("p"),bn=n(`Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
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`),rr=a(za,"A",{href:!0});var Sp=o(rr);rl=l(Sp,"InputExample"),Sp.forEach(r),sl=l(za,"."),za.forEach(r),Ws=p(e),re=a(e,"DIV",{class:!0});var Ca=o(re);u(ut.$$.fragment,Ca),al=p(Ca),sr=a(Ca,"P",{});var bi=o(sr);ol=l(bi,"Loads a data file into a list of "),es=a(bi,"CODE",{});var Ap=o(es);nl=l(Ap,"InputFeatures"),Ap.forEach(r),bi.forEach(r),Ca.forEach(r),Xs=p(e),se=a(e,"H2",{class:!0});var Oa=o(se);Pe=a(Oa,"A",{id:!0,class:!0,href:!0});var Lp=o(Pe);ts=a(Lp,"SPAN",{});var Dp=o(ts);u(_t.$$.fragment,Dp),Dp.forEach(r),Lp.forEach(r),ll=p(Oa),rs=a(Oa,"SPAN",{});var Tp=o(rs);il=l(Tp,"XNLI"),Tp.forEach(r),Oa.forEach(r),Js=p(e),ae=a(e,"P",{});var Ss=o(ae);gt=a(Ss,"A",{href:!0,rel:!0});var Np=o(gt);pl=l(Np,"The Cross-Lingual NLI Corpus (XNLI)"),Np.forEach(r),cl=l(Ss,` is a benchmark that evaluates the
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different languages (including both high-resource language such as English and low-resource languages such as Swahili).`),Ss.forEach(r),Bs=p(e),$t=a(e,"P",{});var xi=o($t);ml=l(xi,"It was released together with the paper "),bt=a(xi,"A",{href:!0,rel:!0});var Vp=o(bt);hl=l(Vp,"XNLI: Evaluating Cross-lingual Sentence Representations"),Vp.forEach(r),xi.forEach(r),Ks=p(e),ar=a(e,"P",{});var zp=o(ar);ul=l(zp,"This library hosts the processor to load the XNLI data:"),zp.forEach(r),Ys=p(e),or=a(e,"UL",{});var Cp=o(or);as=a(Cp,"LI",{});var Op=o(as);os=a(Op,"CODE",{});var Qp=o(os);_l=l(Qp,"XnliProcessor"),Qp.forEach(r),Op.forEach(r),Cp.forEach(r),Zs=p(e),nr=a(e,"P",{});var Up=o(nr);gl=l(Up,"Please note that since the gold labels are available on the test set, evaluation is performed on the test set."),Up.forEach(r),ea=p(e),qe=a(e,"P",{});var Qa=o(qe);vl=l(Qa,"An example using these processors is given in the "),xt=a(Qa,"A",{href:!0,rel:!0});var Fp=o(xt);$l=l(Fp,"run_xnli.py"),Fp.forEach(r),bl=l(Qa," script."),Qa.forEach(r),ta=p(e),oe=a(e,"H2",{class:!0});var Ua=o(oe);ke=a(Ua,"A",{id:!0,class:!0,href:!0});var Hp=o(ke);ns=a(Hp,"SPAN",{});var Gp=o(ns);u(Et.$$.fragment,Gp),Gp.forEach(r),Hp.forEach(r),xl=p(Ua),ls=a(Ua,"SPAN",{});var Rp=o(ls);El=l(Rp,"SQuAD"),Rp.forEach(r),Ua.forEach(r),ra=p(e),H=a(e,"P",{});var Qt=o(H);wt=a(Qt,"A",{href:!0,rel:!0});var Wp=o(wt);wl=l(Wp,"The Stanford Question Answering Dataset (SQuAD)"),Wp.forEach(r),yl=l(Qt,` is a benchmark that
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/main_classes/keras_callbacks.mdx-7ee92fa9.js | import{S as pt,i as ht,s as ut,e as o,k as c,w as x,t as n,M as mt,c as s,d as a,m as d,a as r,x as P,h as l,b as p,F as t,g as b,y as E,L as ft,q as z,o as H,B as q}from"../../chunks/vendor-4833417e.js";import{D as dt}from"../../chunks/Docstring-4f315ed9.js";import{C as We}from"../../chunks/CodeBlock-6a3d1b46.js";import{I as Fe}from"../../chunks/IconCopyLink-4b81c553.js";import"../../chunks/CopyButton-dacfbfaf.js";function bt(ue){let k,I,m,_,W,K,me,F,fe,se,A,be,re,y,C,G,O,_e,R,ge,ne,i,j,ke,u,ve,V,ye,we,J,$e,Ce,Q,Te,xe,X,Pe,Ee,ze,Y,He,qe,D,Ke,Z,Oe,je,N,le,w,T,ee,M,De,te,Ne,ie,g,L,Me,$,Le,ae,Se,Ie,oe,Ae,Ue,Be,S,ce;return K=new Fe({}),O=new Fe({}),j=new dt({props:{name:"class transformers.KerasMetricCallback",anchor:"transformers.KerasMetricCallback",parameters:[{name:"metric_fn",val:": typing.Callable"},{name:"eval_dataset",val:": typing.Union[tensorflow.python.data.ops.dataset_ops.DatasetV2, numpy.ndarray, tensorflow.python.framework.ops.Tensor, tuple, dict]"},{name:"output_cols",val:": typing.Optional[typing.List[str]] = None"},{name:"label_cols",val:": typing.Optional[typing.List[str]] = None"},{name:"batch_size",val:": typing.Optional[int] = None"},{name:"predict_with_generate",val:": typing.Optional[bool] = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/keras_callbacks.py#L22",parametersDescription:[{anchor:"transformers.KerasMetricCallback.metric_fn",description:`<strong>metric_fn</strong> (<code>Callable</code>) —
Metric function provided by the user. It will be called with two arguments - <code>predictions</code> and <code>labels</code>.
These contain the model’s outputs and matching labels from the dataset. It should return a dict mapping
metric names to numerical values.`,name:"metric_fn"},{anchor:"transformers.KerasMetricCallback.eval_dataset",description:`<strong>eval_dataset</strong> (<code>tf.data.Dataset</code> or <code>dict</code> or <code>tuple</code> or <code>np.ndarray</code> or <code>tf.Tensor</code>) —
Validation data to be used to generate predictions for the <code>metric_fn</code>.`,name:"eval_dataset"},{anchor:"transformers.KerasMetricCallback.output_cols",description:"<strong>output_cols</strong> (`List[str], <em>optional</em>) —\nA list of columns to be retained from the model output as the predictions. Defaults to all.",name:"output_cols"},{anchor:"transformers.KerasMetricCallback.label_cols",description:`<strong>label_cols</strong> (’<code>List[str]</code>, <em>optional</em>’) —
A list of columns to be retained from the input dataset as the labels. Will be autodetected if this is not
supplied.`,name:"label_cols"},{anchor:"transformers.KerasMetricCallback.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>) —
Batch size. Only used when the data is not a pre-batched <code>tf.data.Dataset</code>.`,name:"batch_size"},{anchor:"transformers.KerasMetricCallback.predict_with_generate",description:`<strong>predict_with_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether we should use <code>model.generate()</code> to get outputs for the model.`,name:"predict_with_generate"}]}}),D=new We({props:{code:`from datasets import load_metric
rouge_metric = load_metric("rouge")
def rouge_fn(predictions, labels):
decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=True)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
result = rouge_metric.compute(predictions=decoded_predictions, references=decoded_labels)
return {key: value.mid.fmeasure * 100 for key, value in result.items()}`,highlighted:`<span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric
rouge_metric = load_metric(<span class="hljs-string">"rouge"</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">rouge_fn</span>(<span class="hljs-params">predictions, labels</span>):
decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=<span class="hljs-literal">True</span>)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=<span class="hljs-literal">True</span>)
result = rouge_metric.compute(predictions=decoded_predictions, references=decoded_labels)
<span class="hljs-keyword">return</span> {key: value.mid.fmeasure * <span class="hljs-number">100</span> <span class="hljs-keyword">for</span> key, value <span class="hljs-keyword">in</span> result.items()}`}}),N=new We({props:{code:"{'rouge1': 37.4199, 'rouge2': 13.9768, 'rougeL': 34.361, 'rougeLsum': 35.0781",highlighted:'{'rouge1': <span class="hljs-number">37.4199</span>, 'rouge2': <span class="hljs-number">13.9768</span>, 'rougeL': <span class="hljs-number">34.361</span>, 'rougeLsum': <span class="hljs-number">35.0781</span>'}}),M=new Fe({}),L=new dt({props:{name:"class transformers.PushToHubCallback",anchor:"transformers.PushToHubCallback",parameters:[{name:"output_dir",val:": typing.Union[str, pathlib.Path]"},{name:"save_strategy",val:": typing.Union[str, transformers.trainer_utils.IntervalStrategy] = 'epoch'"},{name:"save_steps",val:": typing.Optional[int] = None"},{name:"tokenizer",val:": typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None"},{name:"hub_model_id",val:": typing.Optional[str] = None"},{name:"hub_token",val:": typing.Optional[str] = None"},{name:"checkpoint",val:": bool = False"},{name:"**model_card_args",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/keras_callbacks.py#L242",parametersDescription:[{anchor:"transformers.PushToHubCallback.output_dir",description:`<strong>output_dir</strong> (<code>str</code>) —
The output directory where the model predictions and checkpoints will be written and synced with the
repository on the Hub.`,name:"output_dir"},{anchor:"transformers.PushToHubCallback.save_strategy",description:`<strong>save_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"epoch"</code>) —
The checkpoint save strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: Save is done at the end of training.</li>
<li><code>"epoch"</code>: Save is done at the end of each epoch.</li>
<li><code>"steps"</code>: Save is done every <code>save_steps</code></li>
</ul>`,name:"save_strategy"},{anchor:"transformers.PushToHubCallback.save_steps",description:`<strong>save_steps</strong> (<code>int</code>, <em>optional</em>) —
The number of steps between saves when using the “steps” <code>save_strategy</code>.`,name:"save_steps"},{anchor:"transformers.PushToHubCallback.tokenizer",description:`<strong>tokenizer</strong> (<code>PreTrainedTokenizerBase</code>, <em>optional</em>) —
The tokenizer used by the model. If supplied, will be uploaded to the repo alongside the weights.`,name:"tokenizer"},{anchor:"transformers.PushToHubCallback.hub_model_id",description:`<strong>hub_model_id</strong> (<code>str</code>, <em>optional</em>) —
The name of the repository to keep in sync with the local <code>output_dir</code>. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance <code>"user_name/model"</code>, which allows you to push to an organization you are a member of with
<code>"organization_name/model"</code>.</p>
<p>Will default to to the name of <code>output_dir</code>.`,name:"hub_model_id"},{anchor:"transformers.PushToHubCallback.hub_token",description:`<strong>hub_token</strong> (<code>str</code>, <em>optional</em>) —
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
<code>huggingface-cli login</code>.`,name:"hub_token"},{anchor:"transformers.PushToHubCallback.checkpoint",description:`<strong>checkpoint</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to save full training checkpoints (including epoch and optimizer state) to allow training to be
resumed. Only usable when <code>save_strategy</code> is <code>"epoch"</code>.`,name:"checkpoint"}]}}),S=new We({props:{code:`from transformers.keras_callbacks import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
output_dir="./model_save",
tokenizer=tokenizer,
hub_model_id="gpt5-7xlarge",
)
model.fit(train_dataset, callbacks=[push_to_hub_callback])`,highlighted:`<span class="hljs-keyword">from</span> transformers.keras_callbacks <span class="hljs-keyword">import</span> PushToHubCallback
push_to_hub_callback = PushToHubCallback(
output_dir=<span class="hljs-string">"./model_save"</span>,
tokenizer=tokenizer,
hub_model_id=<span class="hljs-string">"gpt5-7xlarge"</span>,
)
model.fit(train_dataset, callbacks=[push_to_hub_callback])`}}),{c(){k=o("meta"),I=c(),m=o("h1"),_=o("a"),W=o("span"),x(K.$$.fragment),me=c(),F=o("span"),fe=n("Keras callbacks"),se=c(),A=o("p"),be=n(`When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
tasks:`),re=c(),y=o("h2"),C=o("a"),G=o("span"),x(O.$$.fragment),_e=c(),R=o("span"),ge=n("KerasMetricCallback"),ne=c(),i=o("div"),x(j.$$.fragment),ke=c(),u=o("p"),ve=n(`Callback to compute metrics at the end of every epoch. Unlike normal Keras metrics, these do not need to be
compilable by TF. It is particularly useful for common NLP metrics like BLEU and ROUGE that require string
operations or generation loops that cannot be compiled. Predictions (or generations) will be computed on the
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<span class="hljs-meta">>>> </span>pipe(<span class="hljs-string">"This restaurant is awesome"</span>)
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<span class="hljs-meta">>>> </span>pipe([<span class="hljs-string">"This restaurant is awesome"</span>, <span class="hljs-string">"This restaurant is aweful"</span>])
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from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
from tqdm.auto import tqdm
pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
dataset = datasets.load_dataset("superb", name="asr", split="test")
# KeyDataset (only *pt*) will simply return the item in the dict returned by the dataset item
# as we're not interested in the *target* part of the dataset.
for out in tqdm(pipe(KeyDataset(dataset, "file"))):
print(out)
# {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
# {"text": ....}
# ....`,highlighted:`<span class="hljs-keyword">import</span> datasets
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-keyword">from</span> transformers.pipelines.pt_utils <span class="hljs-keyword">import</span> KeyDataset
<span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm
pipe = pipeline(<span class="hljs-string">"automatic-speech-recognition"</span>, model=<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>, device=<span class="hljs-number">0</span>)
dataset = datasets.load_dataset(<span class="hljs-string">"superb"</span>, name=<span class="hljs-string">"asr"</span>, split=<span class="hljs-string">"test"</span>)
<span class="hljs-comment"># KeyDataset (only *pt*) will simply return the item in the dict returned by the dataset item</span>
<span class="hljs-comment"># as we're not interested in the *target* part of the dataset.</span>
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> tqdm(pipe(KeyDataset(dataset, <span class="hljs-string">"file"</span>))):
<span class="hljs-built_in">print</span>(out)
<span class="hljs-comment"># {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}</span>
<span class="hljs-comment"># {"text": ....}</span>
<span class="hljs-comment"># ....</span>`}}),Er=new z({props:{code:`from transformers import pipeline
pipe = pipeline("text-classification")
def data():
while True:
# This could come from a dataset, a database, a queue or HTTP request
# in a server
# Caveat: because this is iterative, you cannot use \`num_workers > 1\` variable
# to use multiple threads to preprocess data. You can still have 1 thread that
# does the preprocessing while the main runs the big inference
yield "This is a test"
for out in pipe(data()):
print(out)
# {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
# {"text": ....}
# ....`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
pipe = pipeline(<span class="hljs-string">"text-classification"</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">data</span>():
<span class="hljs-keyword">while</span> <span class="hljs-literal">True</span>:
<span class="hljs-comment"># This could come from a dataset, a database, a queue or HTTP request</span>
<span class="hljs-comment"># in a server</span>
<span class="hljs-comment"># Caveat: because this is iterative, you cannot use \`num_workers > 1\` variable</span>
<span class="hljs-comment"># to use multiple threads to preprocess data. You can still have 1 thread that</span>
<span class="hljs-comment"># does the preprocessing while the main runs the big inference</span>
<span class="hljs-keyword">yield</span> <span class="hljs-string">"This is a test"</span>
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> pipe(data()):
<span class="hljs-built_in">print</span>(out)
<span class="hljs-comment"># {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}</span>
<span class="hljs-comment"># {"text": ....}</span>
<span class="hljs-comment"># ....</span>`}}),qr=new w({props:{name:"transformers.pipeline",anchor:"transformers.pipeline",parameters:[{name:"task",val:": str = None"},{name:"model",val:": typing.Optional = None"},{name:"config",val:": typing.Union[str, transformers.configuration_utils.PretrainedConfig, NoneType] = None"},{name:"tokenizer",val:": typing.Union[str, transformers.tokenization_utils.PreTrainedTokenizer, NoneType] = None"},{name:"feature_extractor",val:": typing.Union[str, ForwardRef('SequenceFeatureExtractor'), NoneType] = None"},{name:"framework",val:": typing.Optional[str] = None"},{name:"revision",val:": typing.Optional[str] = None"},{name:"use_fast",val:": bool = True"},{name:"use_auth_token",val:": typing.Union[str, bool, NoneType] = None"},{name:"model_kwargs",val:": typing.Dict[str, typing.Any] = None"},{name:"pipeline_class",val:": typing.Optional[typing.Any] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines.py#L373",parametersDescription:[{anchor:"transformers.pipeline.task",description:`<strong>task</strong> (<code>str</code>) —
The task defining which pipeline will be returned. Currently accepted tasks are:</p>
<ul>
<li><code>"audio-classification"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AudioClassificationPipeline">AudioClassificationPipeline</a>.</li>
<li><code>"automatic-speech-recognition"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline">AutomaticSpeechRecognitionPipeline</a>.</li>
<li><code>"conversational"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ConversationalPipeline">ConversationalPipeline</a>.</li>
<li><code>"feature-extraction"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.FeatureExtractionPipeline">FeatureExtractionPipeline</a>.</li>
<li><code>"fill-mask"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.FillMaskPipeline">FillMaskPipeline</a>:.</li>
<li><code>"image-classification"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ImageClassificationPipeline">ImageClassificationPipeline</a>.</li>
<li><code>"question-answering"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.QuestionAnsweringPipeline">QuestionAnsweringPipeline</a>.</li>
<li><code>"table-question-answering"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TableQuestionAnsweringPipeline">TableQuestionAnsweringPipeline</a>.</li>
<li><code>"text2text-generation"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Text2TextGenerationPipeline">Text2TextGenerationPipeline</a>.</li>
<li><code>"text-classification"</code> (alias <code>"sentiment-analysis"</code> available): will return a
<a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TextClassificationPipeline">TextClassificationPipeline</a>.</li>
<li><code>"text-generation"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TextGenerationPipeline">TextGenerationPipeline</a>:.</li>
<li><code>"token-classification"</code> (alias <code>"ner"</code> available): will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TokenClassificationPipeline">TokenClassificationPipeline</a>.</li>
<li><code>"translation"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TranslationPipeline">TranslationPipeline</a>.</li>
<li><code>"translation_xx_to_yy"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TranslationPipeline">TranslationPipeline</a>.</li>
<li><code>"summarization"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.SummarizationPipeline">SummarizationPipeline</a>.</li>
<li><code>"zero-shot-classification"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ZeroShotClassificationPipeline">ZeroShotClassificationPipeline</a>.</li>
</ul>`,name:"task"},{anchor:"transformers.pipeline.model",description:`<strong>model</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>, <em>optional</em>) —
The model that will be used by the pipeline to make predictions. This can be a model identifier or an
actual instance of a pretrained model inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> (for PyTorch) or
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> (for TensorFlow).</p>
<p>If not provided, the default for the <code>task</code> will be loaded.`,name:"model"},{anchor:"transformers.pipeline.config",description:`<strong>config</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) —
The configuration that will be used by the pipeline to instantiate the model. This can be a model
identifier or an actual pretrained model configuration inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>.</p>
<p>If not provided, the default configuration file for the requested model will be used. That means that if
<code>model</code> is given, its default configuration will be used. However, if <code>model</code> is not supplied, this
<code>task</code>’s default model’s config is used instead.`,name:"config"},{anchor:"transformers.pipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>, <em>optional</em>) —
The tokenizer that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained tokenizer inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.</p>
<p>If not provided, the default tokenizer for the given <code>model</code> will be loaded (if it is a string). If <code>model</code>
is not specified or not a string, then the default tokenizer for <code>config</code> is loaded (if it is a string).
However, if <code>config</code> is also not given or not a string, then the default tokenizer for the given <code>task</code>
will be loaded.`,name:"tokenizer"},{anchor:"transformers.pipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>str</code> or <code>PreTrainedFeatureExtractor</code>, <em>optional</em>) —
The feature extractor that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained feature extractor inheriting from <code>PreTrainedFeatureExtractor</code>.</p>
<p>Feature extractors are used for non-NLP models, such as Speech or Vision models as well as multi-modal
models. Multi-modal models will also require a tokenizer to be passed.</p>
<p>If not provided, the default feature extractor for the given <code>model</code> will be loaded (if it is a string). If
<code>model</code> is not specified or not a string, then the default feature extractor for <code>config</code> is loaded (if it
is a string). However, if <code>config</code> is also not given or not a string, then the default feature extractor
for the given <code>task</code> will be loaded.`,name:"feature_extractor"},{anchor:"transformers.pipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.pipeline.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) —
When passing a task name or a string model identifier: The specific model version to use. It can be a
branch name, a tag name, or a commit id, since we use a git-based system for storing models and other
artifacts on huggingface.co, so <code>revision</code> can be any identifier allowed by git.`,name:"revision"},{anchor:"transformers.pipeline.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to use a Fast tokenizer if possible (a <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>).`,name:"use_fast"},{anchor:"transformers.pipeline.use_auth_token",description:`<strong>use_auth_token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>).
model_kwargs —
Additional dictionary of keyword arguments passed along to the model’s <code>from_pretrained(..., **model_kwargs)</code> function.
kwargs —
Additional keyword arguments passed along to the specific pipeline init (see the documentation for the
corresponding pipeline class for possible values).`,name:"use_auth_token"}],returnDescription:`
<p>A suitable pipeline for the task.</p>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Pipeline"
>Pipeline</a></p>
`}}),Dr=new z({props:{code:`from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
# Sentiment analysis pipeline
pipeline("sentiment-analysis")
# Question answering pipeline, specifying the checkpoint identifier
pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="bert-base-cased")
# Named entity recognition pipeline, passing in a specific model and tokenizer
model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
pipeline("ner", model=model, tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline, AutoModelForTokenClassification, AutoTokenizer
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Sentiment analysis pipeline</span>
<span class="hljs-meta">>>> </span>pipeline(<span class="hljs-string">"sentiment-analysis"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Question answering pipeline, specifying the checkpoint identifier</span>
<span class="hljs-meta">>>> </span>pipeline(<span class="hljs-string">"question-answering"</span>, model=<span class="hljs-string">"distilbert-base-cased-distilled-squad"</span>, tokenizer=<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Named entity recognition pipeline, passing in a specific model and tokenizer</span>
<span class="hljs-meta">>>> </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"dbmdz/bert-large-cased-finetuned-conll03-english"</span>)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span>pipeline(<span class="hljs-string">"ner"</span>, model=model, tokenizer=tokenizer)`}}),Ir=new y({}),jr=new z({props:{code:`from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
import datasets
dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
pipe = pipeline("text-classification", device=0)
for out in pipe(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
print(out)
# [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
# Exactly the same output as before, but the content are passed
# as batches to the model`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-keyword">from</span> transformers.pipelines.pt_utils <span class="hljs-keyword">import</span> KeyDataset
<span class="hljs-keyword">import</span> datasets
dataset = datasets.load_dataset(<span class="hljs-string">"imdb"</span>, name=<span class="hljs-string">"plain_text"</span>, split=<span class="hljs-string">"unsupervised"</span>)
pipe = pipeline(<span class="hljs-string">"text-classification"</span>, device=<span class="hljs-number">0</span>)
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> pipe(KeyDataset(dataset, <span class="hljs-string">"text"</span>), batch_size=<span class="hljs-number">8</span>, truncation=<span class="hljs-string">"only_first"</span>):
<span class="hljs-built_in">print</span>(out)
<span class="hljs-comment"># [{'label': 'POSITIVE', 'score': 0.9998743534088135}]</span>
<span class="hljs-comment"># Exactly the same output as before, but the content are passed</span>
<span class="hljs-comment"># as batches to the model</span>`}}),en=new kA({props:{warning:"{true}",$$slots:{default:[$A]},$$scope:{ctx:vr}}}),Sr=new z({props:{code:`from transformers import pipeline
from torch.utils.data import Dataset
from tqdm.auto import tqdm
pipe = pipeline("text-classification", device=0)
class MyDataset(Dataset):
def __len__(self):
return 5000
def __getitem__(self, i):
return "This is a test"
dataset = MyDataset()
for batch_size in [1, 8, 64, 256]:
print("-" * 30)
print(f"Streaming batch_size={batch_size}")
for out in tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)):
pass`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-keyword">from</span> torch.utils.data <span class="hljs-keyword">import</span> Dataset
<span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm
pipe = pipeline(<span class="hljs-string">"text-classification"</span>, device=<span class="hljs-number">0</span>)
<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyDataset</span>(<span class="hljs-title class_ inherited__">Dataset</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__len__</span>(<span class="hljs-params">self</span>):
<span class="hljs-keyword">return</span> <span class="hljs-number">5000</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__getitem__</span>(<span class="hljs-params">self, i</span>):
<span class="hljs-keyword">return</span> <span class="hljs-string">"This is a test"</span>
dataset = MyDataset()
<span class="hljs-keyword">for</span> batch_size <span class="hljs-keyword">in</span> [<span class="hljs-number">1</span>, <span class="hljs-number">8</span>, <span class="hljs-number">64</span>, <span class="hljs-number">256</span>]:
<span class="hljs-built_in">print</span>(<span class="hljs-string">"-"</span> * <span class="hljs-number">30</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Streaming batch_size=<span class="hljs-subst">{batch_size}</span>"</span>)
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> tqdm(pipe(dataset, batch_size=batch_size), total=<span class="hljs-built_in">len</span>(dataset)):
<span class="hljs-keyword">pass</span>`}}),Mr=new z({props:{code:`# On GTX 970
------------------------------
Streaming no batching
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:26<00:00, 187.52it/s]
------------------------------
Streaming batch_size=8
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:04<00:00, 1205.95it/s]
------------------------------
Streaming batch_size=64
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:02<00:00, 2478.24it/s]
------------------------------
Streaming batch_size=256
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:01<00:00, 2554.43it/s]
(diminishing returns, saturated the GPU)`,highlighted:`<span class="hljs-section"># On GTX 970
------------------------------</span>
Streaming no batching
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:26<00:00, 187.52it/s]
<span class="hljs-code">------------------------------
Streaming batch_size=8
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:04<00:00, 1205.95it/s]
------------------------------</span>
Streaming batch<span class="hljs-emphasis">_size=64
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:02<00:00, 2478.24it/s]
------------------------------
Streaming batch_</span>size=256
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5000/5000 [00:01<00:00, 2554.43it/s]
(diminishing returns, saturated the GPU)`}}),Fr=new z({props:{code:`class MyDataset(Dataset):
def __len__(self):
return 5000
def __getitem__(self, i):
if i % 64 == 0:
n = 100
else:
n = 1
return "This is a test" * n`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyDataset</span>(<span class="hljs-title class_ inherited__">Dataset</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__len__</span>(<span class="hljs-params">self</span>):
<span class="hljs-keyword">return</span> <span class="hljs-number">5000</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__getitem__</span>(<span class="hljs-params">self, i</span>):
<span class="hljs-keyword">if</span> i % <span class="hljs-number">64</span> == <span class="hljs-number">0</span>:
n = <span class="hljs-number">100</span>
<span class="hljs-keyword">else</span>:
n = <span class="hljs-number">1</span>
<span class="hljs-keyword">return</span> <span class="hljs-string">"This is a test"</span> * n`}}),Lr=new z({props:{code:`------------------------------
Streaming no batching
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:05<00:00, 183.69it/s]
------------------------------
Streaming batch_size=8
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:03<00:00, 265.74it/s]
------------------------------
Streaming batch_size=64
100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:26<00:00, 37.80it/s]
------------------------------
Streaming batch_size=256
0%| | 0/1000 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/home/nicolas/src/transformers/test.py", line 42, in <module>
for out in tqdm(pipe(dataset, batch_size=256), total=len(dataset)):
....
q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head)
RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch)`,highlighted:`<span class="hljs-comment">------------------------------</span>
Streaming no batching
<span class="hljs-number">100</span>%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| <span class="hljs-number">1000</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">05</span><<span class="hljs-number">00</span>:<span class="hljs-number">00</span>, <span class="hljs-number">183.69</span><span class="hljs-keyword">it</span>/s]
<span class="hljs-comment">------------------------------</span>
Streaming batch_size=<span class="hljs-number">8</span>
<span class="hljs-number">100</span>%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| <span class="hljs-number">1000</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">03</span><<span class="hljs-number">00</span>:<span class="hljs-number">00</span>, <span class="hljs-number">265.74</span><span class="hljs-keyword">it</span>/s]
<span class="hljs-comment">------------------------------</span>
Streaming batch_size=<span class="hljs-number">64</span>
<span class="hljs-number">100</span>%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| <span class="hljs-number">1000</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">26</span><<span class="hljs-number">00</span>:<span class="hljs-number">00</span>, <span class="hljs-number">37.80</span><span class="hljs-keyword">it</span>/s]
<span class="hljs-comment">------------------------------</span>
Streaming batch_size=<span class="hljs-number">256</span>
<span class="hljs-number">0</span>%| | <span class="hljs-number">0</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">00</span><span class="hljs-meta"><?</span>, ?<span class="hljs-keyword">it</span>/s]
Traceback (most recent call <span class="hljs-keyword">last</span>):
File <span class="hljs-string">"/home/nicolas/src/transformers/test.py"</span>, <span class="hljs-built_in">line</span> <span class="hljs-number">42</span>, <span class="hljs-keyword">in</span> <module>
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> tqdm(pipe(dataset, batch_size=<span class="hljs-number">256</span>), total=<span class="hljs-built_in">len</span>(dataset)):
....
q = q / math.<span class="hljs-built_in">sqrt</span>(dim_per_head) <span class="hljs-comment"># (bs, n_heads, q_length, dim_per_head)</span>
RuntimeError: CUDA out <span class="hljs-keyword">of</span> memory. Tried <span class="hljs-built_in">to</span> allocate <span class="hljs-number">376.00</span> MiB (GPU <span class="hljs-number">0</span>; <span class="hljs-number">3.95</span> GiB total capacity; <span class="hljs-number">1.72</span> GiB already allocated; <span class="hljs-number">354.88</span> MiB free; <span class="hljs-number">2.46</span> GiB reserved <span class="hljs-keyword">in</span> total <span class="hljs-keyword">by</span> PyTorch)`}}),Gr=new y({}),Nr=new z({props:{code:`preprocessed = pipe.preprocess(inputs)
model_outputs = pipe.forward(preprocessed)
outputs = pipe.postprocess(model_outputs)`,highlighted:`preprocessed = pipe.preprocess(inputs)
model_outputs = pipe.forward(preprocessed)
outputs = pipe.postprocess(model_outputs)`}}),Or=new z({props:{code:`all_model_outputs = []
for preprocessed in pipe.preprocess(inputs):
model_outputs = pipe.forward(preprocessed)
all_model_outputs.append(model_outputs)
outputs = pipe.postprocess(all_model_outputs)`,highlighted:`all_model_outputs = []
<span class="hljs-keyword">for</span> preprocessed <span class="hljs-keyword">in</span> pipe.preprocess(inputs):
model_outputs = pipe.forward(preprocessed)
all_model_outputs.append(model_outputs)
outputs = pipe.postprocess(all_model_outputs)`}}),Rr=new y({}),Hr=new z({props:{code:`class MyPipeline(TextClassificationPipeline):
def postprocess():
# Your code goes here
scores = scores * 100
# And here
my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
# or if you use *pipeline* function, then:
my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)`,highlighted:`<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyPipeline</span>(<span class="hljs-title class_ inherited__">TextClassificationPipeline</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">postprocess</span>():
<span class="hljs-comment"># Your code goes here</span>
scores = scores * <span class="hljs-number">100</span>
<span class="hljs-comment"># And here</span>
my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
<span class="hljs-comment"># or if you use *pipeline* function, then:</span>
my_pipeline = pipeline(model=<span class="hljs-string">"xxxx"</span>, pipeline_class=MyPipeline)`}}),Wr=new y({}),Qr=new y({}),Vr=new y({}),Br=new w({props:{name:"class transformers.AudioClassificationPipeline",anchor:"transformers.AudioClassificationPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/audio_classification.py#L67",parametersDescription:[{anchor:"transformers.AudioClassificationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.AudioClassificationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.AudioClassificationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.AudioClassificationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.AudioClassificationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.AudioClassificationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.AudioClassificationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.AudioClassificationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.AudioClassificationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.AudioClassificationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),Kr=new w({props:{name:"__call__",anchor:"transformers.AudioClassificationPipeline.__call__",parameters:[{name:"inputs",val:": typing.Union[numpy.ndarray, bytes, str]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/audio_classification.py#L90",parametersDescription:[{anchor:"transformers.AudioClassificationPipeline.__call__.inputs",description:`<strong>inputs</strong> (<code>np.ndarray</code> or <code>bytes</code> or <code>str</code>) —
The inputs is either a raw waveform (<code>np.ndarray</code> of shape (n, ) of type <code>np.float32</code> or <code>np.float64</code>)
at the correct sampling rate (no further check will be done) or a <code>str</code> that is the filename of the
audio file, the file will be read at the correct sampling rate to get the waveform using <em>ffmpeg</em>. This
requires <em>ffmpeg</em> to be installed on the system. If <em>inputs</em> is <code>bytes</code> it is supposed to be the
content of an audio file and is interpreted by <em>ffmpeg</em> in the same way.`,name:"inputs"},{anchor:"transformers.AudioClassificationPipeline.__call__.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to None) —
The number of top labels that will be returned by the pipeline. If the provided number is <code>None</code> or
higher than the number of labels available in the model configuration, it will default to the number of
labels.`,name:"top_k"}],returnDescription:`
<ul>
<li><strong>label</strong> (<code>str</code>) \u2014 The label predicted.</li>
<li><strong>score</strong> (<code>float</code>) \u2014 The corresponding probability.</li>
</ul>
`,returnType:`
<p>A list of <code>dict</code> with the following keys</p>
`}}),eo=new y({}),to=new w({props:{name:"class transformers.AutomaticSpeechRecognitionPipeline",anchor:"transformers.AutomaticSpeechRecognitionPipeline",parameters:[{name:"feature_extractor",val:": typing.Union[ForwardRef('SequenceFeatureExtractor'), str]"},{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/automatic_speech_recognition.py#L69"}}),no=new w({props:{name:"__call__",anchor:"transformers.AutomaticSpeechRecognitionPipeline.__call__",parameters:[{name:"inputs",val:": typing.Union[numpy.ndarray, bytes, str]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/automatic_speech_recognition.py#L127",parametersDescription:[{anchor:"transformers.AutomaticSpeechRecognitionPipeline.__call__.inputs",description:`<strong>inputs</strong> (<code>np.ndarray</code> or <code>bytes</code> or <code>str</code> or <code>dict</code>) —
The inputs is either :<ul>
<li><code>str</code> that is the filename of the audio file, the file will be read at the correct sampling rate
to get the waveform using <em>ffmpeg</em>. This requires <em>ffmpeg</em> to be installed on the system.</li>
<li><code>bytes</code> it is supposed to be the content of an audio file and is interpreted by <em>ffmpeg</em> in the
same way.</li>
<li>(<code>np.ndarray</code> of shape (n, ) of type <code>np.float32</code> or <code>np.float64</code>)
Raw audio at the correct sampling rate (no further check will be done)</li>
<li><code>dict</code> form can be used to pass raw audio sampled at arbitrary <code>sampling_rate</code> and let this
pipeline do the resampling. The dict must be in the format <code>{"sampling_rate": int, "raw": np.array}</code> with optionally a <code>"stride": (left: int, right: int)</code> than can ask the pipeline to
treat the first <code>left</code> samples and last <code>right</code> samples to be ignored in decoding (but used at
inference to provide more context to the model). Only use <code>stride</code> with CTC models.</li>
</ul>`,name:"inputs"},{anchor:"transformers.AutomaticSpeechRecognitionPipeline.__call__.return_timestamps",description:`<strong>return_timestamps</strong> (<em>optional</em>, <code>str</code>) —
Only available for pure CTC models. If set to <code>"char"</code>, the pipeline will return <code>timestamps</code> along the
text for every character in the text. For instance if you get <code>[{"text": "h", "timestamps": (0.5,0.6), {"text": "i", "timestamps": (0.7, .9)}]</code>, then it means the model predicts that the letter “h” was
pronounced after <code>0.5</code> and before <code>0.6</code> seconds. If set to <code>"word"</code>, the pipeline will return
<code>timestamps</code> along the text for every word in the text. For instance if you get <code>[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, .1.5)}]</code>, then it means the model
predicts that the word “hi” was pronounces before 0.5 and after 0.9 seconds.`,name:"return_timestamps"}],returnDescription:`
<p>A dictionary with the following keys:</p>
<ul>
<li><strong>text</strong> (<code>str</code> ) \u2014 The recognized text.</li>
<li><strong>chunks</strong> (<em>optional(, <code>List[Dict]</code>)
When using <code>return_timestamps</code>, the <code>chunks</code> will become a list containing all the various text
chunks identified by the model, </em>e.g.* <code>[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, 1.5)}]</code>. The original full text can roughly be recovered by doing
<code>"".join(chunk["text"] for chunk in output["chunks"])</code>.</li>
</ul>
`,returnType:`
<p><code>Dict</code></p>
`}}),oo=new y({}),so=new w({props:{name:"class transformers.Conversation",anchor:"transformers.Conversation",parameters:[{name:"text",val:": str = None"},{name:"conversation_id",val:": UUID = None"},{name:"past_user_inputs",val:" = None"},{name:"generated_responses",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L19",parametersDescription:[{anchor:"transformers.Conversation.text",description:`<strong>text</strong> (<code>str</code>, <em>optional</em>) —
The initial user input to start the conversation. If not provided, a user input needs to be provided
manually using the <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation.add_user_input">add_user_input()</a> method before the conversation can begin.`,name:"text"},{anchor:"transformers.Conversation.conversation_id",description:`<strong>conversation_id</strong> (<code>uuid.UUID</code>, <em>optional</em>) —
Unique identifier for the conversation. If not provided, a random UUID4 id will be assigned to the
conversation.`,name:"conversation_id"},{anchor:"transformers.Conversation.past_user_inputs",description:`<strong>past_user_inputs</strong> (<code>List[str]</code>, <em>optional</em>) —
Eventual past history of the conversation of the user. You don’t need to pass it manually if you use the
pipeline interactively but if you want to recreate history you need to set both <code>past_user_inputs</code> and
<code>generated_responses</code> with equal length lists of strings`,name:"past_user_inputs"},{anchor:"transformers.Conversation.generated_responses",description:`<strong>generated_responses</strong> (<code>List[str]</code>, <em>optional</em>) —
Eventual past history of the conversation of the model. You don’t need to pass it manually if you use the
pipeline interactively but if you want to recreate history you need to set both <code>past_user_inputs</code> and
<code>generated_responses</code> with equal length lists of strings`,name:"generated_responses"}]}}),ao=new z({props:{code:`conversation = Conversation("Going to the movies tonight - any suggestions?")
# Steps usually performed by the model when generating a response:
# 1. Mark the user input as processed (moved to the history)
conversation.mark_processed()
# 2. Append a mode response
conversation.append_response("The Big lebowski.")
conversation.add_user_input("Is it good?")`,highlighted:`conversation = Conversation(<span class="hljs-string">"Going to the movies tonight - any suggestions?"</span>)
<span class="hljs-comment"># Steps usually performed by the model when generating a response:</span>
<span class="hljs-comment"># 1. Mark the user input as processed (moved to the history)</span>
conversation.mark_processed()
<span class="hljs-comment"># 2. Append a mode response</span>
conversation.append_response(<span class="hljs-string">"The Big lebowski."</span>)
conversation.add_user_input(<span class="hljs-string">"Is it good?"</span>)`}}),io=new w({props:{name:"add_user_input",anchor:"transformers.Conversation.add_user_input",parameters:[{name:"text",val:": str"},{name:"overwrite",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L83",parametersDescription:[{anchor:"transformers.Conversation.add_user_input.text",description:"<strong>text</strong> (<code>str</code>) — The user input for the next conversation round.",name:"text"},{anchor:"transformers.Conversation.add_user_input.overwrite",description:`<strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not existing and unprocessed user input should be overwritten when this function is called.`,name:"overwrite"}]}}),po=new w({props:{name:"append_response",anchor:"transformers.Conversation.append_response",parameters:[{name:"response",val:": str"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L116",parametersDescription:[{anchor:"transformers.Conversation.append_response.response",description:"<strong>response</strong> (<code>str</code>) — The model generated response.",name:"response"}]}}),co=new w({props:{name:"iter_texts",anchor:"transformers.Conversation.iter_texts",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L125"}}),mo=new w({props:{name:"mark_processed",anchor:"transformers.Conversation.mark_processed",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L107"}}),fo=new w({props:{name:"class transformers.ConversationalPipeline",anchor:"transformers.ConversationalPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L164",parametersDescription:[{anchor:"transformers.ConversationalPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.ConversationalPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.ConversationalPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.ConversationalPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.ConversationalPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.ConversationalPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.ConversationalPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.ConversationalPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.ConversationalPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.ConversationalPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"},{anchor:"transformers.ConversationalPipeline.min_length_for_response",description:`<strong>min_length_for_response</strong> (<code>int</code>, <em>optional</em>, defaults to 32) —
The minimum length (in number of tokens) for a response.`,name:"min_length_for_response"},{anchor:"transformers.ConversationalPipeline.minimum_tokens",description:`<strong>minimum_tokens</strong> (<code>int</code>, <em>optional</em>, defaults to 10) —
The minimum length of tokens to leave for a response.`,name:"minimum_tokens"}]}}),uo=new z({props:{code:`conversational_pipeline = pipeline("conversational")
conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
conversation_2 = Conversation("What's the last book you have read?")
conversational_pipeline([conversation_1, conversation_2])
conversation_1.add_user_input("Is it an action movie?")
conversation_2.add_user_input("What is the genre of this book?")
conversational_pipeline([conversation_1, conversation_2])`,highlighted:`conversational_pipeline = pipeline(<span class="hljs-string">"conversational"</span>)
conversation_1 = Conversation(<span class="hljs-string">"Going to the movies tonight - any suggestions?"</span>)
conversation_2 = Conversation(<span class="hljs-string">"What's the last book you have read?"</span>)
conversational_pipeline([conversation_1, conversation_2])
conversation_1.add_user_input(<span class="hljs-string">"Is it an action movie?"</span>)
conversation_2.add_user_input(<span class="hljs-string">"What is the genre of this book?"</span>)
conversational_pipeline([conversation_1, conversation_2])`}}),go=new w({props:{name:"__call__",anchor:"transformers.ConversationalPipeline.__call__",parameters:[{name:"conversations",val:": typing.Union[transformers.pipelines.conversational.Conversation, typing.List[transformers.pipelines.conversational.Conversation]]"},{name:"num_workers",val:" = 0"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L219",parametersDescription:[{anchor:"transformers.ConversationalPipeline.__call__.conversations",description:`<strong>conversations</strong> (a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation">Conversation</a> or a list of <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation">Conversation</a>) —
Conversations to generate responses for.`,name:"conversations"},{anchor:"transformers.ConversationalPipeline.__call__.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).`,name:"clean_up_tokenization_spaces"}],returnDescription:`
<p>Conversation(s) with updated generated responses for those
containing a new user input.</p>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation"
>Conversation</a> or a list of <a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation"
>Conversation</a></p>
`}}),_o=new y({}),vo=new w({props:{name:"class transformers.FeatureExtractionPipeline",anchor:"transformers.FeatureExtractionPipeline",parameters:[{name:"model",val:": typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]"},{name:"tokenizer",val:": typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None"},{name:"feature_extractor",val:": typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None"},{name:"modelcard",val:": typing.Optional[transformers.modelcard.ModelCard] = None"},{name:"framework",val:": typing.Optional[str] = None"},{name:"task",val:": str = ''"},{name:"args_parser",val:": ArgumentHandler = None"},{name:"device",val:": int = -1"},{name:"binary_output",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/feature_extraction.py#L7",parametersDescription:[{anchor:"transformers.FeatureExtractionPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.FeatureExtractionPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.FeatureExtractionPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.FeatureExtractionPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.FeatureExtractionPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.FeatureExtractionPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.FeatureExtractionPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"}]}}),ko=new w({props:{name:"__call__",anchor:"transformers.FeatureExtractionPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/feature_extraction.py#L69",parametersDescription:[{anchor:"transformers.FeatureExtractionPipeline.__call__.args",description:"<strong>args</strong> (<code>str</code> or <code>List[str]</code>) — One or several texts (or one list of texts) to get the features of.",name:"args"}],returnDescription:`
<p>The features computed by the model.</p>
`,returnType:`
<p>A nested list of <code>float</code></p>
`}}),Po=new y({}),To=new w({props:{name:"class transformers.FillMaskPipeline",anchor:"transformers.FillMaskPipeline",parameters:[{name:"model",val:": typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]"},{name:"tokenizer",val:": typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None"},{name:"feature_extractor",val:": typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None"},{name:"modelcard",val:": typing.Optional[transformers.modelcard.ModelCard] = None"},{name:"framework",val:": typing.Optional[str] = None"},{name:"task",val:": str = ''"},{name:"args_parser",val:": ArgumentHandler = None"},{name:"device",val:": int = -1"},{name:"binary_output",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/fill_mask.py#L33",parametersDescription:[{anchor:"transformers.FillMaskPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.FillMaskPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.FillMaskPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.FillMaskPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.FillMaskPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.FillMaskPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.FillMaskPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.FillMaskPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.FillMaskPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.FillMaskPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"},{anchor:"transformers.FillMaskPipeline.top_k",description:`<strong>top_k</strong> (<code>int</code>, defaults to 5) —
The number of predictions to return.`,name:"top_k"},{anchor:"transformers.FillMaskPipeline.targets",description:`<strong>targets</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) —
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).`,name:"targets"}]}}),kn=new kA({props:{$$slots:{default:[EA]},$$scope:{ctx:vr}}}),$o=new w({props:{name:"__call__",anchor:"transformers.FillMaskPipeline.__call__",parameters:[{name:"inputs",val:""},{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/fill_mask.py#L204",parametersDescription:[{anchor:"transformers.FillMaskPipeline.__call__.args",description:`<strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
One or several texts (or one list of prompts) with masked tokens.`,name:"args"},{anchor:"transformers.FillMaskPipeline.__call__.targets",description:`<strong>targets</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) —
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first
resulting token will be used (with a warning, and that might be slower).`,name:"targets"},{anchor:"transformers.FillMaskPipeline.__call__.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>) —
When passed, overrides the number of predictions to return.`,name:"top_k"}],returnDescription:`
<p>Each result comes as list of dictionaries with the following keys:</p>
<ul>
<li><strong>sequence</strong> (<code>str</code>) \u2014 The corresponding input with the mask token prediction.</li>
<li><strong>score</strong> (<code>float</code>) \u2014 The corresponding probability.</li>
<li><strong>token</strong> (<code>int</code>) \u2014 The predicted token id (to replace the masked one).</li>
<li><strong>token</strong> (<code>str</code>) \u2014 The predicted token (to replace the masked one).</li>
</ul>
`,returnType:`
<p>A list or a list of list of <code>dict</code></p>
`}}),Eo=new y({}),qo=new w({props:{name:"class transformers.ImageClassificationPipeline",anchor:"transformers.ImageClassificationPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_classification.py#L31",parametersDescription:[{anchor:"transformers.ImageClassificationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.ImageClassificationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.ImageClassificationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.ImageClassificationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.ImageClassificationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.ImageClassificationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.ImageClassificationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.ImageClassificationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.ImageClassificationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.ImageClassificationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),Do=new w({props:{name:"__call__",anchor:"transformers.ImageClassificationPipeline.__call__",parameters:[{name:"images",val:": typing.Union[str, typing.List[str], ForwardRef('Image.Image'), typing.List[ForwardRef('Image.Image')]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_classification.py#L58",parametersDescription:[{anchor:"transformers.ImageClassificationPipeline.__call__.images",description:`<strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing a http link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul>
<p>The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
images.`,name:"images"},{anchor:"transformers.ImageClassificationPipeline.__call__.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 5) —
The number of top labels that will be returned by the pipeline. If the provided number is higher than
the number of labels available in the model configuration, it will default to the number of labels.`,name:"top_k"}],returnDescription:`
<p>A dictionary or a list of dictionaries containing result. If the input is a single image, will return a
dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to
the images.</p>
<p>The dictionaries contain the following keys:</p>
<ul>
<li><strong>label</strong> (<code>str</code>) \u2014 The label identified by the model.</li>
<li><strong>score</strong> (<code>int</code>) \u2014 The score attributed by the model for that label.</li>
</ul>
`}}),Io=new y({}),jo=new w({props:{name:"class transformers.ImageSegmentationPipeline",anchor:"transformers.ImageSegmentationPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_segmentation.py#L34",parametersDescription:[{anchor:"transformers.ImageSegmentationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.ImageSegmentationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.ImageSegmentationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.ImageSegmentationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.ImageSegmentationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.ImageSegmentationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.ImageSegmentationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.ImageSegmentationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.ImageSegmentationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.ImageSegmentationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),Lo=new w({props:{name:"__call__",anchor:"transformers.ImageSegmentationPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_segmentation.py#L69",parametersDescription:[{anchor:"transformers.ImageSegmentationPipeline.__call__.images",description:`<strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing an HTTP(S) link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul>
<p>The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
same format: all as HTTP(S) links, all as local paths, or all as PIL images.`,name:"images"},{anchor:"transformers.ImageSegmentationPipeline.__call__.threshold",description:`<strong>threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The probability necessary to make a prediction.`,name:"threshold"},{anchor:"transformers.ImageSegmentationPipeline.__call__.mask_threshold",description:`<strong>mask_threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.`,name:"mask_threshold"}],returnDescription:`
<p>A dictionary or a list of dictionaries containing the result. If the input is a single image, will return a
list of dictionaries, if the input is a list of several images, will return a list of list of dictionaries
corresponding to each image.</p>
<p>The dictionaries contain the following keys:</p>
<ul>
<li><strong>label</strong> (<code>str</code>) \u2014 The class label identified by the model.</li>
<li><strong>mask</strong> (<code>PIL.Image</code>) \u2014 Pil Image with size (heigth, width) of the original image. Pixel values in the
image are in the range 0-255. 0 means the pixel is <em>not</em> part of the <em>label</em>, 255 means it definitely is.</li>
<li><strong>score</strong> (<em>optional</em> <code>float</code>) \u2014 Optionally, when the model is capable of estimating a confidence of the
\u201Cobject\u201D described by the label and the mask.</li>
</ul>
`}}),Uo=new y({}),Go=new w({props:{name:"class transformers.TokenClassificationPipeline",anchor:"transformers.TokenClassificationPipeline",parameters:[{name:"args_parser",val:" = <transformers.pipelines.token_classification.TokenClassificationArgumentHandler object at 0x7f45b52f37c0>"},{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L86",parametersDescription:[{anchor:"transformers.TokenClassificationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.TokenClassificationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.TokenClassificationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.TokenClassificationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.TokenClassificationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.TokenClassificationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.TokenClassificationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.TokenClassificationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.TokenClassificationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.TokenClassificationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"},{anchor:"transformers.TokenClassificationPipeline.ignore_labels",description:`<strong>ignore_labels</strong> (<code>List[str]</code>, defaults to <code>["O"]</code>) —
A list of labels to ignore.`,name:"ignore_labels"},{anchor:"transformers.TokenClassificationPipeline.grouped_entities",description:`<strong>grouped_entities</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
DEPRECATED, use <code>aggregation_strategy</code> instead. Whether or not to group the tokens corresponding to the
same entity together in the predictions or not.`,name:"grouped_entities"},{anchor:"transformers.TokenClassificationPipeline.aggregation_strategy",description:`<strong>aggregation_strategy</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"none"</code>) —
The strategy to fuse (or not) tokens based on the model prediction.</p>
<ul>
<li>“none” : Will simply not do any aggregation and simply return raw results from the model</li>
<li>“simple” : Will attempt to group entities following the default schema. (A, B-TAG), (B, I-TAG), (C,
I-TAG), (D, B-TAG2) (E, B-TAG2) will end up being [{“word”: ABC, “entity”: “TAG”}, {“word”: “D”,
“entity”: “TAG2”}, {“word”: “E”, “entity”: “TAG2”}] Notice that two consecutive B tags will end up as
different entities. On word based languages, we might end up splitting words undesirably : Imagine
Microsoft being tagged as [{“word”: “Micro”, “entity”: “ENTERPRISE”}, {“word”: “soft”, “entity”:
“NAME”}]. Look for FIRST, MAX, AVERAGE for ways to mitigate that and disambiguate words (on languages
that support that meaning, which is basically tokens separated by a space). These mitigations will
only work on real words, “New york” might still be tagged with two different entities.</li>
<li>“first” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Words will simply use the tag of the first token of the word when there
is ambiguity.</li>
<li>“average” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words,
cannot end up with different tags. scores will be averaged first across tokens, and then the maximum
label is applied.</li>
<li>“max” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Word entity will simply be the token with the maximum score.</li>
</ul>`,name:"aggregation_strategy"}]}}),Ro=new w({props:{name:"aggregate_words",anchor:"transformers.TokenClassificationPipeline.aggregate_words",parameters:[{name:"entities",val:": typing.List[dict]"},{name:"aggregation_strategy",val:": AggregationStrategy"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L366"}}),Ho=new w({props:{name:"gather_pre_entities",anchor:"transformers.TokenClassificationPipeline.gather_pre_entities",parameters:[{name:"sentence",val:": str"},{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"offset_mapping",val:": typing.Union[typing.List[typing.Tuple[int, int]], NoneType]"},{name:"special_tokens_mask",val:": ndarray"},{name:"aggregation_strategy",val:": AggregationStrategy"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L252"}}),Wo=new w({props:{name:"group_entities",anchor:"transformers.TokenClassificationPipeline.group_entities",parameters:[{name:"entities",val:": typing.List[dict]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L428",parametersDescription:[{anchor:"transformers.TokenClassificationPipeline.group_entities.entities",description:"<strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.",name:"entities"}]}}),Qo=new w({props:{name:"group_sub_entities",anchor:"transformers.TokenClassificationPipeline.group_sub_entities",parameters:[{name:"entities",val:": typing.List[dict]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L393",parametersDescription:[{anchor:"transformers.TokenClassificationPipeline.group_sub_entities.entities",description:"<strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.",name:"entities"}]}}),Vo=new y({}),Bo=new w({props:{name:"class transformers.ObjectDetectionPipeline",anchor:"transformers.ObjectDetectionPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/object_detection.py#L25",parametersDescription:[{anchor:"transformers.ObjectDetectionPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.ObjectDetectionPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.ObjectDetectionPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.ObjectDetectionPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.ObjectDetectionPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.ObjectDetectionPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.ObjectDetectionPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.ObjectDetectionPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.ObjectDetectionPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.ObjectDetectionPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),Ko=new w({props:{name:"__call__",anchor:"transformers.ObjectDetectionPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/object_detection.py#L51",parametersDescription:[{anchor:"transformers.ObjectDetectionPipeline.__call__.images",description:`<strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing an HTTP(S) link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul>
<p>The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
same format: all as HTTP(S) links, all as local paths, or all as PIL images.`,name:"images"},{anchor:"transformers.ObjectDetectionPipeline.__call__.threshold",description:`<strong>threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The probability necessary to make a prediction.`,name:"threshold"}],returnDescription:`
<p>A list of dictionaries or a list of list of dictionaries containing the result. If the input is a single
image, will return a list of dictionaries, if the input is a list of several images, will return a list of
list of dictionaries corresponding to each image.</p>
<p>The dictionaries contain the following keys:</p>
<ul>
<li><strong>label</strong> (<code>str</code>) \u2014 The class label identified by the model.</li>
<li><strong>score</strong> (<code>float</code>) \u2014 The score attributed by the model for that label.</li>
<li><strong>box</strong> (<code>List[Dict[str, int]]</code>) \u2014 The bounding box of detected object in image\u2019s original size.</li>
</ul>
`}}),Jo=new y({}),es=new w({props:{name:"class transformers.QuestionAnsweringPipeline",anchor:"transformers.QuestionAnsweringPipeline",parameters:[{name:"model",val:": typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]"},{name:"tokenizer",val:": PreTrainedTokenizer"},{name:"modelcard",val:": typing.Optional[transformers.modelcard.ModelCard] = None"},{name:"framework",val:": typing.Optional[str] = None"},{name:"device",val:": int = -1"},{name:"task",val:": str = ''"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L102",parametersDescription:[{anchor:"transformers.QuestionAnsweringPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.QuestionAnsweringPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.QuestionAnsweringPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.QuestionAnsweringPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.QuestionAnsweringPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.QuestionAnsweringPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.QuestionAnsweringPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.QuestionAnsweringPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.QuestionAnsweringPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.QuestionAnsweringPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),rs=new w({props:{name:"__call__",anchor:"transformers.QuestionAnsweringPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L205",parametersDescription:[{anchor:"transformers.QuestionAnsweringPipeline.__call__.args",description:`<strong>args</strong> (<code>SquadExample</code>or a list of <code>SquadExample</code> —
One or several <code>SquadExample</code>containing the question and context.`,name:"args"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.X",description:`<strong>X</strong> (<code>SquadExample</code>or a list of <code>SquadExample</code> <em>optional</em>) —
One or several <code>SquadExample</code>containing the question and context (will be treated the same way as if
passed as the first positional argument).`,name:"X"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.data",description:`<strong>data</strong> (<code>SquadExample</code>or a list of <code>SquadExample</code> <em>optional</em>) —
One or several <code>SquadExample</code>containing the question and context (will be treated the same way as if
passed as the first positional argument).`,name:"data"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.question",description:`<strong>question</strong> (<code>str</code> or <code>List[str]</code>) —
One or several question(s) (must be used in conjunction with the <code>context</code> argument).`,name:"question"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.context",description:`<strong>context</strong> (<code>str</code> or <code>List[str]</code>) —
One or several context(s) associated with the question(s) (must be used in conjunction with the
<code>question</code> argument).`,name:"context"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.topk",description:`<strong>topk</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The number of answers to return (will be chosen by order of likelihood). Note that we return less than
topk answers if there are not enough options available within the context.`,name:"topk"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.doc_stride",description:`<strong>doc_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 128) —
If the context is too long to fit with the question for the model, it will be split in several chunks
with some overlap. This argument controls the size of that overlap.`,name:"doc_stride"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.max_answer_len",description:`<strong>max_answer_len</strong> (<code>int</code>, <em>optional</em>, defaults to 15) —
The maximum length of predicted answers (e.g., only answers with a shorter length are considered).`,name:"max_answer_len"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.max_seq_len",description:`<strong>max_seq_len</strong> (<code>int</code>, <em>optional</em>, defaults to 384) —
The maximum length of the total sentence (context + question) after tokenization. The context will be
split in several chunks (using <code>doc_stride</code>) if needed.`,name:"max_seq_len"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.max_question_len",description:`<strong>max_question_len</strong> (<code>int</code>, <em>optional</em>, defaults to 64) —
The maximum length of the question after tokenization. It will be truncated if needed.`,name:"max_question_len"},{anchor:"transformers.QuestionAnsweringPipeline.__call__.handle_impossible_answer",description:`<strong>handle_impossible_answer</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not we accept impossible as an answer.`,name:"handle_impossible_answer"}],returnDescription:`
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>score</strong> (<code>float</code>) \u2014 The probability associated to the answer.</li>
<li><strong>start</strong> (<code>int</code>) \u2014 The character start index of the answer (in the tokenized version of the input).</li>
<li><strong>end</strong> (<code>int</code>) \u2014 The character end index of the answer (in the tokenized version of the input).</li>
<li><strong>answer</strong> (<code>str</code>) \u2014 The answer to the question.</li>
</ul>
`,returnType:`
<p>A <code>dict</code> or a list of <code>dict</code></p>
`}}),os=new w({props:{name:"create_sample",anchor:"transformers.QuestionAnsweringPipeline.create_sample",parameters:[{name:"question",val:": typing.Union[str, typing.List[str]]"},{name:"context",val:": typing.Union[str, typing.List[str]]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L143",parametersDescription:[{anchor:"transformers.QuestionAnsweringPipeline.create_sample.question",description:"<strong>question</strong> (<code>str</code> or <code>List[str]</code>) — The question(s) asked.",name:"question"},{anchor:"transformers.QuestionAnsweringPipeline.create_sample.context",description:"<strong>context</strong> (<code>str</code> or <code>List[str]</code>) — The context(s) in which we will look for the answer.",name:"context"}],returnDescription:`
<p>The corresponding <code>SquadExample</code>grouping question and context.</p>
`}}),ss=new w({props:{name:"decode",anchor:"transformers.QuestionAnsweringPipeline.decode",parameters:[{name:"start",val:": ndarray"},{name:"end",val:": ndarray"},{name:"topk",val:": int"},{name:"max_answer_len",val:": int"},{name:"undesired_tokens",val:": ndarray"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L481",parametersDescription:[{anchor:"transformers.QuestionAnsweringPipeline.decode.start",description:"<strong>start</strong> (<code>np.ndarray</code>) — Individual start probabilities for each token.",name:"start"},{anchor:"transformers.QuestionAnsweringPipeline.decode.end",description:"<strong>end</strong> (<code>np.ndarray</code>) — Individual end probabilities for each token.",name:"end"},{anchor:"transformers.QuestionAnsweringPipeline.decode.topk",description:"<strong>topk</strong> (<code>int</code>) — Indicates how many possible answer span(s) to extract from the model output.",name:"topk"},{anchor:"transformers.QuestionAnsweringPipeline.decode.max_answer_len",description:"<strong>max_answer_len</strong> (<code>int</code>) — Maximum size of the answer to extract from the model’s output.",name:"max_answer_len"},{anchor:"transformers.QuestionAnsweringPipeline.decode.undesired_tokens",description:"<strong>undesired_tokens</strong> (<code>np.ndarray</code>) — Mask determining tokens that can be part of the answer",name:"undesired_tokens"}]}}),is=new w({props:{name:"span_to_answer",anchor:"transformers.QuestionAnsweringPipeline.span_to_answer",parameters:[{name:"text",val:": str"},{name:"start",val:": int"},{name:"end",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L530",parametersDescription:[{anchor:"transformers.QuestionAnsweringPipeline.span_to_answer.text",description:"<strong>text</strong> (<code>str</code>) — The actual context to extract the answer from.",name:"text"},{anchor:"transformers.QuestionAnsweringPipeline.span_to_answer.start",description:"<strong>start</strong> (<code>int</code>) — The answer starting token index.",name:"start"},{anchor:"transformers.QuestionAnsweringPipeline.span_to_answer.end",description:"<strong>end</strong> (<code>int</code>) — The answer end token index.",name:"end"}],returnDescription:`
<p>str, \u2018start\u2019: int, \u2018end\u2019: int}\`</p>
`,returnType:`
<p>Dictionary like \`{\u2018answer\u2019</p>
`}}),ls=new y({}),ps=new w({props:{name:"class transformers.SummarizationPipeline",anchor:"transformers.SummarizationPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L186",parametersDescription:[{anchor:"transformers.SummarizationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.SummarizationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.SummarizationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.SummarizationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.SummarizationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.SummarizationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.SummarizationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.SummarizationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.SummarizationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.SummarizationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),ds=new z({props:{code:`# use bart in pytorch
summarizer = pipeline("summarization")
summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)
# use t5 in tf
summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
summarizer("An apple a day, keeps the doctor away", min_length=5, max_length=20)`,highlighted:`<span class="hljs-comment"># use bart in pytorch</span>
summarizer = pipeline(<span class="hljs-string">"summarization"</span>)
summarizer(<span class="hljs-string">"An apple a day, keeps the doctor away"</span>, min_length=<span class="hljs-number">5</span>, max_length=<span class="hljs-number">20</span>)
<span class="hljs-comment"># use t5 in tf</span>
summarizer = pipeline(<span class="hljs-string">"summarization"</span>, model=<span class="hljs-string">"t5-base"</span>, tokenizer=<span class="hljs-string">"t5-base"</span>, framework=<span class="hljs-string">"tf"</span>)
summarizer(<span class="hljs-string">"An apple a day, keeps the doctor away"</span>, min_length=<span class="hljs-number">5</span>, max_length=<span class="hljs-number">20</span>)`}}),ms=new w({props:{name:"__call__",anchor:"transformers.SummarizationPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L212",parametersDescription:[{anchor:"transformers.SummarizationPipeline.__call__.documents",description:`<strong>documents</strong> (<em>str</em> or <code>List[str]</code>) —
One or several articles (or one list of articles) to summarize.`,name:"documents"},{anchor:"transformers.SummarizationPipeline.__call__.return_text",description:`<strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs`,name:"return_text"},{anchor:"transformers.SummarizationPipeline.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.`,name:"return_tensors"},{anchor:"transformers.SummarizationPipeline.__call__.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).`,name:"clean_up_tokenization_spaces"}],returnDescription:`
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>summary_text</strong> (<code>str</code>, present when <code>return_text=True</code>) \u2014 The summary of the corresponding input.</li>
<li><strong>summary_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) \u2014 The token
ids of the summary.</li>
</ul>
`,returnType:`
<p>A list or a list of list of <code>dict</code></p>
`}}),fs=new y({}),hs=new w({props:{name:"class transformers.TableQuestionAnsweringPipeline",anchor:"transformers.TableQuestionAnsweringPipeline",parameters:[{name:"args_parser",val:" = <transformers.pipelines.table_question_answering.TableQuestionAnsweringArgumentHandler object at 0x7f45b52d1cd0>"},{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/table_question_answering.py#L83",parametersDescription:[{anchor:"transformers.TableQuestionAnsweringPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.TableQuestionAnsweringPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.TableQuestionAnsweringPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.TableQuestionAnsweringPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.TableQuestionAnsweringPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.TableQuestionAnsweringPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.TableQuestionAnsweringPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.TableQuestionAnsweringPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.TableQuestionAnsweringPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.TableQuestionAnsweringPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),vs=new w({props:{name:"__call__",anchor:"transformers.TableQuestionAnsweringPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/table_question_answering.py#L244",parametersDescription:[{anchor:"transformers.TableQuestionAnsweringPipeline.__call__.table",description:`<strong>table</strong> (<code>pd.DataFrame</code> or <code>Dict</code>) —
Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
See above for an example of dictionary.`,name:"table"},{anchor:"transformers.TableQuestionAnsweringPipeline.__call__.query",description:`<strong>query</strong> (<code>str</code> or <code>List[str]</code>) —
Query or list of queries that will be sent to the model alongside the table.`,name:"query"},{anchor:"transformers.TableQuestionAnsweringPipeline.__call__.sequential",description:`<strong>sequential</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
inference to be done sequentially to extract relations within sequences, given their conversational
nature.`,name:"sequential"},{anchor:"transformers.TableQuestionAnsweringPipeline.__call__.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.TableQuestionAnsweringPipeline.__call__.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <code>TapasTruncationStrategy</code>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'drop_rows_to_fit'</code>: Truncate to a maximum length specified with the argument <code>max_length</code>
or to the maximum acceptable input length for the model if that argument is not provided. This will
truncate row by row, removing rows from the table.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"}],returnDescription:`
<p>Each result is a dictionary with the following
keys:</p>
<ul>
<li><strong>answer</strong> (<code>str</code>) \u2014 The answer of the query given the table. If there is an aggregator, the answer will
be preceded by <code>AGGREGATOR ></code>.</li>
<li><strong>coordinates</strong> (<code>List[Tuple[int, int]]</code>) \u2014 Coordinates of the cells of the answers.</li>
<li><strong>cells</strong> (<code>List[str]</code>) \u2014 List of strings made up of the answer cell values.</li>
<li><strong>aggregator</strong> (<code>str</code>) \u2014 If the model has an aggregator, this returns the aggregator.</li>
</ul>
`,returnType:`
<p>A dictionary or a list of dictionaries containing results</p>
`}}),bs=new z({props:{code:`data = {
"actors": ["brad pitt", "leonardo di caprio", "george clooney"],
"age": ["56", "45", "59"],
"number of movies": ["87", "53", "69"],
"date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
}`,highlighted:`data = {
<span class="hljs-string">"actors"</span>: [<span class="hljs-string">"brad pitt"</span>, <span class="hljs-string">"leonardo di caprio"</span>, <span class="hljs-string">"george clooney"</span>],
<span class="hljs-string">"age"</span>: [<span class="hljs-string">"56"</span>, <span class="hljs-string">"45"</span>, <span class="hljs-string">"59"</span>],
<span class="hljs-string">"number of movies"</span>: [<span class="hljs-string">"87"</span>, <span class="hljs-string">"53"</span>, <span class="hljs-string">"69"</span>],
<span class="hljs-string">"date of birth"</span>: [<span class="hljs-string">"7 february 1967"</span>, <span class="hljs-string">"10 june 1996"</span>, <span class="hljs-string">"28 november 1967"</span>],
}`}}),ks=new z({props:{code:`import pandas as pd
table = pd.DataFrame.from_dict(data)`,highlighted:`<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
table = pd.DataFrame.from_dict(data)`}}),Ps=new y({}),Ts=new w({props:{name:"class transformers.TextClassificationPipeline",anchor:"transformers.TextClassificationPipeline",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_classification.py#L47",parametersDescription:[{anchor:"transformers.TextClassificationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.TextClassificationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.TextClassificationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.TextClassificationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.TextClassificationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.TextClassificationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.TextClassificationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.TextClassificationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.TextClassificationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.TextClassificationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"},{anchor:"transformers.TextClassificationPipeline.return_all_scores",description:`<strong>return_all_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to return all prediction scores or just the one of the predicted class.`,name:"return_all_scores"},{anchor:"transformers.TextClassificationPipeline.function_to_apply",description:`<strong>function_to_apply</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"default"</code>) —
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:</p>
<ul>
<li><code>"default"</code>: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.</li>
<li><code>"sigmoid"</code>: Applies the sigmoid function on the output.</li>
<li><code>"softmax"</code>: Applies the softmax function on the output.</li>
<li><code>"none"</code>: Does not apply any function on the output.</li>
</ul>`,name:"function_to_apply"}]}}),Es=new w({props:{name:"__call__",anchor:"transformers.TextClassificationPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_classification.py#L92",parametersDescription:[{anchor:"transformers.TextClassificationPipeline.__call__.args",description:`<strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
One or several texts (or one list of prompts) to classify.`,name:"args"},{anchor:"transformers.TextClassificationPipeline.__call__.return_all_scores",description:`<strong>return_all_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to return scores for all labels.`,name:"return_all_scores"},{anchor:"transformers.TextClassificationPipeline.__call__.function_to_apply",description:`<strong>function_to_apply</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"default"</code>) —
The function to apply to the model outputs in order to retrieve the scores. Accepts four different
values:</p>
<p>If this argument is not specified, then it will apply the following functions according to the number
of labels:</p>
<ul>
<li>If the model has a single label, will apply the sigmoid function on the output.</li>
<li>If the model has several labels, will apply the softmax function on the output.</li>
</ul>
<p>Possible values are:</p>
<ul>
<li><code>"sigmoid"</code>: Applies the sigmoid function on the output.</li>
<li><code>"softmax"</code>: Applies the softmax function on the output.</li>
<li><code>"none"</code>: Does not apply any function on the output.</li>
</ul>`,name:"function_to_apply"}],returnDescription:`
<p>Each result comes as list of dictionaries with the following keys:</p>
<ul>
<li><strong>label</strong> (<code>str</code>) \u2014 The label predicted.</li>
<li><strong>score</strong> (<code>float</code>) \u2014 The corresponding probability.</li>
</ul>
<p>If <code>self.return_all_scores=True</code>, one such dictionary is returned per label.</p>
`,returnType:`
<p>A list or a list of list of <code>dict</code></p>
`}}),qs=new y({}),As=new w({props:{name:"class transformers.TextGenerationPipeline",anchor:"transformers.TextGenerationPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_generation.py#L20",parametersDescription:[{anchor:"transformers.TextGenerationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.TextGenerationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.TextGenerationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.TextGenerationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.TextGenerationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.TextGenerationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.TextGenerationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.TextGenerationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.TextGenerationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.TextGenerationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),Is=new w({props:{name:"__call__",anchor:"transformers.TextGenerationPipeline.__call__",parameters:[{name:"text_inputs",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_generation.py#L136",parametersDescription:[{anchor:"transformers.TextGenerationPipeline.__call__.args",description:`<strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
One or several prompts (or one list of prompts) to complete.`,name:"args"},{anchor:"transformers.TextGenerationPipeline.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.`,name:"return_tensors"},{anchor:"transformers.TextGenerationPipeline.__call__.return_text",description:`<strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs.`,name:"return_text"},{anchor:"transformers.TextGenerationPipeline.__call__.return_full_text",description:`<strong>return_full_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If set to <code>False</code> only added text is returned, otherwise the full text is returned Only meaningful if
<em>return_text</em> is set to True.`,name:"return_full_text"},{anchor:"transformers.TextGenerationPipeline.__call__.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.TextGenerationPipeline.__call__.prefix",description:`<strong>prefix</strong> (<code>str</code>, <em>optional</em>) —
Prefix added to prompt.`,name:"prefix"},{anchor:"transformers.TextGenerationPipeline.__call__.handle_long_generation",description:`<strong>handle_long_generation</strong> (<code>str</code>, <em>optional</em>) —
By default, this pipelines does not handle long generation (ones that exceed in one form or the other
the model maximum length). There is no perfect way to adress this (more info
:<a href="https://github.com/huggingface/transformers/issues/14033#issuecomment-948385227" rel="nofollow">https://github.com/huggingface/transformers/issues/14033#issuecomment-948385227</a>). This provides common
strategies to work around that problem depending on your use case.</p>
<ul>
<li><code>None</code> : default strategy where nothing in particular happens</li>
<li><code>"hole"</code>: Truncates left of input, and leaves a gap wide enough to let generation happen (might
truncate a lot of the prompt and not suitable when generation exceed the model capacity)</li>
</ul>`,name:"handle_long_generation"}],returnDescription:`
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>generated_text</strong> (<code>str</code>, present when <code>return_text=True</code>) \u2014 The generated text.</li>
<li><strong>generated_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) \u2014 The token
ids of the generated text.</li>
</ul>
`,returnType:`
<p>A list or a list of list of <code>dict</code></p>
`}}),js=new y({}),Ss=new w({props:{name:"class transformers.Text2TextGenerationPipeline",anchor:"transformers.Text2TextGenerationPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L26",parametersDescription:[{anchor:"transformers.Text2TextGenerationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.Text2TextGenerationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.Text2TextGenerationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.Text2TextGenerationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.Text2TextGenerationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.Text2TextGenerationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.Text2TextGenerationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.Text2TextGenerationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.Text2TextGenerationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.Text2TextGenerationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),Ls=new z({props:{code:`text2text_generator = pipeline("text2text-generation")
text2text_generator("question: What is 42 ? context: 42 is the answer to life, the universe and everything")`,highlighted:`text2text_generator = pipeline(<span class="hljs-string">"text2text-generation"</span>)
text2text_generator(<span class="hljs-string">"question: What is 42 ? context: 42 is the answer to life, the universe and everything"</span>)`}}),Us=new w({props:{name:"__call__",anchor:"transformers.Text2TextGenerationPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L109",parametersDescription:[{anchor:"transformers.Text2TextGenerationPipeline.__call__.args",description:`<strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
Input text for the encoder.`,name:"args"},{anchor:"transformers.Text2TextGenerationPipeline.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.`,name:"return_tensors"},{anchor:"transformers.Text2TextGenerationPipeline.__call__.return_text",description:`<strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs.`,name:"return_text"},{anchor:"transformers.Text2TextGenerationPipeline.__call__.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.Text2TextGenerationPipeline.__call__.truncation",description:`<strong>truncation</strong> (<code>TruncationStrategy</code>, <em>optional</em>, defaults to <code>TruncationStrategy.DO_NOT_TRUNCATE</code>) —
The truncation strategy for the tokenization within the pipeline. <code>TruncationStrategy.DO_NOT_TRUNCATE</code>
(default) will never truncate, but it is sometimes desirable to truncate the input to fit the model’s
max_length instead of throwing an error down the line.
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).`,name:"truncation"}],returnDescription:`
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>generated_text</strong> (<code>str</code>, present when <code>return_text=True</code>) \u2014 The generated text.</li>
<li><strong>generated_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) \u2014 The token
ids of the generated text.</li>
</ul>
`,returnType:`
<p>A list or a list of list of <code>dict</code></p>
`}}),Gs=new w({props:{name:"check_inputs",anchor:"transformers.Text2TextGenerationPipeline.check_inputs",parameters:[{name:"input_length",val:": int"},{name:"min_length",val:": int"},{name:"max_length",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L82"}}),Ns=new y({}),Os=new w({props:{name:"class transformers.TokenClassificationPipeline",anchor:"transformers.TokenClassificationPipeline",parameters:[{name:"args_parser",val:" = <transformers.pipelines.token_classification.TokenClassificationArgumentHandler object at 0x7f45b52f37c0>"},{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L86",parametersDescription:[{anchor:"transformers.TokenClassificationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.TokenClassificationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.TokenClassificationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.TokenClassificationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.TokenClassificationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.TokenClassificationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.TokenClassificationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.TokenClassificationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.TokenClassificationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.TokenClassificationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"},{anchor:"transformers.TokenClassificationPipeline.ignore_labels",description:`<strong>ignore_labels</strong> (<code>List[str]</code>, defaults to <code>["O"]</code>) —
A list of labels to ignore.`,name:"ignore_labels"},{anchor:"transformers.TokenClassificationPipeline.grouped_entities",description:`<strong>grouped_entities</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
DEPRECATED, use <code>aggregation_strategy</code> instead. Whether or not to group the tokens corresponding to the
same entity together in the predictions or not.`,name:"grouped_entities"},{anchor:"transformers.TokenClassificationPipeline.aggregation_strategy",description:`<strong>aggregation_strategy</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"none"</code>) —
The strategy to fuse (or not) tokens based on the model prediction.</p>
<ul>
<li>“none” : Will simply not do any aggregation and simply return raw results from the model</li>
<li>“simple” : Will attempt to group entities following the default schema. (A, B-TAG), (B, I-TAG), (C,
I-TAG), (D, B-TAG2) (E, B-TAG2) will end up being [{“word”: ABC, “entity”: “TAG”}, {“word”: “D”,
“entity”: “TAG2”}, {“word”: “E”, “entity”: “TAG2”}] Notice that two consecutive B tags will end up as
different entities. On word based languages, we might end up splitting words undesirably : Imagine
Microsoft being tagged as [{“word”: “Micro”, “entity”: “ENTERPRISE”}, {“word”: “soft”, “entity”:
“NAME”}]. Look for FIRST, MAX, AVERAGE for ways to mitigate that and disambiguate words (on languages
that support that meaning, which is basically tokens separated by a space). These mitigations will
only work on real words, “New york” might still be tagged with two different entities.</li>
<li>“first” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Words will simply use the tag of the first token of the word when there
is ambiguity.</li>
<li>“average” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words,
cannot end up with different tags. scores will be averaged first across tokens, and then the maximum
label is applied.</li>
<li>“max” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Word entity will simply be the token with the maximum score.</li>
</ul>`,name:"aggregation_strategy"}]}}),Ws=new w({props:{name:"__call__",anchor:"transformers.TokenClassificationPipeline.__call__",parameters:[{name:"inputs",val:": typing.Union[str, typing.List[str]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L160",parametersDescription:[{anchor:"transformers.TokenClassificationPipeline.__call__.inputs",description:`<strong>inputs</strong> (<code>str</code> or <code>List[str]</code>) —
One or several texts (or one list of texts) for token classification.`,name:"inputs"}],returnDescription:`
<p>Each result comes as a list of dictionaries (one for each token in the
corresponding input, or each entity if this pipeline was instantiated with an aggregation_strategy) with
the following keys:</p>
<ul>
<li><strong>word</strong> (<code>str</code>) \u2014 The token/word classified.</li>
<li><strong>score</strong> (<code>float</code>) \u2014 The corresponding probability for <code>entity</code>.</li>
<li><strong>entity</strong> (<code>str</code>) \u2014 The entity predicted for that token/word (it is named <em>entity_group</em> when
<em>aggregation_strategy</em> is not <code>"none"</code>.</li>
<li><strong>index</strong> (<code>int</code>, only present when <code>aggregation_strategy="none"</code>) \u2014 The index of the corresponding
token in the sentence.</li>
<li><strong>start</strong> (<code>int</code>, <em>optional</em>) \u2014 The index of the start of the corresponding entity in the sentence. Only
exists if the offsets are available within the tokenizer</li>
<li><strong>end</strong> (<code>int</code>, <em>optional</em>) \u2014 The index of the end of the corresponding entity in the sentence. Only
exists if the offsets are available within the tokenizer</li>
</ul>
`,returnType:`
<p>A list or a list of list of <code>dict</code></p>
`}}),Qs=new w({props:{name:"aggregate_words",anchor:"transformers.TokenClassificationPipeline.aggregate_words",parameters:[{name:"entities",val:": typing.List[dict]"},{name:"aggregation_strategy",val:": AggregationStrategy"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L366"}}),Vs=new w({props:{name:"gather_pre_entities",anchor:"transformers.TokenClassificationPipeline.gather_pre_entities",parameters:[{name:"sentence",val:": str"},{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"offset_mapping",val:": typing.Union[typing.List[typing.Tuple[int, int]], NoneType]"},{name:"special_tokens_mask",val:": ndarray"},{name:"aggregation_strategy",val:": AggregationStrategy"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L252"}}),Bs=new w({props:{name:"group_entities",anchor:"transformers.TokenClassificationPipeline.group_entities",parameters:[{name:"entities",val:": typing.List[dict]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L428",parametersDescription:[{anchor:"transformers.TokenClassificationPipeline.group_entities.entities",description:"<strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.",name:"entities"}]}}),Zs=new w({props:{name:"group_sub_entities",anchor:"transformers.TokenClassificationPipeline.group_sub_entities",parameters:[{name:"entities",val:": typing.List[dict]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L393",parametersDescription:[{anchor:"transformers.TokenClassificationPipeline.group_sub_entities.entities",description:"<strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.",name:"entities"}]}}),Ys=new y({}),Xs=new w({props:{name:"class transformers.TranslationPipeline",anchor:"transformers.TranslationPipeline",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L253",parametersDescription:[{anchor:"transformers.TranslationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.TranslationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.TranslationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.TranslationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.TranslationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.TranslationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.TranslationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.TranslationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.TranslationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.TranslationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),ea=new z({props:{code:`en_fr_translator = pipeline("translation_en_to_fr")
en_fr_translator("How old are you?")`,highlighted:`en_fr_translator = pipeline(<span class="hljs-string">"translation_en_to_fr"</span>)
en_fr_translator(<span class="hljs-string">"How old are you?"</span>)`}}),ta=new w({props:{name:"__call__",anchor:"transformers.TranslationPipeline.__call__",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L305",parametersDescription:[{anchor:"transformers.TranslationPipeline.__call__.args",description:`<strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
Texts to be translated.`,name:"args"},{anchor:"transformers.TranslationPipeline.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.`,name:"return_tensors"},{anchor:"transformers.TranslationPipeline.__call__.return_text",description:`<strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs.`,name:"return_text"},{anchor:"transformers.TranslationPipeline.__call__.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.TranslationPipeline.__call__.src_lang",description:`<strong>src_lang</strong> (<code>str</code>, <em>optional</em>) —
The language of the input. Might be required for multilingual models. Will not have any effect for
single pair translation models`,name:"src_lang"},{anchor:"transformers.TranslationPipeline.__call__.tgt_lang",description:`<strong>tgt_lang</strong> (<code>str</code>, <em>optional</em>) —
The language of the desired output. Might be required for multilingual models. Will not have any effect
for single pair translation models
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).`,name:"tgt_lang"}],returnDescription:`
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>translation_text</strong> (<code>str</code>, present when <code>return_text=True</code>) \u2014 The translation.</li>
<li><strong>translation_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) \u2014 The
token ids of the translation.</li>
</ul>
`,returnType:`
<p>A list or a list of list of <code>dict</code></p>
`}}),na=new y({}),ra=new w({props:{name:"class transformers.ZeroShotClassificationPipeline",anchor:"transformers.ZeroShotClassificationPipeline",parameters:[{name:"args_parser",val:" = <transformers.pipelines.zero_shot_classification.ZeroShotClassificationArgumentHandler object at 0x7f45b52fe1c0>"},{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_classification.py#L47",parametersDescription:[{anchor:"transformers.ZeroShotClassificationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.ZeroShotClassificationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.ZeroShotClassificationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.ZeroShotClassificationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.ZeroShotClassificationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.ZeroShotClassificationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.ZeroShotClassificationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.ZeroShotClassificationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.ZeroShotClassificationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.ZeroShotClassificationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),ia=new w({props:{name:"__call__",anchor:"transformers.ZeroShotClassificationPipeline.__call__",parameters:[{name:"sequences",val:": typing.Union[str, typing.List[str]]"},{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_classification.py#L139",parametersDescription:[{anchor:"transformers.ZeroShotClassificationPipeline.__call__.sequences",description:`<strong>sequences</strong> (<code>str</code> or <code>List[str]</code>) —
The sequence(s) to classify, will be truncated if the model input is too large.`,name:"sequences"},{anchor:"transformers.ZeroShotClassificationPipeline.__call__.candidate_labels",description:`<strong>candidate_labels</strong> (<code>str</code> or <code>List[str]</code>) —
The set of possible class labels to classify each sequence into. Can be a single label, a string of
comma-separated labels, or a list of labels.`,name:"candidate_labels"},{anchor:"transformers.ZeroShotClassificationPipeline.__call__.hypothesis_template",description:`<strong>hypothesis_template</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"This example is {}."</code>) —
The template used to turn each label into an NLI-style hypothesis. This template must include a {} or
similar syntax for the candidate label to be inserted into the template. For example, the default
template is <code>"This example is {}."</code> With the candidate label <code>"sports"</code>, this would be fed into the
model like <code>"<cls> sequence to classify <sep> This example is sports . <sep>"</code>. The default template
works well in many cases, but it may be worthwhile to experiment with different templates depending on
the task setting.`,name:"hypothesis_template"},{anchor:"transformers.ZeroShotClassificationPipeline.__call__.multi_label",description:`<strong>multi_label</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not multiple candidate labels can be true. If <code>False</code>, the scores are normalized such that
the sum of the label likelihoods for each sequence is 1. If <code>True</code>, the labels are considered
independent and probabilities are normalized for each candidate by doing a softmax of the entailment
score vs. the contradiction score.`,name:"multi_label"}],returnDescription:`
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>sequence</strong> (<code>str</code>) \u2014 The sequence for which this is the output.</li>
<li><strong>labels</strong> (<code>List[str]</code>) \u2014 The labels sorted by order of likelihood.</li>
<li><strong>scores</strong> (<code>List[float]</code>) \u2014 The probabilities for each of the labels.</li>
</ul>
`,returnType:`
<p>A <code>dict</code> or a list of <code>dict</code></p>
`}}),pa=new y({}),ca=new w({props:{name:"class transformers.ZeroShotImageClassificationPipeline",anchor:"transformers.ZeroShotImageClassificationPipeline",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_image_classification.py#L29",parametersDescription:[{anchor:"transformers.ZeroShotImageClassificationPipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.ZeroShotImageClassificationPipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.ZeroShotImageClassificationPipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.ZeroShotImageClassificationPipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.ZeroShotImageClassificationPipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.ZeroShotImageClassificationPipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.ZeroShotImageClassificationPipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.ZeroShotImageClassificationPipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.ZeroShotImageClassificationPipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.ZeroShotImageClassificationPipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),fa=new w({props:{name:"__call__",anchor:"transformers.ZeroShotImageClassificationPipeline.__call__",parameters:[{name:"images",val:": typing.Union[str, typing.List[str], ForwardRef('Image'), typing.List[ForwardRef('Image')]]"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_image_classification.py#L48",parametersDescription:[{anchor:"transformers.ZeroShotImageClassificationPipeline.__call__.images",description:`<strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing a http link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul>`,name:"images"},{anchor:"transformers.ZeroShotImageClassificationPipeline.__call__.candidate_labels",description:`<strong>candidate_labels</strong> (<code>List[str]</code>) —
The candidate labels for this image`,name:"candidate_labels"},{anchor:"transformers.ZeroShotImageClassificationPipeline.__call__.hypothesis_template",description:`<strong>hypothesis_template</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"This is a photo of {}"</code>) —
The sentence used in cunjunction with <em>candidate_labels</em> to attempt the image classification by
replacing the placeholder with the candidate_labels. Then likelihood is estimated by using
logits_per_image`,name:"hypothesis_template"}],returnDescription:`
<p>A list of dictionaries containing result, one dictionnary per proposed label. The dictionaries contain the
following keys:</p>
<ul>
<li><strong>label</strong> (<code>str</code>) \u2014 The label identified by the model. It is one of the suggested <code>candidate_label</code>.</li>
<li><strong>score</strong> (<code>float</code>) \u2014 The score attributed by the model for that label (between 0 and 1).</li>
</ul>
`}}),ha=new y({}),ua=new w({props:{name:"class transformers.Pipeline",anchor:"transformers.Pipeline",parameters:[{name:"model",val:": typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]"},{name:"tokenizer",val:": typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None"},{name:"feature_extractor",val:": typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None"},{name:"modelcard",val:": typing.Optional[transformers.modelcard.ModelCard] = None"},{name:"framework",val:": typing.Optional[str] = None"},{name:"task",val:": str = ''"},{name:"args_parser",val:": ArgumentHandler = None"},{name:"device",val:": int = -1"},{name:"binary_output",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L711",parametersDescription:[{anchor:"transformers.Pipeline.model",description:`<strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.`,name:"model"},{anchor:"transformers.Pipeline.tokenizer",description:`<strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.`,name:"tokenizer"},{anchor:"transformers.Pipeline.modelcard",description:`<strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.`,name:"modelcard"},{anchor:"transformers.Pipeline.framework",description:`<strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.`,name:"framework"},{anchor:"transformers.Pipeline.task",description:`<strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.`,name:"task"},{anchor:"transformers.Pipeline.num_workers",description:`<strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.`,name:"num_workers"},{anchor:"transformers.Pipeline.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .`,name:"batch_size"},{anchor:"transformers.Pipeline.args_parser",description:`<strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.`,name:"args_parser"},{anchor:"transformers.Pipeline.device",description:`<strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.`,name:"device"},{anchor:"transformers.Pipeline.binary_output",description:`<strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.`,name:"binary_output"}]}}),ga=new w({props:{name:"check_model_type",anchor:"transformers.Pipeline.check_model_type",parameters:[{name:"supported_models",val:": typing.Union[typing.List[str], dict]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L864",parametersDescription:[{anchor:"transformers.Pipeline.check_model_type.supported_models",description:`<strong>supported_models</strong> (<code>List[str]</code> or <code>dict</code>) —
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the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
`),Ca=o(Ch,"A",{href:!0});var Yx=s(Ca);jg=i(Yx,"task summary"),Yx.forEach(n),Sg=i(Ch," for examples of use."),Ch.forEach(n),Bm=p(t),za=o(t,"P",{});var Xx=s(za);Mg=i(Xx,"There are two categories of pipeline abstractions to be aware about:"),Xx.forEach(n),Zm=p(t),Vt=o(t,"UL",{});var zh=s(Vt);yl=o(zh,"LI",{});var Kx=s(yl);wr=o(Kx,"P",{});var Dh=s(wr);Fg=i(Dh,"The "),Da=o(Dh,"A",{href:!0});var Jx=s(Da);Lg=i(Jx,"pipeline()"),Jx.forEach(n),Ug=i(Dh," which is the most powerful object encapsulating all other pipelines."),Dh.forEach(n),Kx.forEach(n),Gg=p(zh),br=o(zh,"LI",{});var Ih=s(br);xl=o(Ih,"P",{});var e3=s(xl);Ng=i(e3,"The other task-specific pipelines:"),e3.forEach(n),Og=p(Ih),b=o(Ih,"UL",{});var k=s(b);$l=o(k,"LI",{});var t3=s($l);Ia=o(t3,"A",{href:!0});var n3=s(Ia);Rg=i(n3,"AudioClassificationPipeline"),n3.forEach(n),t3.forEach(n),Hg=p(k),El=o(k,"LI",{});var r3=s(El);ja=o(r3,"A",{href:!0});var o3=s(ja);Wg=i(o3,"AutomaticSpeechRecognitionPipeline"),o3.forEach(n),r3.forEach(n),Qg=p(k),ql=o(k,"LI",{});var s3=s(ql);Sa=o(s3,"A",{href:!0});var a3=s(Sa);Vg=i(a3,"ConversationalPipeline"),a3.forEach(n),s3.forEach(n),Bg=p(k),Al=o(k,"LI",{});var i3=s(Al);Ma=o(i3,"A",{href:!0});var l3=s(Ma);Zg=i(l3,"FeatureExtractionPipeline"),l3.forEach(n),i3.forEach(n),Yg=p(k),Cl=o(k,"LI",{});var p3=s(Cl);Fa=o(p3,"A",{href:!0});var c3=s(Fa);Xg=i(c3,"FillMaskPipeline"),c3.forEach(n),p3.forEach(n),Kg=p(k),zl=o(k,"LI",{});var d3=s(zl);La=o(d3,"A",{href:!0});var m3=s(La);Jg=i(m3,"ImageClassificationPipeline"),m3.forEach(n),d3.forEach(n),e_=p(k),Dl=o(k,"LI",{});var f3=s(Dl);Ua=o(f3,"A",{href:!0});var h3=s(Ua);t_=i(h3,"ImageSegmentationPipeline"),h3.forEach(n),f3.forEach(n),n_=p(k),Il=o(k,"LI",{});var u3=s(Il);Ga=o(u3,"A",{href:!0});var g3=s(Ga);r_=i(g3,"ObjectDetectionPipeline"),g3.forEach(n),u3.forEach(n),o_=p(k),jl=o(k,"LI",{});var _3=s(jl);Na=o(_3,"A",{href:!0});var v3=s(Na);s_=i(v3,"QuestionAnsweringPipeline"),v3.forEach(n),_3.forEach(n),a_=p(k),Sl=o(k,"LI",{});var w3=s(Sl);Oa=o(w3,"A",{href:!0});var b3=s(Oa);i_=i(b3,"SummarizationPipeline"),b3.forEach(n),w3.forEach(n),l_=p(k),Ml=o(k,"LI",{});var k3=s(Ml);Ra=o(k3,"A",{href:!0});var P3=s(Ra);p_=i(P3,"TableQuestionAnsweringPipeline"),P3.forEach(n),k3.forEach(n),c_=p(k),Fl=o(k,"LI",{});var T3=s(Fl);Ha=o(T3,"A",{href:!0});var y3=s(Ha);d_=i(y3,"TextClassificationPipeline"),y3.forEach(n),T3.forEach(n),m_=p(k),Ll=o(k,"LI",{});var x3=s(Ll);Wa=o(x3,"A",{href:!0});var $3=s(Wa);f_=i($3,"TextGenerationPipeline"),$3.forEach(n),x3.forEach(n),h_=p(k),Ul=o(k,"LI",{});var E3=s(Ul);Qa=o(E3,"A",{href:!0});var q3=s(Qa);u_=i(q3,"Text2TextGenerationPipeline"),q3.forEach(n),E3.forEach(n),g_=p(k),Gl=o(k,"LI",{});var A3=s(Gl);Va=o(A3,"A",{href:!0});var C3=s(Va);__=i(C3,"TokenClassificationPipeline"),C3.forEach(n),A3.forEach(n),v_=p(k),Nl=o(k,"LI",{});var z3=s(Nl);Ba=o(z3,"A",{href:!0});var D3=s(Ba);w_=i(D3,"TranslationPipeline"),D3.forEach(n),z3.forEach(n),b_=p(k),Ol=o(k,"LI",{});var I3=s(Ol);Za=o(I3,"A",{href:!0});var j3=s(Za);k_=i(j3,"ZeroShotClassificationPipeline"),j3.forEach(n),I3.forEach(n),P_=p(k),Rl=o(k,"LI",{});var S3=s(Rl);Ya=o(S3,"A",{href:!0});var M3=s(Ya);T_=i(M3,"ZeroShotImageClassificationPipeline"),M3.forEach(n),S3.forEach(n),k.forEach(n),Ih.forEach(n),zh.forEach(n),Ym=p(t),Ve=o(t,"H2",{class:!0});var jh=s(Ve);Bt=o(jh,"A",{id:!0,class:!0,href:!0});var F3=s(Bt);Hl=o(F3,"SPAN",{});var L3=s(Hl);h(kr.$$.fragment,L3),L3.forEach(n),F3.forEach(n),y_=p(jh),Wl=o(jh,"SPAN",{});var U3=s(Wl);x_=i(U3,"The pipeline abstraction"),U3.forEach(n),jh.forEach(n),Xm=p(t),Zt=o(t,"P",{});var Sh=s(Zt);$_=i(Sh,"The "),Ql=o(Sh,"EM",{});var G3=s(Ql);E_=i(G3,"pipeline"),G3.forEach(n),q_=i(Sh,` abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
pipeline but can provide additional quality of life.`),Sh.forEach(n),Km=p(t),Xa=o(t,"P",{});var N3=s(Xa);A_=i(N3,"Simple call on one item:"),N3.forEach(n),Jm=p(t),h(Pr.$$.fragment,t),ef=p(t),Yt=o(t,"P",{});var Mh=s(Yt);C_=i(Mh,"If you want to use a specific model from the "),Tr=o(Mh,"A",{href:!0,rel:!0});var O3=s(Tr);z_=i(O3,"hub"),O3.forEach(n),D_=i(Mh,` you can ignore the task if the model on
the hub already defines it:`),Mh.forEach(n),tf=p(t),h(yr.$$.fragment,t),nf=p(t),Xt=o(t,"P",{});var Fh=s(Xt);I_=i(Fh,"To call a pipeline on many items, you can either call with a "),Vl=o(Fh,"EM",{});var R3=s(Vl);j_=i(R3,"list"),R3.forEach(n),S_=i(Fh,"."),Fh.forEach(n),rf=p(t),h(xr.$$.fragment,t),of=p(t),Kt=o(t,"P",{});var Lh=s(Kt);M_=i(Lh,"To iterate of full datasets it is recommended to use a "),Bl=o(Lh,"CODE",{});var H3=s(Bl);F_=i(H3,"dataset"),H3.forEach(n),L_=i(Lh,` directly. This means you don\u2019t need to allocate
the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
GPU. If it doesn\u2019t don\u2019t hesitate to create an issue.`),Lh.forEach(n),sf=p(t),h($r.$$.fragment,t),af=p(t),Ka=o(t,"P",{});var W3=s(Ka);U_=i(W3,"For ease of use, a generator is also possible:"),W3.forEach(n),lf=p(t),h(Er.$$.fragment,t),pf=p(t),R=o(t,"DIV",{class:!0});var we=s(R);h(qr.$$.fragment,we),G_=p(we),Ar=o(we,"P",{});var Uh=s(Ar);N_=i(Uh,"Utility factory method to build a "),Ja=o(Uh,"A",{href:!0});var Q3=s(Ja);O_=i(Q3,"Pipeline"),Q3.forEach(n),R_=i(Uh,"."),Uh.forEach(n),H_=p(we),Zl=o(we,"P",{});var V3=s(Zl);W_=i(V3,"Pipelines are made of:"),V3.forEach(n),Q_=p(we),Be=o(we,"UL",{});var Hi=s(Be);Cr=o(Hi,"LI",{});var Gh=s(Cr);V_=i(Gh,"A "),ei=o(Gh,"A",{href:!0});var B3=s(ei);B_=i(B3,"tokenizer"),B3.forEach(n),Z_=i(Gh," in charge of mapping raw textual input to token."),Gh.forEach(n),Y_=p(Hi),zr=o(Hi,"LI",{});var Nh=s(zr);X_=i(Nh,"A "),ti=o(Nh,"A",{href:!0});var Z3=s(ti);K_=i(Z3,"model"),Z3.forEach(n),J_=i(Nh," to make predictions from the inputs."),Nh.forEach(n),e1=p(Hi),Yl=o(Hi,"LI",{});var Y3=s(Yl);t1=i(Y3,"Some (optional) post processing for enhancing model\u2019s output."),Y3.forEach(n),Hi.forEach(n),n1=p(we),Xl=o(we,"P",{});var X3=s(Xl);r1=i(X3,"Examples:"),X3.forEach(n),o1=p(we),h(Dr.$$.fragment,we),we.forEach(n),cf=p(t),Ze=o(t,"H2",{class:!0});var Oh=s(Ze);Jt=o(Oh,"A",{id:!0,class:!0,href:!0});var K3=s(Jt);Kl=o(K3,"SPAN",{});var J3=s(Kl);h(Ir.$$.fragment,J3),J3.forEach(n),K3.forEach(n),s1=p(Oh),Jl=o(Oh,"SPAN",{});var e$=s(Jl);a1=i(e$,"Pipeline batching"),e$.forEach(n),Oh.forEach(n),df=p(t),qe=o(t,"P",{});var Wi=s(qe);i1=i(Wi,`All pipelines can use batching. This will work
whenever the pipeline uses its streaming ability (so when passing lists or `),ep=o(Wi,"CODE",{});var t$=s(ep);l1=i(t$,"Dataset"),t$.forEach(n),p1=i(Wi," or "),tp=o(Wi,"CODE",{});var n$=s(tp);c1=i(n$,"generator"),n$.forEach(n),d1=i(Wi,")."),Wi.forEach(n),mf=p(t),h(jr.$$.fragment,t),ff=p(t),h(en.$$.fragment,t),hf=p(t),h(Sr.$$.fragment,t),uf=p(t),h(Mr.$$.fragment,t),gf=p(t),ni=o(t,"P",{});var r$=s(ni);m1=i(r$,"Example where it\u2019s most a slowdown:"),r$.forEach(n),_f=p(t),h(Fr.$$.fragment,t),vf=p(t),tn=o(t,"P",{});var Rh=s(tn);f1=i(Rh,"This is a occasional very long sentence compared to the other. In that case, the "),np=o(Rh,"STRONG",{});var o$=s(np);h1=i(o$,"whole"),o$.forEach(n),u1=i(Rh,` batch will need to be 400
tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on
bigger batches, the program simply crashes.`),Rh.forEach(n),wf=p(t),h(Lr.$$.fragment,t),bf=p(t),ri=o(t,"P",{});var s$=s(ri);g1=i(s$,`There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of
thumb:`),s$.forEach(n),kf=p(t),oi=o(t,"P",{});var a$=s(oi);_1=i(a$,"For users, a rule of thumb is:"),a$.forEach(n),Pf=p(t),le=o(t,"UL",{});var Se=s(le);rp=o(Se,"LI",{});var i$=s(rp);op=o(i$,"P",{});var l$=s(op);sp=o(l$,"STRONG",{});var p$=s(sp);v1=i(p$,`Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the
only way to go.`),p$.forEach(n),l$.forEach(n),i$.forEach(n),w1=p(Se),ap=o(Se,"LI",{});var c$=s(ap);ip=o(c$,"P",{});var d$=s(ip);b1=i(d$,"If you are latency constrained (live product doing inference), don\u2019t batch"),d$.forEach(n),c$.forEach(n),k1=p(Se),lp=o(Se,"LI",{});var m$=s(lp);pp=o(m$,"P",{});var f$=s(pp);P1=i(f$,"If you are using CPU, don\u2019t batch."),f$.forEach(n),m$.forEach(n),T1=p(Se),Ur=o(Se,"LI",{});var Hh=s(Ur);cp=o(Hh,"P",{});var h$=s(cp);y1=i(h$,"If you are using throughput (you want to run your model on a bunch of static data), on GPU, then:"),h$.forEach(n),x1=p(Hh),Ye=o(Hh,"UL",{});var Qi=s(Ye);dp=o(Qi,"LI",{});var u$=s(dp);$1=i(u$,`If you have no clue about the size of the sequence_length (\u201Cnatural\u201D data), by default don\u2019t batch, measure and
try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don\u2019t
control the sequence_length.)`),u$.forEach(n),E1=p(Qi),mp=o(Qi,"LI",{});var g$=s(mp);q1=i(g$,`If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push
it until you get OOMs.`),g$.forEach(n),A1=p(Qi),fp=o(Qi,"LI",{});var _$=s(fp);C1=i(_$,"The larger the GPU the more likely batching is going to be more interesting"),_$.forEach(n),Qi.forEach(n),Hh.forEach(n),z1=p(Se),hp=o(Se,"LI",{});var v$=s(hp);up=o(v$,"P",{});var w$=s(up);D1=i(w$,"As soon as you enable batching, make sure you can handle OOMs nicely."),w$.forEach(n),v$.forEach(n),Se.forEach(n),Tf=p(t),Xe=o(t,"H2",{class:!0});var Wh=s(Xe);nn=o(Wh,"A",{id:!0,class:!0,href:!0});var b$=s(nn);gp=o(b$,"SPAN",{});var k$=s(gp);h(Gr.$$.fragment,k$),k$.forEach(n),b$.forEach(n),I1=p(Wh),_p=o(Wh,"SPAN",{});var P$=s(_p);j1=i(P$,"Pipeline chunk batching"),P$.forEach(n),Wh.forEach(n),yf=p(t),ye=o(t,"P",{});var Aa=s(ye);vp=o(Aa,"CODE",{});var T$=s(vp);S1=i(T$,"zero-shot-classification"),T$.forEach(n),M1=i(Aa," and "),wp=o(Aa,"CODE",{});var y$=s(wp);F1=i(y$,"question-answering"),y$.forEach(n),L1=i(Aa,` are slightly specific in the sense, that a single input might yield
multiple forward pass of a model. Under normal circumstances, this would yield issues with `),bp=o(Aa,"CODE",{});var x$=s(bp);U1=i(x$,"batch_size"),x$.forEach(n),G1=i(Aa," argument."),Aa.forEach(n),xf=p(t),Ae=o(t,"P",{});var Vi=s(Ae);N1=i(Vi,"In order to circumvent this issue, both of these pipelines are a bit specific, they are "),kp=o(Vi,"CODE",{});var $$=s(kp);O1=i($$,"ChunkPipeline"),$$.forEach(n),R1=i(Vi,` instead of
regular `),Pp=o(Vi,"CODE",{});var E$=s(Pp);H1=i(E$,"Pipeline"),E$.forEach(n),W1=i(Vi,". In short:"),Vi.forEach(n),$f=p(t),h(Nr.$$.fragment,t),Ef=p(t),si=o(t,"P",{});var q$=s(si);Q1=i(q$,"Now becomes:"),q$.forEach(n),qf=p(t),h(Or.$$.fragment,t),Af=p(t),ai=o(t,"P",{});var A$=s(ai);V1=i(A$,`This should be very transparent to your code because the pipelines are used in
the same way.`),A$.forEach(n),Cf=p(t),rn=o(t,"P",{});var Qh=s(rn);B1=i(Qh,`This is a simplified view, since the pipeline can handle automatically the batch to ! Meaning you don\u2019t have to care
about how many forward passes you inputs are actually going to trigger, you can optimize the `),Tp=o(Qh,"CODE",{});var C$=s(Tp);Z1=i(C$,"batch_size"),C$.forEach(n),Y1=i(Qh,`
independently of the inputs. The caveats from the previous section still apply.`),Qh.forEach(n),zf=p(t),Ke=o(t,"H2",{class:!0});var Vh=s(Ke);on=o(Vh,"A",{id:!0,class:!0,href:!0});var z$=s(on);yp=o(z$,"SPAN",{});var D$=s(yp);h(Rr.$$.fragment,D$),D$.forEach(n),z$.forEach(n),X1=p(Vh),xp=o(Vh,"SPAN",{});var I$=s(xp);K1=i(I$,"Pipeline custom code"),I$.forEach(n),Vh.forEach(n),Df=p(t),ii=o(t,"P",{});var j$=s(ii);J1=i(j$,"If you want to override a specific pipeline."),j$.forEach(n),If=p(t),sn=o(t,"P",{});var Bh=s(sn);ev=i(Bh,`Don\u2019t hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
cases, so `),$p=o(Bh,"CODE",{});var S$=s($p);tv=i(S$,"transformers"),S$.forEach(n),nv=i(Bh," could maybe support your use case."),Bh.forEach(n),jf=p(t),li=o(t,"P",{});var M$=s(li);rv=i(M$,"If you want to try simply you can:"),M$.forEach(n),Sf=p(t),pi=o(t,"UL",{});var F$=s(pi);Ep=o(F$,"LI",{});var L$=s(Ep);ov=i(L$,"Subclass your pipeline of choice"),L$.forEach(n),F$.forEach(n),Mf=p(t),h(Hr.$$.fragment,t),Ff=p(t),ci=o(t,"P",{});var U$=s(ci);sv=i(U$,"That should enable you to do all the custom code you want."),U$.forEach(n),Lf=p(t),Je=o(t,"H2",{class:!0});var Zh=s(Je);an=o(Zh,"A",{id:!0,class:!0,href:!0});var G$=s(an);qp=o(G$,"SPAN",{});var N$=s(qp);h(Wr.$$.fragment,N$),N$.forEach(n),G$.forEach(n),av=p(Zh),Ap=o(Zh,"SPAN",{});var O$=s(Ap);iv=i(O$,"Implementing a pipeline"),O$.forEach(n),Zh.forEach(n),Uf=p(t),di=o(t,"P",{});var R$=s(di);mi=o(R$,"A",{href:!0});var H$=s(mi);lv=i(H$,"Implementing a new pipeline"),H$.forEach(n),R$.forEach(n),Gf=p(t),et=o(t,"H2",{class:!0});var Yh=s(et);ln=o(Yh,"A",{id:!0,class:!0,href:!0});var W$=s(ln);Cp=o(W$,"SPAN",{});var Q$=s(Cp);h(Qr.$$.fragment,Q$),Q$.forEach(n),W$.forEach(n),pv=p(Yh),zp=o(Yh,"SPAN",{});var V$=s(zp);cv=i(V$,"The task specific pipelines"),V$.forEach(n),Yh.forEach(n),Nf=p(t),tt=o(t,"H3",{class:!0});var Xh=s(tt);pn=o(Xh,"A",{id:!0,class:!0,href:!0});var B$=s(pn);Dp=o(B$,"SPAN",{});var Z$=s(Dp);h(Vr.$$.fragment,Z$),Z$.forEach(n),B$.forEach(n),dv=p(Xh),Ip=o(Xh,"SPAN",{});var Y$=s(Ip);mv=i(Y$,"AudioClassificationPipeline"),Y$.forEach(n),Xh.forEach(n),Of=p(t),J=o(t,"DIV",{class:!0});var Me=s(J);h(Br.$$.fragment,Me),fv=p(Me),Zr=o(Me,"P",{});var Kh=s(Zr);hv=i(Kh,"Audio classification pipeline using any "),jp=o(Kh,"CODE",{});var X$=s(jp);uv=i(X$,"AutoModelForAudioClassification"),X$.forEach(n),gv=i(Kh,`. This pipeline predicts the class of a
raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio
formats.`),Kh.forEach(n),_v=p(Me),nt=o(Me,"P",{});var Bi=s(nt);vv=i(Bi,"This pipeline can currently be loaded from "),fi=o(Bi,"A",{href:!0});var K$=s(fi);wv=i(K$,"pipeline()"),K$.forEach(n),bv=i(Bi,` using the following task identifier:
`),Sp=o(Bi,"CODE",{});var J$=s(Sp);kv=i(J$,'"audio-classification"'),J$.forEach(n),Pv=i(Bi,"."),Bi.forEach(n),Tv=p(Me),Yr=o(Me,"P",{});var Jh=s(Yr);yv=i(Jh,`See the list of available models on
`),Xr=o(Jh,"A",{href:!0,rel:!0});var eE=s(Xr);xv=i(eE,"huggingface.co/models"),eE.forEach(n),$v=i(Jh,"."),Jh.forEach(n),Ev=p(Me),cn=o(Me,"DIV",{class:!0});var eu=s(cn);h(Kr.$$.fragment,eu),qv=p(eu),Jr=o(eu,"P",{});var tu=s(Jr);Av=i(tu,"Classify the sequence(s) given as inputs. See the "),hi=o(tu,"A",{href:!0});var tE=s(hi);Cv=i(tE,"AutomaticSpeechRecognitionPipeline"),tE.forEach(n),zv=i(tu,` documentation for more
information.`),tu.forEach(n),eu.forEach(n),Me.forEach(n),Rf=p(t),rt=o(t,"H3",{class:!0});var nu=s(rt);dn=o(nu,"A",{id:!0,class:!0,href:!0});var nE=s(dn);Mp=o(nE,"SPAN",{});var rE=s(Mp);h(eo.$$.fragment,rE),rE.forEach(n),nE.forEach(n),Dv=p(nu),Fp=o(nu,"SPAN",{});var oE=s(Fp);Iv=i(oE,"AutomaticSpeechRecognitionPipeline"),oE.forEach(n),nu.forEach(n),Hf=p(t),he=o(t,"DIV",{class:!0});var fr=s(he);h(to.$$.fragment,fr),jv=p(fr),Lp=o(fr,"P",{});var sE=s(Lp);Sv=i(sE,"Pipeline that aims at extracting spoken text contained within some audio."),sE.forEach(n),Mv=p(fr),Up=o(fr,"P",{});var aE=s(Up);Fv=i(aE,`The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for
to support multiple audio formats`),aE.forEach(n),Lv=p(fr),mn=o(fr,"DIV",{class:!0});var ru=s(mn);h(no.$$.fragment,ru),Uv=p(ru),ro=o(ru,"P",{});var ou=s(ro);Gv=i(ou,"Classify the sequence(s) given as inputs. See the "),ui=o(ou,"A",{href:!0});var iE=s(ui);Nv=i(iE,"AutomaticSpeechRecognitionPipeline"),iE.forEach(n),Ov=i(ou,` documentation for more
information.`),ou.forEach(n),ru.forEach(n),fr.forEach(n),Wf=p(t),ot=o(t,"H3",{class:!0});var su=s(ot);fn=o(su,"A",{id:!0,class:!0,href:!0});var lE=s(fn);Gp=o(lE,"SPAN",{});var pE=s(Gp);h(oo.$$.fragment,pE),pE.forEach(n),lE.forEach(n),Rv=p(su),Np=o(su,"SPAN",{});var cE=s(Np);Hv=i(cE,"ConversationalPipeline"),cE.forEach(n),su.forEach(n),Qf=p(t),D=o(t,"DIV",{class:!0});var B=s(D);h(so.$$.fragment,B),Wv=p(B),xe=o(B,"P",{});var hr=s(xe);Qv=i(hr,`Utility class containing a conversation and its history. This class is meant to be used as an input to the
`),gi=o(hr,"A",{href:!0});var dE=s(gi);Vv=i(dE,"ConversationalPipeline"),dE.forEach(n),Bv=i(hr,`. The conversation contains a number of utility function to manage the addition of new
user input and generated model responses. A conversation needs to contain an unprocessed user input before being
passed to the `),_i=o(hr,"A",{href:!0});var mE=s(_i);Zv=i(mE,"ConversationalPipeline"),mE.forEach(n),Yv=i(hr,`. This user input is either created when the class is instantiated, or by
calling `),Op=o(hr,"CODE",{});var fE=s(Op);Xv=i(fE,'conversational_pipeline.append_response("input")'),fE.forEach(n),Kv=i(hr," after a conversation turn."),hr.forEach(n),Jv=p(B),Rp=o(B,"P",{});var hE=s(Rp);ew=i(hE,"Usage:"),hE.forEach(n),tw=p(B),h(ao.$$.fragment,B),nw=p(B),hn=o(B,"DIV",{class:!0});var au=s(hn);h(io.$$.fragment,au),rw=p(au),lo=o(au,"P",{});var iu=s(lo);ow=i(iu,"Add a user input to the conversation for the next round. This populates the internal "),Hp=o(iu,"CODE",{});var uE=s(Hp);sw=i(uE,"new_user_input"),uE.forEach(n),aw=i(iu," field."),iu.forEach(n),au.forEach(n),iw=p(B),un=o(B,"DIV",{class:!0});var lu=s(un);h(po.$$.fragment,lu),lw=p(lu),Wp=o(lu,"P",{});var gE=s(Wp);pw=i(gE,"Append a response to the list of generated responses."),gE.forEach(n),lu.forEach(n),cw=p(B),Ce=o(B,"DIV",{class:!0});var Zi=s(Ce);h(co.$$.fragment,Zi),dw=p(Zi),Qp=o(Zi,"P",{});var _E=s(Qp);mw=i(_E,"Iterates over all blobs of the conversation."),_E.forEach(n),fw=p(Zi),ue=o(Zi,"P",{});var Fe=s(ue);hw=i(Fe,"Returns: Iterator of (is_user, text_chunk) in chronological order of the conversation. "),Vp=o(Fe,"CODE",{});var vE=s(Vp);uw=i(vE,"is_user"),vE.forEach(n),gw=i(Fe," is a "),Bp=o(Fe,"CODE",{});var wE=s(Bp);_w=i(wE,"bool"),wE.forEach(n),vw=i(Fe,`,
`),Zp=o(Fe,"CODE",{});var bE=s(Zp);ww=i(bE,"text_chunks"),bE.forEach(n),bw=i(Fe," is a "),Yp=o(Fe,"CODE",{});var kE=s(Yp);kw=i(kE,"str"),kE.forEach(n),Pw=i(Fe,"."),Fe.forEach(n),Zi.forEach(n),Tw=p(B),gn=o(B,"DIV",{class:!0});var pu=s(gn);h(mo.$$.fragment,pu),yw=p(pu),$e=o(pu,"P",{});var ur=s($e);xw=i(ur,"Mark the conversation as processed (moves the content of "),Xp=o(ur,"CODE",{});var PE=s(Xp);$w=i(PE,"new_user_input"),PE.forEach(n),Ew=i(ur," to "),Kp=o(ur,"CODE",{});var TE=s(Kp);qw=i(TE,"past_user_inputs"),TE.forEach(n),Aw=i(ur,`) and empties
the `),Jp=o(ur,"CODE",{});var yE=s(Jp);Cw=i(yE,"new_user_input"),yE.forEach(n),zw=i(ur," field."),ur.forEach(n),pu.forEach(n),B.forEach(n),Vf=p(t),M=o(t,"DIV",{class:!0});var pe=s(M);h(fo.$$.fragment,pe),Dw=p(pe),ec=o(pe,"P",{});var xE=s(ec);Iw=i(xE,"Multi-turn conversational pipeline."),xE.forEach(n),jw=p(pe),st=o(pe,"P",{});var Yi=s(st);Sw=i(Yi,"This conversational pipeline can currently be loaded from "),vi=o(Yi,"A",{href:!0});var $E=s(vi);Mw=i($E,"pipeline()"),$E.forEach(n),Fw=i(Yi,` using the following task identifier:
`),tc=o(Yi,"CODE",{});var EE=s(tc);Lw=i(EE,'"conversational"'),EE.forEach(n),Uw=i(Yi,"."),Yi.forEach(n),Gw=p(pe),ge=o(pe,"P",{});var Le=s(ge);Nw=i(Le,`The models that this pipeline can use are models that have been fine-tuned on a multi-turn conversational task,
currently: `),nc=o(Le,"EM",{});var qE=s(nc);Ow=i(qE,"\u2018microsoft/DialoGPT-small\u2019"),qE.forEach(n),Rw=i(Le,", "),rc=o(Le,"EM",{});var AE=s(rc);Hw=i(AE,"\u2018microsoft/DialoGPT-medium\u2019"),AE.forEach(n),Ww=i(Le,", "),oc=o(Le,"EM",{});var CE=s(oc);Qw=i(CE,"\u2018microsoft/DialoGPT-large\u2019"),CE.forEach(n),Vw=i(Le,`. See the
up-to-date list of available models on
`),ho=o(Le,"A",{href:!0,rel:!0});var zE=s(ho);Bw=i(zE,"huggingface.co/models"),zE.forEach(n),Zw=i(Le,"."),Le.forEach(n),Yw=p(pe),sc=o(pe,"P",{});var DE=s(sc);Xw=i(DE,"Usage:"),DE.forEach(n),Kw=p(pe),h(uo.$$.fragment,pe),Jw=p(pe),_n=o(pe,"DIV",{class:!0});var cu=s(_n);h(go.$$.fragment,cu),eb=p(cu),ac=o(cu,"P",{});var IE=s(ac);tb=i(IE,"Generate responses for the conversation(s) given as inputs."),IE.forEach(n),cu.forEach(n),pe.forEach(n),Bf=p(t),at=o(t,"H3",{class:!0});var du=s(at);vn=o(du,"A",{id:!0,class:!0,href:!0});var jE=s(vn);ic=o(jE,"SPAN",{});var SE=s(ic);h(_o.$$.fragment,SE),SE.forEach(n),jE.forEach(n),nb=p(du),lc=o(du,"SPAN",{});var ME=s(lc);rb=i(ME,"FeatureExtractionPipeline"),ME.forEach(n),du.forEach(n),Zf=p(t),ee=o(t,"DIV",{class:!0});var Ue=s(ee);h(vo.$$.fragment,Ue),ob=p(Ue),pc=o(Ue,"P",{});var FE=s(pc);sb=i(FE,`Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
transformer, which can be used as features in downstream tasks.`),FE.forEach(n),ab=p(Ue),it=o(Ue,"P",{});var Xi=s(it);ib=i(Xi,"This feature extraction pipeline can currently be loaded from "),wi=o(Xi,"A",{href:!0});var LE=s(wi);lb=i(LE,"pipeline()"),LE.forEach(n),pb=i(Xi,` using the task identifier:
`),cc=o(Xi,"CODE",{});var UE=s(cc);cb=i(UE,'"feature-extraction"'),UE.forEach(n),db=i(Xi,"."),Xi.forEach(n),mb=p(Ue),wo=o(Ue,"P",{});var mu=s(wo);fb=i(mu,`All models may be used for this pipeline. See a list of all models, including community-contributed models on
`),bo=o(mu,"A",{href:!0,rel:!0});var GE=s(bo);hb=i(GE,"huggingface.co/models"),GE.forEach(n),ub=i(mu,"."),mu.forEach(n),gb=p(Ue),wn=o(Ue,"DIV",{class:!0});var fu=s(wn);h(ko.$$.fragment,fu),_b=p(fu),dc=o(fu,"P",{});var NE=s(dc);vb=i(NE,"Extract the features of the input(s)."),NE.forEach(n),fu.forEach(n),Ue.forEach(n),Yf=p(t),lt=o(t,"H3",{class:!0});var hu=s(lt);bn=o(hu,"A",{id:!0,class:!0,href:!0});var OE=s(bn);mc=o(OE,"SPAN",{});var RE=s(mc);h(Po.$$.fragment,RE),RE.forEach(n),OE.forEach(n),wb=p(hu),fc=o(hu,"SPAN",{});var HE=s(fc);bb=i(HE,"FillMaskPipeline"),HE.forEach(n),hu.forEach(n),Xf=p(t),H=o(t,"DIV",{class:!0});var be=s(H);h(To.$$.fragment,be),kb=p(be),pt=o(be,"P",{});var Ki=s(pt);Pb=i(Ki,"Masked language modeling prediction pipeline using any "),hc=o(Ki,"CODE",{});var WE=s(hc);Tb=i(WE,"ModelWithLMHead"),WE.forEach(n),yb=i(Ki,". See the "),bi=o(Ki,"A",{href:!0});var QE=s(bi);xb=i(QE,`masked language modeling
examples`),QE.forEach(n),$b=i(Ki," for more information."),Ki.forEach(n),Eb=p(be),ct=o(be,"P",{});var Ji=s(ct);qb=i(Ji,"This mask filling pipeline can currently be loaded from "),ki=o(Ji,"A",{href:!0});var VE=s(ki);Ab=i(VE,"pipeline()"),VE.forEach(n),Cb=i(Ji,` using the following task identifier:
`),uc=o(Ji,"CODE",{});var BE=s(uc);zb=i(BE,'"fill-mask"'),BE.forEach(n),Db=i(Ji,"."),Ji.forEach(n),Ib=p(be),yo=o(be,"P",{});var uu=s(yo);jb=i(uu,`The models that this pipeline can use are models that have been trained with a masked language modeling objective,
which includes the bi-directional models in the library. See the up-to-date list of available models on
`),xo=o(uu,"A",{href:!0,rel:!0});var ZE=s(xo);Sb=i(ZE,"huggingface.co/models"),ZE.forEach(n),Mb=i(uu,"."),uu.forEach(n),Fb=p(be),h(kn.$$.fragment,be),Lb=p(be),Pn=o(be,"DIV",{class:!0});var gu=s(Pn);h($o.$$.fragment,gu),Ub=p(gu),gc=o(gu,"P",{});var YE=s(gc);Gb=i(YE,"Fill the masked token in the text(s) given as inputs."),YE.forEach(n),gu.forEach(n),be.forEach(n),Kf=p(t),dt=o(t,"H3",{class:!0});var _u=s(dt);Tn=o(_u,"A",{id:!0,class:!0,href:!0});var XE=s(Tn);_c=o(XE,"SPAN",{});var KE=s(_c);h(Eo.$$.fragment,KE),KE.forEach(n),XE.forEach(n),Nb=p(_u),vc=o(_u,"SPAN",{});var JE=s(vc);Ob=i(JE,"ImageClassificationPipeline"),JE.forEach(n),_u.forEach(n),Jf=p(t),te=o(t,"DIV",{class:!0});var Ge=s(te);h(qo.$$.fragment,Ge),Rb=p(Ge),Ao=o(Ge,"P",{});var vu=s(Ao);Hb=i(vu,"Image classification pipeline using any "),wc=o(vu,"CODE",{});var e6=s(wc);Wb=i(e6,"AutoModelForImageClassification"),e6.forEach(n),Qb=i(vu,`. This pipeline predicts the class of an
image.`),vu.forEach(n),Vb=p(Ge),mt=o(Ge,"P",{});var el=s(mt);Bb=i(el,"This image classification pipeline can currently be loaded from "),Pi=o(el,"A",{href:!0});var t6=s(Pi);Zb=i(t6,"pipeline()"),t6.forEach(n),Yb=i(el,` using the following task identifier:
`),bc=o(el,"CODE",{});var n6=s(bc);Xb=i(n6,'"image-classification"'),n6.forEach(n),Kb=i(el,"."),el.forEach(n),Jb=p(Ge),Co=o(Ge,"P",{});var wu=s(Co);ek=i(wu,`See the list of available models on
`),zo=o(wu,"A",{href:!0,rel:!0});var r6=s(zo);tk=i(r6,"huggingface.co/models"),r6.forEach(n),nk=i(wu,"."),wu.forEach(n),rk=p(Ge),yn=o(Ge,"DIV",{class:!0});var bu=s(yn);h(Do.$$.fragment,bu),ok=p(bu),kc=o(bu,"P",{});var o6=s(kc);sk=i(o6,"Assign labels to the image(s) passed as inputs."),o6.forEach(n),bu.forEach(n),Ge.forEach(n),eh=p(t),ft=o(t,"H3",{class:!0});var ku=s(ft);xn=o(ku,"A",{id:!0,class:!0,href:!0});var s6=s(xn);Pc=o(s6,"SPAN",{});var a6=s(Pc);h(Io.$$.fragment,a6),a6.forEach(n),s6.forEach(n),ak=p(ku),Tc=o(ku,"SPAN",{});var i6=s(Tc);ik=i(i6,"ImageSegmentationPipeline"),i6.forEach(n),ku.forEach(n),th=p(t),ne=o(t,"DIV",{class:!0});var Ne=s(ne);h(jo.$$.fragment,Ne),lk=p(Ne),So=o(Ne,"P",{});var Pu=s(So);pk=i(Pu,"Image segmentation pipeline using any "),yc=o(Pu,"CODE",{});var l6=s(yc);ck=i(l6,"AutoModelForXXXSegmentation"),l6.forEach(n),dk=i(Pu,`. This pipeline predicts masks of objects and
their classes.`),Pu.forEach(n),mk=p(Ne),ht=o(Ne,"P",{});var tl=s(ht);fk=i(tl,"This image segmentation pipeline can currently be loaded from "),Ti=o(tl,"A",{href:!0});var p6=s(Ti);hk=i(p6,"pipeline()"),p6.forEach(n),uk=i(tl,` using the following task identifier:
`),xc=o(tl,"CODE",{});var c6=s(xc);gk=i(c6,'"image-segmentation"'),c6.forEach(n),_k=i(tl,"."),tl.forEach(n),vk=p(Ne),Mo=o(Ne,"P",{});var Tu=s(Mo);wk=i(Tu,`See the list of available models on
`),Fo=o(Tu,"A",{href:!0,rel:!0});var d6=s(Fo);bk=i(d6,"huggingface.co/models"),d6.forEach(n),kk=i(Tu,"."),Tu.forEach(n),Pk=p(Ne),$n=o(Ne,"DIV",{class:!0});var yu=s($n);h(Lo.$$.fragment,yu),Tk=p(yu),$c=o(yu,"P",{});var m6=s($c);yk=i(m6,"Perform segmentation (detect masks & classes) in the image(s) passed as inputs."),m6.forEach(n),yu.forEach(n),Ne.forEach(n),nh=p(t),ut=o(t,"H3",{class:!0});var xu=s(ut);En=o(xu,"A",{id:!0,class:!0,href:!0});var f6=s(En);Ec=o(f6,"SPAN",{});var h6=s(Ec);h(Uo.$$.fragment,h6),h6.forEach(n),f6.forEach(n),xk=p(xu),qc=o(xu,"SPAN",{});var u6=s(qc);$k=i(u6,"NerPipeline"),u6.forEach(n),xu.forEach(n),rh=p(t),I=o(t,"DIV",{class:!0});var Z=s(I);h(Go.$$.fragment,Z),Ek=p(Z),gt=o(Z,"P",{});var nl=s(gt);qk=i(nl,"Named Entity Recognition pipeline using any "),Ac=o(nl,"CODE",{});var g6=s(Ac);Ak=i(g6,"ModelForTokenClassification"),g6.forEach(n),Ck=i(nl,". See the "),yi=o(nl,"A",{href:!0});var _6=s(yi);zk=i(_6,`named entity recognition
examples`),_6.forEach(n),Dk=i(nl," for more information."),nl.forEach(n),Ik=p(Z),_t=o(Z,"P",{});var rl=s(_t);jk=i(rl,"This token recognition pipeline can currently be loaded from "),xi=o(rl,"A",{href:!0});var v6=s(xi);Sk=i(v6,"pipeline()"),v6.forEach(n),Mk=i(rl,` using the following task identifier:
`),Cc=o(rl,"CODE",{});var w6=s(Cc);Fk=i(w6,'"ner"'),w6.forEach(n),Lk=i(rl," (for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous)."),rl.forEach(n),Uk=p(Z),No=o(Z,"P",{});var $u=s(No);Gk=i($u,`The models that this pipeline can use are models that have been fine-tuned on a token classification task. See the
up-to-date list of available models on
`),Oo=o($u,"A",{href:!0,rel:!0});var b6=s(Oo);Nk=i(b6,"huggingface.co/models"),b6.forEach(n),Ok=i($u,"."),$u.forEach(n),Rk=p(Z),ze=o(Z,"DIV",{class:!0});var ol=s(ze);h(Ro.$$.fragment,ol),Hk=p(ol),zc=o(ol,"P",{});var k6=s(zc);Wk=i(k6,"Override tokens from a given word that disagree to force agreement on word boundaries."),k6.forEach(n),Qk=p(ol),Dc=o(ol,"P",{});var P6=s(Dc);Vk=i(P6,`Example: micro|soft| com|pany| B-ENT I-NAME I-ENT I-ENT will be rewritten with first strategy as microsoft|
company| B-ENT I-ENT`),P6.forEach(n),ol.forEach(n),Bk=p(Z),qn=o(Z,"DIV",{class:!0});var Eu=s(qn);h(Ho.$$.fragment,Eu),Zk=p(Eu),Ic=o(Eu,"P",{});var T6=s(Ic);Yk=i(T6,"Fuse various numpy arrays into dicts with all the information needed for aggregation"),T6.forEach(n),Eu.forEach(n),Xk=p(Z),An=o(Z,"DIV",{class:!0});var qu=s(An);h(Wo.$$.fragment,qu),Kk=p(qu),jc=o(qu,"P",{});var y6=s(jc);Jk=i(y6,"Find and group together the adjacent tokens with the same entity predicted."),y6.forEach(n),qu.forEach(n),eP=p(Z),Cn=o(Z,"DIV",{class:!0});var Au=s(Cn);h(Qo.$$.fragment,Au),tP=p(Au),Sc=o(Au,"P",{});var x6=s(Sc);nP=i(x6,"Group together the adjacent tokens with the same entity predicted."),x6.forEach(n),Au.forEach(n),Z.forEach(n),oh=p(t),zn=o(t,"P",{});var Cu=s(zn);rP=i(Cu,"See "),$i=o(Cu,"A",{href:!0});var $6=s($i);oP=i($6,"TokenClassificationPipeline"),$6.forEach(n),sP=i(Cu," for all details."),Cu.forEach(n),sh=p(t),vt=o(t,"H3",{class:!0});var zu=s(vt);Dn=o(zu,"A",{id:!0,class:!0,href:!0});var E6=s(Dn);Mc=o(E6,"SPAN",{});var q6=s(Mc);h(Vo.$$.fragment,q6),q6.forEach(n),E6.forEach(n),aP=p(zu),Fc=o(zu,"SPAN",{});var A6=s(Fc);iP=i(A6,"ObjectDetectionPipeline"),A6.forEach(n),zu.forEach(n),ah=p(t),re=o(t,"DIV",{class:!0});var Oe=s(re);h(Bo.$$.fragment,Oe),lP=p(Oe),Zo=o(Oe,"P",{});var Du=s(Zo);pP=i(Du,"Object detection pipeline using any "),Lc=o(Du,"CODE",{});var C6=s(Lc);cP=i(C6,"AutoModelForObjectDetection"),C6.forEach(n),dP=i(Du,`. This pipeline predicts bounding boxes of objects
and their classes.`),Du.forEach(n),mP=p(Oe),wt=o(Oe,"P",{});var sl=s(wt);fP=i(sl,"This object detection pipeline can currently be loaded from "),Ei=o(sl,"A",{href:!0});var z6=s(Ei);hP=i(z6,"pipeline()"),z6.forEach(n),uP=i(sl,` using the following task identifier:
`),Uc=o(sl,"CODE",{});var D6=s(Uc);gP=i(D6,'"object-detection"'),D6.forEach(n),_P=i(sl,"."),sl.forEach(n),vP=p(Oe),Yo=o(Oe,"P",{});var Iu=s(Yo);wP=i(Iu,"See the list of available models on "),Xo=o(Iu,"A",{href:!0,rel:!0});var I6=s(Xo);bP=i(I6,"huggingface.co/models"),I6.forEach(n),kP=i(Iu,"."),Iu.forEach(n),PP=p(Oe),In=o(Oe,"DIV",{class:!0});var ju=s(In);h(Ko.$$.fragment,ju),TP=p(ju),Gc=o(ju,"P",{});var j6=s(Gc);yP=i(j6,"Detect objects (bounding boxes & classes) in the image(s) passed as inputs."),j6.forEach(n),ju.forEach(n),Oe.forEach(n),ih=p(t),bt=o(t,"H3",{class:!0});var Su=s(bt);jn=o(Su,"A",{id:!0,class:!0,href:!0});var S6=s(jn);Nc=o(S6,"SPAN",{});var M6=s(Nc);h(Jo.$$.fragment,M6),M6.forEach(n),S6.forEach(n),xP=p(Su),Oc=o(Su,"SPAN",{});var F6=s(Oc);$P=i(F6,"QuestionAnsweringPipeline"),F6.forEach(n),Su.forEach(n),lh=p(t),j=o(t,"DIV",{class:!0});var Y=s(j);h(es.$$.fragment,Y),EP=p(Y),kt=o(Y,"P",{});var al=s(kt);qP=i(al,"Question Answering pipeline using any "),Rc=o(al,"CODE",{});var L6=s(Rc);AP=i(L6,"ModelForQuestionAnswering"),L6.forEach(n),CP=i(al,". See the "),qi=o(al,"A",{href:!0});var U6=s(qi);zP=i(U6,`question answering
examples`),U6.forEach(n),DP=i(al," for more information."),al.forEach(n),IP=p(Y),Pt=o(Y,"P",{});var il=s(Pt);jP=i(il,"This question answering pipeline can currently be loaded from "),Ai=o(il,"A",{href:!0});var G6=s(Ai);SP=i(G6,"pipeline()"),G6.forEach(n),MP=i(il,` using the following task identifier:
`),Hc=o(il,"CODE",{});var N6=s(Hc);FP=i(N6,'"question-answering"'),N6.forEach(n),LP=i(il,"."),il.forEach(n),UP=p(Y),ts=o(Y,"P",{});var Mu=s(ts);GP=i(Mu,`The models that this pipeline can use are models that have been fine-tuned on a question answering task. See the
up-to-date list of available models on
`),ns=o(Mu,"A",{href:!0,rel:!0});var O6=s(ns);NP=i(O6,"huggingface.co/models"),O6.forEach(n),OP=i(Mu,"."),Mu.forEach(n),RP=p(Y),Sn=o(Y,"DIV",{class:!0});var Fu=s(Sn);h(rs.$$.fragment,Fu),HP=p(Fu),Wc=o(Fu,"P",{});var R6=s(Wc);WP=i(R6,"Answer the question(s) given as inputs by using the context(s)."),R6.forEach(n),Fu.forEach(n),QP=p(Y),De=o(Y,"DIV",{class:!0});var ll=s(De);h(os.$$.fragment,ll),VP=p(ll),Mn=o(ll,"P",{});var Qm=s(Mn);BP=i(Qm,"QuestionAnsweringPipeline leverages the "),Qc=o(Qm,"CODE",{});var H6=s(Qc);ZP=i(H6,"SquadExample"),H6.forEach(n),YP=i(Qm,`internally. This helper method encapsulate all the
logic for converting question(s) and context(s) to `),Vc=o(Qm,"CODE",{});var W6=s(Vc);XP=i(W6,"SquadExample"),W6.forEach(n),Qm.forEach(n),KP=p(ll),Bc=o(ll,"P",{});var Q6=s(Bc);JP=i(Q6,"We currently support extractive question answering."),Q6.forEach(n),ll.forEach(n),eT=p(Y),Ie=o(Y,"DIV",{class:!0});var pl=s(Ie);h(ss.$$.fragment,pl),tT=p(pl),as=o(pl,"P",{});var Lu=s(as);nT=i(Lu,"Take the output of any "),Zc=o(Lu,"CODE",{});var V6=s(Zc);rT=i(V6,"ModelForQuestionAnswering"),V6.forEach(n),oT=i(Lu,` and will generate probabilities for each span to be the
actual answer.`),Lu.forEach(n),sT=p(pl),Yc=o(pl,"P",{});var B6=s(Yc);aT=i(B6,`In addition, it filters out some unwanted/impossible cases like answer len being greater than max_answer_len or
answer end position being before the starting position. The method supports output the k-best answer through
the topk argument.`),B6.forEach(n),pl.forEach(n),iT=p(Y),Fn=o(Y,"DIV",{class:!0});var Uu=s(Fn);h(is.$$.fragment,Uu),lT=p(Uu),Xc=o(Uu,"P",{});var Z6=s(Xc);pT=i(Z6,"When decoding from token probabilities, this method maps token indexes to actual word in the initial context."),Z6.forEach(n),Uu.forEach(n),Y.forEach(n),ph=p(t),Tt=o(t,"H3",{class:!0});var Gu=s(Tt);Ln=o(Gu,"A",{id:!0,class:!0,href:!0});var Y6=s(Ln);Kc=o(Y6,"SPAN",{});var X6=s(Kc);h(ls.$$.fragment,X6),X6.forEach(n),Y6.forEach(n),cT=p(Gu),Jc=o(Gu,"SPAN",{});var K6=s(Jc);dT=i(K6,"SummarizationPipeline"),K6.forEach(n),Gu.forEach(n),ch=p(t),F=o(t,"DIV",{class:!0});var ce=s(F);h(ps.$$.fragment,ce),mT=p(ce),ed=o(ce,"P",{});var J6=s(ed);fT=i(J6,"Summarize news articles and other documents."),J6.forEach(n),hT=p(ce),yt=o(ce,"P",{});var cl=s(yt);uT=i(cl,"This summarizing pipeline can currently be loaded from "),Ci=o(cl,"A",{href:!0});var e5=s(Ci);gT=i(e5,"pipeline()"),e5.forEach(n),_T=i(cl,` using the following task identifier:
`),td=o(cl,"CODE",{});var t5=s(td);vT=i(t5,'"summarization"'),t5.forEach(n),wT=i(cl,"."),cl.forEach(n),bT=p(ce),L=o(ce,"P",{});var X=s(L);kT=i(X,`The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
currently, \u2019`),nd=o(X,"EM",{});var n5=s(nd);PT=i(n5,"bart-large-cnn"),n5.forEach(n),TT=i(X,"\u2019, \u2019"),rd=o(X,"EM",{});var r5=s(rd);yT=i(r5,"t5-small"),r5.forEach(n),xT=i(X,"\u2019, \u2019"),od=o(X,"EM",{});var o5=s(od);$T=i(o5,"t5-base"),o5.forEach(n),ET=i(X,"\u2019, \u2019"),sd=o(X,"EM",{});var s5=s(sd);qT=i(s5,"t5-large"),s5.forEach(n),AT=i(X,"\u2019, \u2019"),ad=o(X,"EM",{});var a5=s(ad);CT=i(a5,"t5-3b"),a5.forEach(n),zT=i(X,"\u2019, \u2019"),id=o(X,"EM",{});var i5=s(id);DT=i(i5,"t5-11b"),i5.forEach(n),IT=i(X,`\u2019. See the up-to-date
list of available models on `),cs=o(X,"A",{href:!0,rel:!0});var l5=s(cs);jT=i(l5,"huggingface.co/models"),l5.forEach(n),ST=i(X,"."),X.forEach(n),MT=p(ce),ld=o(ce,"P",{});var p5=s(ld);FT=i(p5,"Usage:"),p5.forEach(n),LT=p(ce),h(ds.$$.fragment,ce),UT=p(ce),Un=o(ce,"DIV",{class:!0});var Nu=s(Un);h(ms.$$.fragment,Nu),GT=p(Nu),pd=o(Nu,"P",{});var c5=s(pd);NT=i(c5,"Summarize the text(s) given as inputs."),c5.forEach(n),Nu.forEach(n),ce.forEach(n),dh=p(t),xt=o(t,"H3",{class:!0});var Ou=s(xt);Gn=o(Ou,"A",{id:!0,class:!0,href:!0});var d5=s(Gn);cd=o(d5,"SPAN",{});var m5=s(cd);h(fs.$$.fragment,m5),m5.forEach(n),d5.forEach(n),OT=p(Ou),dd=o(Ou,"SPAN",{});var f5=s(dd);RT=i(f5,"TableQuestionAnsweringPipeline"),f5.forEach(n),Ou.forEach(n),mh=p(t),oe=o(t,"DIV",{class:!0});var Re=s(oe);h(hs.$$.fragment,Re),HT=p(Re),us=o(Re,"P",{});var Ru=s(us);WT=i(Ru,"Table Question Answering pipeline using a "),md=o(Ru,"CODE",{});var h5=s(md);QT=i(h5,"ModelForTableQuestionAnswering"),h5.forEach(n),VT=i(Ru,`. This pipeline is only available in
PyTorch.`),Ru.forEach(n),BT=p(Re),$t=o(Re,"P",{});var dl=s($t);ZT=i(dl,"This tabular question answering pipeline can currently be loaded from "),zi=o(dl,"A",{href:!0});var u5=s(zi);YT=i(u5,"pipeline()"),u5.forEach(n),XT=i(dl,` using the following task
identifier: `),fd=o(dl,"CODE",{});var g5=s(fd);KT=i(g5,'"table-question-answering"'),g5.forEach(n),JT=i(dl,"."),dl.forEach(n),ey=p(Re),gs=o(Re,"P",{});var Hu=s(gs);ty=i(Hu,`The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task.
See the up-to-date list of available models on
`),_s=o(Hu,"A",{href:!0,rel:!0});var _5=s(_s);ny=i(_5,"huggingface.co/models"),_5.forEach(n),ry=i(Hu,"."),Hu.forEach(n),oy=p(Re),C=o(Re,"DIV",{class:!0});var G=s(C);h(vs.$$.fragment,G),sy=p(G),hd=o(G,"P",{});var v5=s(hd);ay=i(v5,"Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:"),v5.forEach(n),iy=p(G),W=o(G,"UL",{});var de=s(W);ud=o(de,"LI",{});var w5=s(ud);gd=o(w5,"CODE",{});var b5=s(gd);ly=i(b5,"pipeline(table, query)"),b5.forEach(n),w5.forEach(n),py=p(de),_d=o(de,"LI",{});var k5=s(_d);vd=o(k5,"CODE",{});var P5=s(vd);cy=i(P5,"pipeline(table, [query])"),P5.forEach(n),k5.forEach(n),dy=p(de),wd=o(de,"LI",{});var T5=s(wd);bd=o(T5,"CODE",{});var y5=s(bd);my=i(y5,"pipeline(table=table, query=query)"),y5.forEach(n),T5.forEach(n),fy=p(de),kd=o(de,"LI",{});var x5=s(kd);Pd=o(x5,"CODE",{});var $5=s(Pd);hy=i($5,"pipeline(table=table, query=[query])"),$5.forEach(n),x5.forEach(n),uy=p(de),Td=o(de,"LI",{});var E5=s(Td);yd=o(E5,"CODE",{});var q5=s(yd);gy=i(q5,'pipeline({"table": table, "query": query})'),q5.forEach(n),E5.forEach(n),_y=p(de),xd=o(de,"LI",{});var A5=s(xd);$d=o(A5,"CODE",{});var C5=s($d);vy=i(C5,'pipeline({"table": table, "query": [query]})'),C5.forEach(n),A5.forEach(n),wy=p(de),Ed=o(de,"LI",{});var z5=s(Ed);qd=o(z5,"CODE",{});var D5=s(qd);by=i(D5,'pipeline([{"table": table, "query": query}, {"table": table, "query": query}])'),D5.forEach(n),z5.forEach(n),de.forEach(n),ky=p(G),ws=o(G,"P",{});var Wu=s(ws);Py=i(Wu,"The "),Ad=o(Wu,"CODE",{});var I5=s(Ad);Ty=i(I5,"table"),I5.forEach(n),yy=i(Wu," argument should be a dict or a DataFrame built from that dict, containing the whole table:"),Wu.forEach(n),xy=p(G),Cd=o(G,"P",{});var j5=s(Cd);$y=i(j5,"Example:"),j5.forEach(n),Ey=p(G),h(bs.$$.fragment,G),qy=p(G),zd=o(G,"P",{});var S5=s(zd);Ay=i(S5,"This dictionary can be passed in as such, or can be converted to a pandas DataFrame:"),S5.forEach(n),Cy=p(G),Dd=o(G,"P",{});var M5=s(Dd);zy=i(M5,"Example:"),M5.forEach(n),Dy=p(G),h(ks.$$.fragment,G),G.forEach(n),Re.forEach(n),fh=p(t),Et=o(t,"H3",{class:!0});var Qu=s(Et);Nn=o(Qu,"A",{id:!0,class:!0,href:!0});var F5=s(Nn);Id=o(F5,"SPAN",{});var L5=s(Id);h(Ps.$$.fragment,L5),L5.forEach(n),F5.forEach(n),Iy=p(Qu),jd=o(Qu,"SPAN",{});var U5=s(jd);jy=i(U5,"TextClassificationPipeline"),U5.forEach(n),Qu.forEach(n),hh=p(t),Q=o(t,"DIV",{class:!0});var ke=s(Q);h(Ts.$$.fragment,ke),Sy=p(ke),qt=o(ke,"P",{});var ml=s(qt);My=i(ml,"Text classification pipeline using any "),Sd=o(ml,"CODE",{});var G5=s(Sd);Fy=i(G5,"ModelForSequenceClassification"),G5.forEach(n),Ly=i(ml,". See the "),Di=o(ml,"A",{href:!0});var N5=s(Di);Uy=i(N5,`sequence classification
examples`),N5.forEach(n),Gy=i(ml," for more information."),ml.forEach(n),Ny=p(ke),At=o(ke,"P",{});var fl=s(At);Oy=i(fl,"This text classification pipeline can currently be loaded from "),Ii=o(fl,"A",{href:!0});var O5=s(Ii);Ry=i(O5,"pipeline()"),O5.forEach(n),Hy=i(fl,` using the following task identifier:
`),Md=o(fl,"CODE",{});var R5=s(Md);Wy=i(R5,'"sentiment-analysis"'),R5.forEach(n),Qy=i(fl," (for classifying sequences according to positive or negative sentiments)."),fl.forEach(n),Vy=p(ke),ys=o(ke,"P",{});var Vu=s(ys);By=i(Vu,"If multiple classification labels are available ("),Fd=o(Vu,"CODE",{});var H5=s(Fd);Zy=i(H5,"model.config.num_labels >= 2"),H5.forEach(n),Yy=i(Vu,`), the pipeline will run a softmax
over the results. If there is a single label, the pipeline will run a sigmoid over the result.`),Vu.forEach(n),Xy=p(ke),xs=o(ke,"P",{});var Bu=s(xs);Ky=i(Bu,`The models that this pipeline can use are models that have been fine-tuned on a sequence classification task. See
the up-to-date list of available models on
`),$s=o(Bu,"A",{href:!0,rel:!0});var W5=s($s);Jy=i(W5,"huggingface.co/models"),W5.forEach(n),e2=i(Bu,"."),Bu.forEach(n),t2=p(ke),On=o(ke,"DIV",{class:!0});var Zu=s(On);h(Es.$$.fragment,Zu),n2=p(Zu),Ld=o(Zu,"P",{});var Q5=s(Ld);r2=i(Q5,"Classify the text(s) given as inputs."),Q5.forEach(n),Zu.forEach(n),ke.forEach(n),uh=p(t),Ct=o(t,"H3",{class:!0});var Yu=s(Ct);Rn=o(Yu,"A",{id:!0,class:!0,href:!0});var V5=s(Rn);Ud=o(V5,"SPAN",{});var B5=s(Ud);h(qs.$$.fragment,B5),B5.forEach(n),V5.forEach(n),o2=p(Yu),Gd=o(Yu,"SPAN",{});var Z5=s(Gd);s2=i(Z5,"TextGenerationPipeline"),Z5.forEach(n),Yu.forEach(n),gh=p(t),se=o(t,"DIV",{class:!0});var He=s(se);h(As.$$.fragment,He),a2=p(He),Cs=o(He,"P",{});var Xu=s(Cs);i2=i(Xu,"Language generation pipeline using any "),Nd=o(Xu,"CODE",{});var Y5=s(Nd);l2=i(Y5,"ModelWithLMHead"),Y5.forEach(n),p2=i(Xu,`. This pipeline predicts the words that will follow a
specified text prompt.`),Xu.forEach(n),c2=p(He),zt=o(He,"P",{});var hl=s(zt);d2=i(hl,"This language generation pipeline can currently be loaded from "),ji=o(hl,"A",{href:!0});var X5=s(ji);m2=i(X5,"pipeline()"),X5.forEach(n),f2=i(hl,` using the following task identifier:
`),Od=o(hl,"CODE",{});var K5=s(Od);h2=i(K5,'"text-generation"'),K5.forEach(n),u2=i(hl,"."),hl.forEach(n),g2=p(He),zs=o(He,"P",{});var Ku=s(zs);_2=i(Ku,`The models that this pipeline can use are models that have been trained with an autoregressive language modeling
objective, which includes the uni-directional models in the library (e.g. gpt2). See the list of available models
on `),Ds=o(Ku,"A",{href:!0,rel:!0});var J5=s(Ds);v2=i(J5,"huggingface.co/models"),J5.forEach(n),w2=i(Ku,"."),Ku.forEach(n),b2=p(He),Hn=o(He,"DIV",{class:!0});var Ju=s(Hn);h(Is.$$.fragment,Ju),k2=p(Ju),Rd=o(Ju,"P",{});var eq=s(Rd);P2=i(eq,"Complete the prompt(s) given as inputs."),eq.forEach(n),Ju.forEach(n),He.forEach(n),_h=p(t),Dt=o(t,"H3",{class:!0});var eg=s(Dt);Wn=o(eg,"A",{id:!0,class:!0,href:!0});var tq=s(Wn);Hd=o(tq,"SPAN",{});var nq=s(Hd);h(js.$$.fragment,nq),nq.forEach(n),tq.forEach(n),T2=p(eg),Wd=o(eg,"SPAN",{});var rq=s(Wd);y2=i(rq,"Text2TextGenerationPipeline"),rq.forEach(n),eg.forEach(n),vh=p(t),S=o(t,"DIV",{class:!0});var K=s(S);h(Ss.$$.fragment,K),x2=p(K),Qd=o(K,"P",{});var oq=s(Qd);$2=i(oq,"Pipeline for text to text generation using seq2seq models."),oq.forEach(n),E2=p(K),It=o(K,"P",{});var ul=s(It);q2=i(ul,"This Text2TextGenerationPipeline pipeline can currently be loaded from "),Si=o(ul,"A",{href:!0});var sq=s(Si);A2=i(sq,"pipeline()"),sq.forEach(n),C2=i(ul,` using the following task
identifier: `),Vd=o(ul,"CODE",{});var aq=s(Vd);z2=i(aq,'"text2text-generation"'),aq.forEach(n),D2=i(ul,"."),ul.forEach(n),I2=p(K),Ms=o(K,"P",{});var tg=s(Ms);j2=i(tg,`The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on
`),Fs=o(tg,"A",{href:!0,rel:!0});var iq=s(Fs);S2=i(iq,"huggingface.co/models"),iq.forEach(n),M2=i(tg,"."),tg.forEach(n),F2=p(K),Bd=o(K,"P",{});var lq=s(Bd);L2=i(lq,"Usage:"),lq.forEach(n),U2=p(K),h(Ls.$$.fragment,K),G2=p(K),Qn=o(K,"DIV",{class:!0});var ng=s(Qn);h(Us.$$.fragment,ng),N2=p(ng),Zd=o(ng,"P",{});var pq=s(Zd);O2=i(pq,"Generate the output text(s) using text(s) given as inputs."),pq.forEach(n),ng.forEach(n),R2=p(K),Vn=o(K,"DIV",{class:!0});var rg=s(Vn);h(Gs.$$.fragment,rg),H2=p(rg),Yd=o(rg,"P",{});var cq=s(Yd);W2=i(cq,"Checks whether there might be something wrong with given input with regard to the model."),cq.forEach(n),rg.forEach(n),K.forEach(n),wh=p(t),jt=o(t,"H3",{class:!0});var og=s(jt);Bn=o(og,"A",{id:!0,class:!0,href:!0});var dq=s(Bn);Xd=o(dq,"SPAN",{});var mq=s(Xd);h(Ns.$$.fragment,mq),mq.forEach(n),dq.forEach(n),Q2=p(og),Kd=o(og,"SPAN",{});var fq=s(Kd);V2=i(fq,"TokenClassificationPipeline"),fq.forEach(n),og.forEach(n),bh=p(t),q=o(t,"DIV",{class:!0});var N=s(q);h(Os.$$.fragment,N),B2=p(N),St=o(N,"P",{});var gl=s(St);Z2=i(gl,"Named Entity Recognition pipeline using any "),Jd=o(gl,"CODE",{});var hq=s(Jd);Y2=i(hq,"ModelForTokenClassification"),hq.forEach(n),X2=i(gl,". See the "),Mi=o(gl,"A",{href:!0});var uq=s(Mi);K2=i(uq,`named entity recognition
examples`),uq.forEach(n),J2=i(gl," for more information."),gl.forEach(n),e4=p(N),Mt=o(N,"P",{});var _l=s(Mt);t4=i(_l,"This token recognition pipeline can currently be loaded from "),Fi=o(_l,"A",{href:!0});var gq=s(Fi);n4=i(gq,"pipeline()"),gq.forEach(n),r4=i(_l,` using the following task identifier:
`),em=o(_l,"CODE",{});var _q=s(em);o4=i(_q,'"ner"'),_q.forEach(n),s4=i(_l," (for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous)."),_l.forEach(n),a4=p(N),Rs=o(N,"P",{});var sg=s(Rs);i4=i(sg,`The models that this pipeline can use are models that have been fine-tuned on a token classification task. See the
up-to-date list of available models on
`),Hs=o(sg,"A",{href:!0,rel:!0});var vq=s(Hs);l4=i(vq,"huggingface.co/models"),vq.forEach(n),p4=i(sg,"."),sg.forEach(n),c4=p(N),Zn=o(N,"DIV",{class:!0});var ag=s(Zn);h(Ws.$$.fragment,ag),d4=p(ag),tm=o(ag,"P",{});var wq=s(tm);m4=i(wq,"Classify each token of the text(s) given as inputs."),wq.forEach(n),ag.forEach(n),f4=p(N),je=o(N,"DIV",{class:!0});var vl=s(je);h(Qs.$$.fragment,vl),h4=p(vl),nm=o(vl,"P",{});var bq=s(nm);u4=i(bq,"Override tokens from a given word that disagree to force agreement on word boundaries."),bq.forEach(n),g4=p(vl),rm=o(vl,"P",{});var kq=s(rm);_4=i(kq,`Example: micro|soft| com|pany| B-ENT I-NAME I-ENT I-ENT will be rewritten with first strategy as microsoft|
company| B-ENT I-ENT`),kq.forEach(n),vl.forEach(n),v4=p(N),Yn=o(N,"DIV",{class:!0});var ig=s(Yn);h(Vs.$$.fragment,ig),w4=p(ig),om=o(ig,"P",{});var Pq=s(om);b4=i(Pq,"Fuse various numpy arrays into dicts with all the information needed for aggregation"),Pq.forEach(n),ig.forEach(n),k4=p(N),Xn=o(N,"DIV",{class:!0});var lg=s(Xn);h(Bs.$$.fragment,lg),P4=p(lg),sm=o(lg,"P",{});var Tq=s(sm);T4=i(Tq,"Find and group together the adjacent tokens with the same entity predicted."),Tq.forEach(n),lg.forEach(n),y4=p(N),Kn=o(N,"DIV",{class:!0});var pg=s(Kn);h(Zs.$$.fragment,pg),x4=p(pg),am=o(pg,"P",{});var yq=s(am);$4=i(yq,"Group together the adjacent tokens with the same entity predicted."),yq.forEach(n),pg.forEach(n),N.forEach(n),kh=p(t),Ft=o(t,"H3",{class:!0});var cg=s(Ft);Jn=o(cg,"A",{id:!0,class:!0,href:!0});var xq=s(Jn);im=o(xq,"SPAN",{});var $q=s(im);h(Ys.$$.fragment,$q),$q.forEach(n),xq.forEach(n),E4=p(cg),lm=o(cg,"SPAN",{});var Eq=s(lm);q4=i(Eq,"TranslationPipeline"),Eq.forEach(n),cg.forEach(n),Ph=p(t),U=o(t,"DIV",{class:!0});var me=s(U);h(Xs.$$.fragment,me),A4=p(me),pm=o(me,"P",{});var qq=s(pm);C4=i(qq,"Translates from one language to another."),qq.forEach(n),z4=p(me),Lt=o(me,"P",{});var wl=s(Lt);D4=i(wl,"This translation pipeline can currently be loaded from "),Li=o(wl,"A",{href:!0});var Aq=s(Li);I4=i(Aq,"pipeline()"),Aq.forEach(n),j4=i(wl,` using the following task identifier:
`),cm=o(wl,"CODE",{});var Cq=s(cm);S4=i(Cq,'"translation_xx_to_yy"'),Cq.forEach(n),M4=i(wl,"."),wl.forEach(n),F4=p(me),Ks=o(me,"P",{});var dg=s(Ks);L4=i(dg,`The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on `),Js=o(dg,"A",{href:!0,rel:!0});var zq=s(Js);U4=i(zq,"huggingface.co/models"),zq.forEach(n),G4=i(dg,"."),dg.forEach(n),N4=p(me),dm=o(me,"P",{});var Dq=s(dm);O4=i(Dq,"Usage:"),Dq.forEach(n),R4=p(me),h(ea.$$.fragment,me),H4=p(me),er=o(me,"DIV",{class:!0});var mg=s(er);h(ta.$$.fragment,mg),W4=p(mg),mm=o(mg,"P",{});var Iq=s(mm);Q4=i(Iq,"Translate the text(s) given as inputs."),Iq.forEach(n),mg.forEach(n),me.forEach(n),Th=p(t),Ut=o(t,"H3",{class:!0});var fg=s(Ut);tr=o(fg,"A",{id:!0,class:!0,href:!0});var jq=s(tr);fm=o(jq,"SPAN",{});var Sq=s(fm);h(na.$$.fragment,Sq),Sq.forEach(n),jq.forEach(n),V4=p(fg),hm=o(fg,"SPAN",{});var Mq=s(hm);B4=i(Mq,"ZeroShotClassificationPipeline"),Mq.forEach(n),fg.forEach(n),yh=p(t),V=o(t,"DIV",{class:!0});var Pe=s(V);h(ra.$$.fragment,Pe),Z4=p(Pe),oa=o(Pe,"P",{});var hg=s(oa);Y4=i(hg,"NLI-based zero-shot classification pipeline using a "),um=o(hg,"CODE",{});var Fq=s(um);X4=i(Fq,"ModelForSequenceClassification"),Fq.forEach(n),K4=i(hg,` trained on NLI (natural
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label being valid. Any NLI model can be used, but the id of the `),_m=o(gr,"EM",{});var Uq=s(_m);r0=i(Uq,"entailment"),Uq.forEach(n),o0=i(gr,` label must be included in the model
config\u2019s :attr:`),vm=o(gr,"EM",{});var Gq=s(vm);s0=i(Gq,"~transformers.PretrainedConfig.label2id"),Gq.forEach(n),a0=i(gr,"."),gr.forEach(n),i0=p(Pe),Gt=o(Pe,"P",{});var bl=s(Gt);l0=i(bl,"This NLI pipeline can currently be loaded from "),Ui=o(bl,"A",{href:!0});var Nq=s(Ui);p0=i(Nq,"pipeline()"),Nq.forEach(n),c0=i(bl,` using the following task identifier:
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AA={local:"pipelines",sections:[{local:"transformers.pipeline",title:"The pipeline abstraction"},{local:"pipeline-batching",title:"Pipeline batching"},{local:"pipeline-chunk-batching",title:"Pipeline chunk batching"},{local:"pipeline-custom-code",title:"Pipeline custom code"},{local:"implementing-a-pipeline",title:"Implementing a pipeline"},{local:"the-task-specific-pipelines",sections:[{local:"transformers.AudioClassificationPipeline",title:"AudioClassificationPipeline"},{local:"transformers.AutomaticSpeechRecognitionPipeline",title:"AutomaticSpeechRecognitionPipeline"},{local:"transformers.Conversation",title:"ConversationalPipeline"},{local:"transformers.FeatureExtractionPipeline",title:"FeatureExtractionPipeline"},{local:"transformers.FillMaskPipeline",title:"FillMaskPipeline"},{local:"transformers.ImageClassificationPipeline",title:"ImageClassificationPipeline"},{local:"transformers.ImageSegmentationPipeline",title:"ImageSegmentationPipeline"},{local:"transformers.TokenClassificationPipeline",title:"NerPipeline"},{local:"transformers.ObjectDetectionPipeline",title:"ObjectDetectionPipeline"},{local:"transformers.QuestionAnsweringPipeline",title:"QuestionAnsweringPipeline"},{local:"transformers.SummarizationPipeline",title:"SummarizationPipeline"},{local:"transformers.TableQuestionAnsweringPipeline",title:"TableQuestionAnsweringPipeline"},{local:"transformers.TextClassificationPipeline",title:"TextClassificationPipeline"},{local:"transformers.TextGenerationPipeline",title:"TextGenerationPipeline"},{local:"transformers.Text2TextGenerationPipeline",title:"Text2TextGenerationPipeline"},{local:"transformers.TokenClassificationPipeline",title:"TokenClassificationPipeline"},{local:"transformers.TranslationPipeline",title:"TranslationPipeline"},{local:"transformers.ZeroShotClassificationPipeline",title:"ZeroShotClassificationPipeline"},{local:"transformers.ZeroShotImageClassificationPipeline",title:"ZeroShotImageClassificationPipeline"}],title:"The 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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/audio_classification.mdx-cab2e6ac.js | import{S as Es,i as xs,s as As,e as r,k as f,w as v,t as l,M as Ts,c as o,d as a,m as h,a as i,x as w,h as n,b as m,F as t,g as p,y as j,q as k,o as y,B as E}from"../../chunks/vendor-4833417e.js";import{T as Wt}from"../../chunks/Tip-fffd6df1.js";import{Y as Ps}from"../../chunks/Youtube-27813aed.js";import{I as Ia}from"../../chunks/IconCopyLink-4b81c553.js";import{C as M}from"../../chunks/CodeBlock-6a3d1b46.js";import"../../chunks/CopyButton-dacfbfaf.js";function qs(F){let c,b,u,_,$;return{c(){c=r("p"),b=l("See the audio classification "),u=r("a"),_=l("task page"),$=l(" for more information about its associated models, datasets, and metrics."),this.h()},l(d){c=o(d,"P",{});var g=i(c);b=n(g,"See the audio classification "),u=o(g,"A",{href:!0,rel:!0});var x=i(u);_=n(x,"task page"),x.forEach(a),$=n(g," for more information about its associated models, datasets, and metrics."),g.forEach(a),this.h()},h(){m(u,"href","https://huggingface.co/tasks/audio-classification"),m(u,"rel","nofollow")},m(d,g){p(d,c,g),t(c,b),t(c,u),t(u,_),t(c,$)},d(d){d&&a(c)}}}function Cs(F){let c,b,u,_,$,d,g,x;return{c(){c=r("p"),b=l("If you aren\u2019t familiar with fine-tuning a model with the "),u=r("a"),_=l("Trainer"),$=l(", take a look at the basic tutorial "),d=r("a"),g=l("here"),x=l("!"),this.h()},l(I){c=o(I,"P",{});var A=i(c);b=n(A,"If you aren\u2019t familiar with fine-tuning a model with the "),u=o(A,"A",{href:!0});var q=i(u);_=n(q,"Trainer"),q.forEach(a),$=n(A,", take a look at the basic tutorial "),d=o(A,"A",{href:!0});var J=i(d);g=n(J,"here"),J.forEach(a),x=n(A,"!"),A.forEach(a),this.h()},h(){m(u,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),m(d,"href","training#finetune-with-trainer")},m(I,A){p(I,c,A),t(c,b),t(c,u),t(u,_),t(c,$),t(c,d),t(d,g),t(c,x)},d(I){I&&a(c)}}}function Ss(F){let c,b,u,_,$;return{c(){c=r("p"),b=l("For a more in-depth example of how to fine-tune a model for audio classification, take a look at the corresponding "),u=r("a"),_=l("PyTorch notebook"),$=l("."),this.h()},l(d){c=o(d,"P",{});var g=i(c);b=n(g,"For a more in-depth example of how to fine-tune a model for audio classification, take a look at the corresponding "),u=o(g,"A",{href:!0,rel:!0});var x=i(u);_=n(x,"PyTorch notebook"),x.forEach(a),$=n(g,"."),g.forEach(a),this.h()},h(){m(u,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb"),m(u,"rel","nofollow")},m(d,g){p(d,c,g),t(c,b),t(c,u),t(u,_),t(c,$)},d(d){d&&a(c)}}}function Ls(F){let c,b,u,_,$,d,g,x,I,A,q,J,be,Oa,Xe,C,Na,Q,Ba,Ma,X,Ra,Ua,Ze,R,ea,O,U,De,Z,Wa,Fe,Va,aa,$e,Ya,ta,ee,sa,ve,za,la,ae,na,T,Ha,Ie,Ka,Ga,Oe,Ja,Qa,Ne,Xa,Za,ra,te,oa,we,et,ia,se,pa,S,at,Be,tt,st,Me,lt,nt,ca,N,W,Re,le,rt,Ue,ot,fa,je,it,ha,ne,ua,ke,pt,ma,L,re,ct,We,ft,ht,ut,oe,mt,ye,dt,_t,gt,Ve,bt,da,ie,_a,P,$t,pe,Ye,vt,wt,ze,jt,kt,He,yt,Et,ga,ce,ba,B,V,Ke,fe,xt,Ge,At,$a,Y,Tt,Ee,Pt,qt,va,he,wa,z,ja,xe,Ct,ka,D,ue,St,Ae,Lt,Dt,Ft,me,It,Te,Ot,Nt,Bt,de,Mt,Pe,Rt,Ut,ya,_e,Ea,H,xa;return d=new Ia({}),q=new Ps({props:{id:"KWwzcmG98Ds"}}),R=new Wt({props:{$$slots:{default:[qs]},$$scope:{ctx:F}}}),Z=new Ia({}),ee=new M({props:{code:`from datasets import load_dataset
ks = load_dataset("superb", "ks")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>ks = load_dataset(<span class="hljs-string">"superb"</span>, <span class="hljs-string">"ks"</span>)`}}),ae=new M({props:{code:'ks["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>ks[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'audio'</span>: {<span class="hljs-string">'array'</span>: array([ <span class="hljs-number">0.</span> , <span class="hljs-number">0.</span> , <span class="hljs-number">0.</span> , ..., -<span class="hljs-number">0.00592041</span>, -<span class="hljs-number">0.00405884</span>, -<span class="hljs-number">0.00253296</span>], dtype=float32), <span class="hljs-string">'path'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/05734a36d88019a09725c20cc024e1c4e7982e37d7d55c0c1ca1742ea1cdd47f/_background_noise_/doing_the_dishes.wav'</span>, <span class="hljs-string">'sampling_rate'</span>: <span class="hljs-number">16000</span>}, <span class="hljs-string">'file'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/05734a36d88019a09725c20cc024e1c4e7982e37d7d55c0c1ca1742ea1cdd47f/_background_noise_/doing_the_dishes.wav'</span>, <span class="hljs-string">'label'</span>: <span class="hljs-number">10</span>}`}}),te=new M({props:{code:`labels = ks["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
label2id[label] = str(i)
id2label[str(i)] = label`,highlighted:`<span class="hljs-meta">>>> </span>labels = ks[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">"label"</span>].names
<span class="hljs-meta">>>> </span>label2id, id2label = <span class="hljs-built_in">dict</span>(), <span class="hljs-built_in">dict</span>()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(labels):
<span class="hljs-meta">... </span> label2id[label] = <span class="hljs-built_in">str</span>(i)
<span class="hljs-meta">... </span> id2label[<span class="hljs-built_in">str</span>(i)] = label`}}),se=new M({props:{code:"id2label[str(10)]",highlighted:`<span class="hljs-meta">>>> </span>id2label[<span class="hljs-built_in">str</span>(<span class="hljs-number">10</span>)]
<span class="hljs-string">'_silence_'</span>`}}),le=new Ia({}),ne=new M({props:{code:`from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
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audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True
)
return inputs`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> audio_arrays = [x[<span class="hljs-string">"array"</span>] <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"audio"</span>]]
<span class="hljs-meta">... </span> inputs = feature_extractor(
<span class="hljs-meta">... </span> audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=<span class="hljs-number">16000</span>, truncation=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> inputs`}}),ce=new M({props:{code:'encoded_ks = ks.map(preprocess_function, remove_columns=["audio", "file"], batched=True)',highlighted:'<span class="hljs-meta">>>> </span>encoded_ks = ks.<span class="hljs-built_in">map</span>(preprocess_function, remove_columns=[<span class="hljs-string">"audio"</span>, <span class="hljs-string">"file"</span>], batched=<span class="hljs-literal">True</span>)'}}),fe=new Ia({}),he=new M({props:{code:`from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer
num_labels = len(id2label)
model = AutoModelForAudioClassification.from_pretrained(
"facebook/wav2vec2-base", num_labels=num_labels, label2id=label2id, id2label=id2label
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForAudioClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>num_labels = <span class="hljs-built_in">len</span>(id2label)
<span class="hljs-meta">>>> </span>model = AutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"facebook/wav2vec2-base"</span>, num_labels=num_labels, label2id=label2id, id2label=id2label
<span class="hljs-meta">... </span>)`}}),z=new Wt({props:{$$slots:{default:[Cs]},$$scope:{ctx:F}}}),_e=new M({props:{code:`training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=3e-5,
num_train_epochs=5,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=encoded_ks["train"],
eval_dataset=encoded_ks["validation"],
tokenizer=feature_extractor,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> save_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">3e-5</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">5</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=encoded_ks[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=encoded_ks[<span class="hljs-string">"validation"</span>],
<span class="hljs-meta">... </span> tokenizer=feature_extractor,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),H=new Wt({props:{$$slots:{default:[Ss]},$$scope:{ctx:F}}}),{c(){c=r("meta"),b=f(),u=r("h1"),_=r("a"),$=r("span"),v(d.$$.fragment),g=f(),x=r("span"),I=l("Audio classification"),A=f(),v(q.$$.fragment),J=f(),be=r("p"),Oa=l("Audio classification assigns a label or class to audio data. It is similar to text classification, except an audio input is continuous and must be discretized, whereas text can be split into tokens. 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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/token_classification.mdx-5446e455.js | import{S as Eo,i as yo,s as To,e as o,k as c,w as g,t as a,M as zo,c as r,d as e,m as h,a as i,x as j,h as n,b as f,F as t,g as p,y as w,q as k,o as b,B as $}from"../../chunks/vendor-4833417e.js";import{T as pa}from"../../chunks/Tip-fffd6df1.js";import{Y as xo}from"../../chunks/Youtube-27813aed.js";import{I as Xt}from"../../chunks/IconCopyLink-4b81c553.js";import{C}from"../../chunks/CodeBlock-6a3d1b46.js";import{C as qo}from"../../chunks/CodeBlockFw-27a176a0.js";import"../../chunks/CopyButton-dacfbfaf.js";function Co(P){let m,x,d,_,y;return{c(){m=o("p"),x=a("See the token classification "),d=o("a"),_=a("task page"),y=a(" for more information about other forms of token classification and their associated models, datasets, and metrics."),this.h()},l(u){m=r(u,"P",{});var v=i(m);x=n(v,"See the token classification "),d=r(v,"A",{href:!0,rel:!0});var T=i(d);_=n(T,"task page"),T.forEach(e),y=n(v," for more information about other forms of token classification and their associated models, datasets, and metrics."),v.forEach(e),this.h()},h(){f(d,"href","https://huggingface.co/tasks/token-classification"),f(d,"rel","nofollow")},m(u,v){p(u,m,v),t(m,x),t(m,d),t(d,_),t(m,y)},d(u){u&&e(m)}}}function Ao(P){let m,x,d,_,y,u,v,T;return{c(){m=o("p"),x=a("If you aren\u2019t familiar with fine-tuning a model with the "),d=o("a"),_=a("Trainer"),y=a(", take a look at the basic tutorial "),u=o("a"),v=a("here"),T=a("!"),this.h()},l(z){m=r(z,"P",{});var E=i(m);x=n(E,"If you aren\u2019t familiar with fine-tuning a model with the "),d=r(E,"A",{href:!0});var A=i(d);_=n(A,"Trainer"),A.forEach(e),y=n(E,", take a look at the basic tutorial "),u=r(E,"A",{href:!0});var O=i(u);v=n(O,"here"),O.forEach(e),T=n(E,"!"),E.forEach(e),this.h()},h(){f(d,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),f(u,"href","training#finetune-with-trainer")},m(z,E){p(z,m,E),t(m,x),t(m,d),t(d,_),t(m,y),t(m,u),t(u,v),t(m,T)},d(z){z&&e(m)}}}function Do(P){let m,x,d,_,y;return{c(){m=o("p"),x=a("If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),d=o("a"),_=a("here"),y=a("!"),this.h()},l(u){m=r(u,"P",{});var v=i(m);x=n(v,"If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),d=r(v,"A",{href:!0});var T=i(d);_=n(T,"here"),T.forEach(e),y=n(v,"!"),v.forEach(e),this.h()},h(){f(d,"href","training#finetune-with-keras")},m(u,v){p(u,m,v),t(m,x),t(m,d),t(d,_),t(m,y)},d(u){u&&e(m)}}}function Fo(P){let m,x,d,_,y,u,v,T;return{c(){m=o("p"),x=a(`For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
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or `),u=o("a"),v=a("TensorFlow notebook"),T=a("."),this.h()},l(z){m=r(z,"P",{});var E=i(m);x=n(E,`For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
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or `),u=r(E,"A",{href:!0,rel:!0});var O=i(u);v=n(O,"TensorFlow notebook"),O.forEach(e),T=n(E,"."),E.forEach(e),this.h()},h(){f(d,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb"),f(d,"rel","nofollow"),f(u,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb"),f(u,"rel","nofollow")},m(z,E){p(z,m,E),t(m,x),t(m,d),t(d,_),t(m,y),t(m,u),t(u,v),t(m,T)},d(z){z&&e(m)}}}function Po(P){let m,x,d,_,y,u,v,T,z,E,A,O,Ks,ca,se,L,ha,ms,fa,ma,ds,da,ua,te,Z,ee,H,J,dt,us,_a,ut,ga,ae,Vs,ja,ne,_s,le,Zs,wa,oe,gs,re,G,ka,_t,ba,$a,ie,js,pe,N,va,gt,xa,Ea,jt,ya,Ta,ce,I,Js,wt,za,qa,Ca,B,kt,Aa,Da,bt,Fa,Pa,$t,Sa,Oa,La,Gs,vt,Na,Ia,he,Y,Q,xt,ws,Ba,Et,Ma,fe,ks,me,X,Ra,yt,Ua,Wa,de,bs,ue,ss,Ha,Tt,Ya,Ka,_e,$s,ge,M,Va,zt,Za,Ja,qt,Ga,Qa,je,R,vs,Xa,xs,Ct,sn,tn,en,S,an,At,nn,ln,Dt,on,rn,Ft,pn,cn,hn,Es,fn,Pt,mn,dn,we,Qs,un,ke,ys,be,D,_n,Ts,St,gn,jn,Ot,wn,kn,Lt,bn,$n,$e,zs,ve,q,vn,Xs,xn,En,Nt,yn,Tn,It,zn,qn,Bt,Cn,An,xe,qs,Ee,K,ts,Mt,Cs,Dn,Rt,Fn,ye,es,Pn,st,Sn,On,Te,As,ze,as,qe,tt,Ln,Ce,U,Ds,Nn,et,In,Bn,Mn,Fs,Rn,at,Un,Wn,Hn,Ps,Yn,nt,Kn,Vn,Ae,Ss,De,V,ns,Ut,Os,Zn,Wt,Jn,Fe,lt,Gn,Pe,ls,Se,F,Qn,Ht,Xn,sl,Ls,Yt,tl,el,Kt,al,nl,Oe,Ns,Le,ot,ll,Ne,Is,Ie,os,ol,rt,rl,il,Be,Bs,Me,rs,pl,Ms,Vt,cl,hl,Re,Rs,Ue,is,fl,Us,Zt,ml,dl,We,Ws,He,ps,Ye;return u=new Xt({}),A=new xo({props:{id:"wVHdVlPScxA"}}),Z=new pa({props:{$$slots:{default:[Co]},$$scope:{ctx:P}}}),us=new Xt({}),_s=new C({props:{code:`from datasets import load_dataset
wnut = load_dataset("wnut_17")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>wnut = load_dataset(<span class="hljs-string">"wnut_17"</span>)`}}),gs=new C({props:{code:'wnut["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>wnut[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'id'</span>: <span class="hljs-string">'0'</span>,
<span class="hljs-string">'ner_tags'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'tokens'</span>: [<span class="hljs-string">'@paulwalk'</span>, <span class="hljs-string">'It'</span>, <span class="hljs-string">"'s"</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'I'</span>, <span class="hljs-string">"'m"</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Empire'</span>, <span class="hljs-string">'State'</span>, <span class="hljs-string">'Building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'ESB'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>]
}`}}),js=new C({props:{code:`label_list = wnut["train"].features[f"ner_tags"].feature.names
label_list`,highlighted:`<span class="hljs-meta">>>> </span>label_list = wnut[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">f"ner_tags"</span>].feature.names
<span class="hljs-meta">>>> </span>label_list
[
<span class="hljs-string">"O"</span>,
<span class="hljs-string">"B-corporation"</span>,
<span class="hljs-string">"I-corporation"</span>,
<span class="hljs-string">"B-creative-work"</span>,
<span class="hljs-string">"I-creative-work"</span>,
<span class="hljs-string">"B-group"</span>,
<span class="hljs-string">"I-group"</span>,
<span class="hljs-string">"B-location"</span>,
<span class="hljs-string">"I-location"</span>,
<span class="hljs-string">"B-person"</span>,
<span class="hljs-string">"I-person"</span>,
<span class="hljs-string">"B-product"</span>,
<span class="hljs-string">"I-product"</span>,
]`}}),ws=new Xt({}),ks=new xo({props:{id:"iY2AZYdZAr0"}}),bs=new C({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),$s=new C({props:{code:`tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
tokens`,highlighted:`<span class="hljs-meta">>>> </span>tokenized_input = tokenizer(example[<span class="hljs-string">"tokens"</span>], is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokens = tokenizer.convert_ids_to_tokens(tokenized_input[<span class="hljs-string">"input_ids"</span>])
<span class="hljs-meta">>>> </span>tokens
[<span class="hljs-string">'[CLS]'</span>, <span class="hljs-string">'@'</span>, <span class="hljs-string">'paul'</span>, <span class="hljs-string">'##walk'</span>, <span class="hljs-string">'it'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'s'</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'i'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'m'</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'empire'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'es'</span>, <span class="hljs-string">'##b'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'[SEP]'</span>]`}}),ys=new C({props:{code:`def tokenize_and_align_labels(examples):
tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
labels = []
for i, label in enumerate(examples[f"ner_tags"]):
word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
previous_word_idx = None
label_ids = []
for word_idx in word_ids: # Set the special tokens to -100.
if word_idx is None:
label_ids.append(-100)
elif word_idx != previous_word_idx: # Only label the first token of a given word.
label_ids.append(label[word_idx])
else:
label_ids.append(-100)
previous_word_idx = word_idx
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_and_align_labels</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> tokenized_inputs = tokenizer(examples[<span class="hljs-string">"tokens"</span>], truncation=<span class="hljs-literal">True</span>, is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> labels = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(examples[<span class="hljs-string">f"ner_tags"</span>]):
<span class="hljs-meta">... </span> word_ids = tokenized_inputs.word_ids(batch_index=i) <span class="hljs-comment"># Map tokens to their respective word.</span>
<span class="hljs-meta">... </span> previous_word_idx = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> label_ids = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> word_idx <span class="hljs-keyword">in</span> word_ids: <span class="hljs-comment"># Set the special tokens to -100.</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> word_idx <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">elif</span> word_idx != previous_word_idx: <span class="hljs-comment"># Only label the first token of a given word.</span>
<span class="hljs-meta">... </span> label_ids.append(label[word_idx])
<span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> previous_word_idx = word_idx
<span class="hljs-meta">... </span> labels.append(label_ids)
<span class="hljs-meta">... </span> tokenized_inputs[<span class="hljs-string">"labels"</span>] = labels
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenized_inputs`}}),zs=new C({props:{code:"tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)",highlighted:'<span class="hljs-meta">>>> </span>tokenized_wnut = wnut.<span class="hljs-built_in">map</span>(tokenize_and_align_labels, batched=<span class="hljs-literal">True</span>)'}}),qs=new qo({props:{group1:{id:"pt",code:`from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForTokenClassification
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)`},group2:{id:"tf",code:`from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForTokenClassification
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors=<span class="hljs-string">"tf"</span>)`}}}),Cs=new Xt({}),As=new C({props:{code:`from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=14)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">14</span>)`}}),as=new pa({props:{$$slots:{default:[Ao]},$$scope:{ctx:P}}}),Ss=new C({props:{code:`training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_wnut["train"],
eval_dataset=tokenized_wnut["test"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_wnut[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_wnut[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),Os=new Xt({}),ls=new pa({props:{$$slots:{default:[Do]},$$scope:{ctx:P}}}),Ns=new C({props:{code:`tf_train_set = tokenized_wnut["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_validation_set = tokenized_wnut["validation"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = tokenized_wnut[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_wnut[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),Is=new C({props:{code:`from transformers import create_optimizer
batch_size = 16
num_train_epochs = 3
num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs
optimizer, lr_schedule = create_optimizer(
init_lr=2e-5,
num_train_steps=num_train_steps,
weight_decay_rate=0.01,
num_warmup_steps=0,
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_train_epochs = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span>num_train_steps = (<span class="hljs-built_in">len</span>(tokenized_wnut[<span class="hljs-string">"train"</span>]) // batch_size) * num_train_epochs
<span class="hljs-meta">>>> </span>optimizer, lr_schedule = create_optimizer(
<span class="hljs-meta">... </span> init_lr=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_train_steps=num_train_steps,
<span class="hljs-meta">... </span> weight_decay_rate=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> num_warmup_steps=<span class="hljs-number">0</span>,
<span class="hljs-meta">... </span>)`}}),Bs=new C({props:{code:`from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=2)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)`}}),Rs=new C({props:{code:`import tensorflow as tf
model.compile(optimizer=optimizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)`}}),Ws=new C({props:{code:"model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)'}}),ps=new pa({props:{$$slots:{default:[Fo]},$$scope:{ctx:P}}}),{c(){m=o("meta"),x=c(),d=o("h1"),_=o("a"),y=o("span"),g(u.$$.fragment),v=c(),T=o("span"),z=a("Token classification"),E=c(),g(A.$$.fragment),O=c(),Ks=o("p"),ca=a("Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization."),se=c(),L=o("p"),ha=a("This guide will show you how to fine-tune "),ms=o("a"),fa=a("DistilBERT"),ma=a(" on the "),ds=o("a"),da=a("WNUT 17"),ua=a(" dataset to detect new entities."),te=c(),g(Z.$$.fragment),ee=c(),H=o("h2"),J=o("a"),dt=o("span"),g(us.$$.fragment),_a=c(),ut=o("span"),ga=a("Load WNUT 17 dataset"),ae=c(),Vs=o("p"),ja=a("Load the WNUT 17 dataset from the \u{1F917} Datasets library:"),ne=c(),g(_s.$$.fragment),le=c(),Zs=o("p"),wa=a("Then take a look at an example:"),oe=c(),g(gs.$$.fragment),re=c(),G=o("p"),ka=a("Each number in "),_t=o("code"),ba=a("ner_tags"),$a=a(" represents an entity. Convert the number to a label name for more information:"),ie=c(),g(js.$$.fragment),pe=c(),N=o("p"),va=a("The "),gt=o("code"),xa=a("ner_tag"),Ea=a(" describes an entity, such as a corporation, location, or person. The letter that prefixes each "),jt=o("code"),ya=a("ner_tag"),Ta=a(" indicates the token position of the entity:"),ce=c(),I=o("ul"),Js=o("li"),wt=o("code"),za=a("B-"),qa=a(" indicates the beginning of an entity."),Ca=c(),B=o("li"),kt=o("code"),Aa=a("I-"),Da=a(" indicates a token is contained inside the same entity (e.g., the "),bt=o("code"),Fa=a("State"),Pa=a(` token is a part of an entity like
`),$t=o("code"),Sa=a("Empire State Building"),Oa=a(")."),La=c(),Gs=o("li"),vt=o("code"),Na=a("0"),Ia=a(" indicates the token doesn\u2019t correspond to any entity."),he=c(),Y=o("h2"),Q=o("a"),xt=o("span"),g(ws.$$.fragment),Ba=c(),Et=o("span"),Ma=a("Preprocess"),fe=c(),g(ks.$$.fragment),me=c(),X=o("p"),Ra=a("Load the DistilBERT tokenizer to process the "),yt=o("code"),Ua=a("tokens"),Wa=a(":"),de=c(),g(bs.$$.fragment),ue=c(),ss=o("p"),Ha=a("Since the input has already been split into words, set "),Tt=o("code"),Ya=a("is_split_into_words=True"),Ka=a(" to tokenize the words into subwords:"),_e=c(),g($s.$$.fragment),ge=c(),M=o("p"),Va=a("Adding the special tokens "),zt=o("code"),Za=a("[CLS]"),Ja=a(" and "),qt=o("code"),Ga=a("[SEP]"),Qa=a(" and subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may be split into two subwords. You will need to realign the tokens and labels by:"),je=c(),R=o("ol"),vs=o("li"),Xa=a("Mapping all tokens to their corresponding word with the "),xs=o("a"),Ct=o("code"),sn=a("word_ids"),tn=a(" method."),en=c(),S=o("li"),an=a("Assigning the label "),At=o("code"),nn=a("-100"),ln=a(" to the special tokens "),Dt=o("code"),on=a("[CLS]"),rn=a(" and "),Ft=o("code"),pn=a("[SEP]"),cn=a(` so the PyTorch loss function ignores
them.`),hn=c(),Es=o("li"),fn=a("Only labeling the first token of a given word. Assign "),Pt=o("code"),mn=a("-100"),dn=a(" to other subtokens from the same word."),we=c(),Qs=o("p"),un=a("Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT\u2019s maximum input length::"),ke=c(),g(ys.$$.fragment),be=c(),D=o("p"),_n=a("Use \u{1F917} Datasets "),Ts=o("a"),St=o("code"),gn=a("map"),jn=a(" function to tokenize and align the labels over the entire dataset. You can speed up the "),Ot=o("code"),wn=a("map"),kn=a(" function by setting "),Lt=o("code"),bn=a("batched=True"),$n=a(" to process multiple elements of the dataset at once:"),$e=c(),g(zs.$$.fragment),ve=c(),q=o("p"),vn=a("Use "),Xs=o("a"),xn=a("DataCollatorForTokenClassification"),En=a(" to create a batch of examples. 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Specify inputs and labels in "),Kt=o("code"),al=a("columns"),nl=a(", whether to shuffle the dataset order, batch size, and the data collator:"),Oe=c(),g(Ns.$$.fragment),Le=c(),ot=o("p"),ll=a("Set up an optimizer function, learning rate schedule, and some training hyperparameters:"),Ne=c(),g(Is.$$.fragment),Ie=c(),os=o("p"),ol=a("Load DistilBERT with "),rt=o("a"),rl=a("TFAutoModelForTokenClassification"),il=a(" along with the number of expected labels:"),Be=c(),g(Bs.$$.fragment),Me=c(),rs=o("p"),pl=a("Configure the model for training with "),Ms=o("a"),Vt=o("code"),cl=a("compile"),hl=a(":"),Re=c(),g(Rs.$$.fragment),Ue=c(),is=o("p"),fl=a("Call "),Us=o("a"),Zt=o("code"),ml=a("fit"),dl=a(" to fine-tune the model:"),We=c(),g(Ws.$$.fragment),He=c(),g(ps.$$.fragment),this.h()},l(s){const l=zo('[data-svelte="svelte-1phssyn"]',document.head);m=r(l,"META",{name:!0,content:!0}),l.forEach(e),x=h(s),d=r(s,"H1",{class:!0});var Hs=i(d);_=r(Hs,"A",{id:!0,class:!0,href:!0});var Jt=i(_);y=r(Jt,"SPAN",{});var Gt=i(y);j(u.$$.fragment,Gt),Gt.forEach(e),Jt.forEach(e),v=h(Hs),T=r(Hs,"SPAN",{});var Qt=i(T);z=n(Qt,"Token classification"),Qt.forEach(e),Hs.forEach(e),E=h(s),j(A.$$.fragment,s),O=h(s),Ks=r(s,"P",{});var gl=i(Ks);ca=n(gl,"Token classification assigns a label to individual tokens in a sentence. 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them.`),cs.forEach(e),hn=h(ft),Es=r(ft,"LI",{});var Xe=i(Es);fn=n(Xe,"Only labeling the first token of a given word. Assign "),Pt=r(Xe,"CODE",{});var Hl=i(Pt);mn=n(Hl,"-100"),Hl.forEach(e),dn=n(Xe," to other subtokens from the same word."),Xe.forEach(e),ft.forEach(e),we=h(s),Qs=r(s,"P",{});var Yl=i(Qs);un=n(Yl,"Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT\u2019s maximum input length::"),Yl.forEach(e),ke=h(s),j(ys.$$.fragment,s),be=h(s),D=r(s,"P",{});var hs=i(D);_n=n(hs,"Use \u{1F917} Datasets "),Ts=r(hs,"A",{href:!0,rel:!0});var Kl=i(Ts);St=r(Kl,"CODE",{});var Vl=i(St);gn=n(Vl,"map"),Vl.forEach(e),Kl.forEach(e),jn=n(hs," function to tokenize and align the labels over the entire dataset. 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| 409 |
0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/multiple_choice.mdx-7c0ab1b6.js | import{S as ql,i as xl,s as Tl,e as o,k as c,w as u,t,M as Cl,c as r,d as a,m as h,a as p,x as _,h as n,b as f,F as e,g as i,y as g,q as j,o as w,B as b}from"../../chunks/vendor-4833417e.js";import{T as zl}from"../../chunks/Tip-fffd6df1.js";import{I as Te}from"../../chunks/IconCopyLink-4b81c553.js";import{C as A}from"../../chunks/CodeBlock-6a3d1b46.js";import{C as Al}from"../../chunks/CodeBlockFw-27a176a0.js";import"../../chunks/CopyButton-dacfbfaf.js";function Pl(ts){let d,$,m,k,x;return{c(){d=o("p"),$=t("If you aren\u2019t familiar with fine-tuning a model with Trainer, take a look at the basic tutorial "),m=o("a"),k=t("here"),x=t("!"),this.h()},l(v){d=r(v,"P",{});var y=p(d);$=n(y,"If you aren\u2019t familiar with fine-tuning a model with Trainer, take a look at the basic tutorial "),m=r(y,"A",{href:!0});var P=p(m);k=n(P,"here"),P.forEach(a),x=n(y,"!"),y.forEach(a),this.h()},h(){f(m,"href","training#finetune-with-trainer")},m(v,y){i(v,d,y),e(d,$),e(d,m),e(m,k),e(d,x)},d(v){v&&a(d)}}}function Dl(ts){let d,$,m,k,x;return{c(){d=o("p"),$=t("If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),m=o("a"),k=t("here"),x=t("!"),this.h()},l(v){d=r(v,"P",{});var y=p(d);$=n(y,"If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),m=r(y,"A",{href:!0});var P=p(m);k=n(P,"here"),P.forEach(a),x=n(y,"!"),y.forEach(a),this.h()},h(){f(m,"href","training#finetune-with-keras")},m(v,y){i(v,d,y),e(d,$),e(d,m),e(m,k),e(d,x)},d(v){v&&a(d)}}}function Ml(ts){let d,$,m,k,x,v,y,P,xa,Ce,Ds,Ta,Ae,T,Ca,ns,Aa,Pa,Qs,Da,Ma,ls,Fa,Oa,Pe,B,G,Vs,os,Sa,Xs,La,De,Ms,Ba,Me,rs,Fe,Fs,Na,Oe,ps,Se,E,Ia,Zs,Wa,Ua,se,Ga,Ra,ee,Ha,Ya,ae,Ka,Ja,Le,N,R,te,is,Qa,ne,Va,Be,Os,Xa,Ne,cs,Ie,Ss,Za,We,M,I,st,le,et,at,oe,tt,nt,lt,hs,ot,re,rt,pt,it,D,ct,pe,ht,ft,ie,dt,mt,ce,ut,_t,Ue,fs,Ge,C,gt,ds,he,jt,wt,fe,bt,kt,de,vt,yt,Re,ms,He,z,$t,Ls,Et,zt,me,qt,xt,ue,Tt,Ct,_e,At,Pt,Ye,us,ge,Dt,Mt,Ke,_s,Je,W,H,je,gs,Ft,we,Ot,Qe,Y,St,Bs,Lt,Bt,Ve,js,Xe,K,Ze,Ns,Nt,sa,F,ws,It,Is,Wt,Ut,Gt,bs,Rt,Ws,Ht,Yt,Kt,ks,Jt,Us,Qt,Vt,ea,vs,aa,U,J,be,ys,Xt,ke,Zt,ta,Gs,sn,na,Q,la,q,en,ve,an,tn,$s,ye,nn,ln,$e,on,rn,Ee,pn,cn,oa,Es,ra,Rs,hn,pa,zs,ia,V,fn,Hs,dn,mn,ca,qs,ha,X,un,xs,ze,_n,gn,fa,Ts,da,Z,jn,Cs,qe,wn,bn,ma,As,ua;return v=new Te({}),os=new Te({}),rs=new A({props:{code:`from datasets import load_dataset
swag = load_dataset("swag", "regular")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>swag = load_dataset(<span class="hljs-string">"swag"</span>, <span class="hljs-string">"regular"</span>)`}}),ps=new A({props:{code:'swag["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>swag[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'ending0'</span>: <span class="hljs-string">'passes by walking down the street playing their instruments.'</span>,
<span class="hljs-string">'ending1'</span>: <span class="hljs-string">'has heard approaching them.'</span>,
<span class="hljs-string">'ending2'</span>: <span class="hljs-string">"arrives and they're outside dancing and asleep."</span>,
<span class="hljs-string">'ending3'</span>: <span class="hljs-string">'turns the lead singer watches the performance.'</span>,
<span class="hljs-string">'fold-ind'</span>: <span class="hljs-string">'3416'</span>,
<span class="hljs-string">'gold-source'</span>: <span class="hljs-string">'gold'</span>,
<span class="hljs-string">'label'</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">'sent1'</span>: <span class="hljs-string">'Members of the procession walk down the street holding small horn brass instruments.'</span>,
<span class="hljs-string">'sent2'</span>: <span class="hljs-string">'A drum line'</span>,
<span class="hljs-string">'startphrase'</span>: <span class="hljs-string">'Members of the procession walk down the street holding small horn brass instruments. A drum line'</span>,
<span class="hljs-string">'video-id'</span>: <span class="hljs-string">'anetv_jkn6uvmqwh4'</span>}`}}),is=new Te({}),cs=new A({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)`}}),fs=new A({props:{code:`ending_names = ["ending0", "ending1", "ending2", "ending3"]
def preprocess_function(examples):
first_sentences = [[context] * 4 for context in examples["sent1"]]
question_headers = examples["sent2"]
second_sentences = [
[f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
]
first_sentences = sum(first_sentences, [])
second_sentences = sum(second_sentences, [])
tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)
return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}`,highlighted:`<span class="hljs-meta">>>> </span>ending_names = [<span class="hljs-string">"ending0"</span>, <span class="hljs-string">"ending1"</span>, <span class="hljs-string">"ending2"</span>, <span class="hljs-string">"ending3"</span>]
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> first_sentences = [[context] * <span class="hljs-number">4</span> <span class="hljs-keyword">for</span> context <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"sent1"</span>]]
<span class="hljs-meta">... </span> question_headers = examples[<span class="hljs-string">"sent2"</span>]
<span class="hljs-meta">... </span> second_sentences = [
<span class="hljs-meta">... </span> [<span class="hljs-string">f"<span class="hljs-subst">{header}</span> <span class="hljs-subst">{examples[end][i]}</span>"</span> <span class="hljs-keyword">for</span> end <span class="hljs-keyword">in</span> ending_names] <span class="hljs-keyword">for</span> i, header <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(question_headers)
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span> first_sentences = <span class="hljs-built_in">sum</span>(first_sentences, [])
<span class="hljs-meta">... </span> second_sentences = <span class="hljs-built_in">sum</span>(second_sentences, [])
<span class="hljs-meta">... </span> tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> {k: [v[i : i + <span class="hljs-number">4</span>] <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(v), <span class="hljs-number">4</span>)] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> tokenized_examples.items()}`}}),ms=new A({props:{code:"tokenized_swag = swag.map(preprocess_function, batched=True)",highlighted:'tokenized_swag = swag.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)'}}),_s=new Al({props:{group1:{id:"pt",code:`from dataclasses import dataclass
from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
from typing import Optional, Union
import torch
@dataclass
class DataCollatorForMultipleChoice:
"""
Data collator that will dynamically pad the inputs for multiple choice received.
"""
tokenizer: PreTrainedTokenizerBase
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
def __call__(self, features):
label_name = "label" if "label" in features[0].keys() else "labels"
labels = [feature.pop(label_name) for feature in features]
batch_size = len(features)
num_choices = len(features[0]["input_ids"])
flattened_features = [
[{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
]
flattened_features = sum(flattened_features, [])
batch = self.tokenizer.pad(
flattened_features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="pt",
)
batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
batch["labels"] = torch.tensor(labels, dtype=torch.int64)
return batch`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.tokenization_utils_base <span class="hljs-keyword">import</span> PreTrainedTokenizerBase, PaddingStrategy
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">Optional</span>, <span class="hljs-type">Union</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">DataCollatorForMultipleChoice</span>:
<span class="hljs-meta">... </span> <span class="hljs-string">"""
<span class="hljs-meta">... </span> Data collator that will dynamically pad the inputs for multiple choice received.
<span class="hljs-meta">... </span> """</span>
<span class="hljs-meta">... </span> tokenizer: PreTrainedTokenizerBase
<span class="hljs-meta">... </span> padding: <span class="hljs-type">Union</span>[<span class="hljs-built_in">bool</span>, <span class="hljs-built_in">str</span>, PaddingStrategy] = <span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> max_length: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">int</span>] = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> pad_to_multiple_of: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">int</span>] = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, features</span>):
<span class="hljs-meta">... </span> label_name = <span class="hljs-string">"label"</span> <span class="hljs-keyword">if</span> <span class="hljs-string">"label"</span> <span class="hljs-keyword">in</span> features[<span class="hljs-number">0</span>].keys() <span class="hljs-keyword">else</span> <span class="hljs-string">"labels"</span>
<span class="hljs-meta">... </span> labels = [feature.pop(label_name) <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span> batch_size = <span class="hljs-built_in">len</span>(features)
<span class="hljs-meta">... </span> num_choices = <span class="hljs-built_in">len</span>(features[<span class="hljs-number">0</span>][<span class="hljs-string">"input_ids"</span>])
<span class="hljs-meta">... </span> flattened_features = [
<span class="hljs-meta">... </span> [{k: v[i] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> feature.items()} <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_choices)] <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span> flattened_features = <span class="hljs-built_in">sum</span>(flattened_features, [])
<span class="hljs-meta">... </span> batch = self.tokenizer.pad(
<span class="hljs-meta">... </span> flattened_features,
<span class="hljs-meta">... </span> padding=self.padding,
<span class="hljs-meta">... </span> max_length=self.max_length,
<span class="hljs-meta">... </span> pad_to_multiple_of=self.pad_to_multiple_of,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> batch = {k: v.view(batch_size, num_choices, -<span class="hljs-number">1</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"labels"</span>] = torch.tensor(labels, dtype=torch.int64)
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> batch`},group2:{id:"tf",code:`from dataclasses import dataclass
from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
from typing import Optional, Union
import tensorflow as tf
@dataclass
class DataCollatorForMultipleChoice:
"""
Data collator that will dynamically pad the inputs for multiple choice received.
"""
tokenizer: PreTrainedTokenizerBase
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
def __call__(self, features):
label_name = "label" if "label" in features[0].keys() else "labels"
labels = [feature.pop(label_name) for feature in features]
batch_size = len(features)
num_choices = len(features[0]["input_ids"])
flattened_features = [
[{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
]
flattened_features = sum(flattened_features, [])
batch = self.tokenizer.pad(
flattened_features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors="tf",
)
batch = {k: tf.reshape(v, (batch_size, num_choices, -1)) for k, v in batch.items()}
batch["labels"] = tf.convert_to_tensor(labels, dtype=tf.int64)
return batch`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.tokenization_utils_base <span class="hljs-keyword">import</span> PreTrainedTokenizerBase, PaddingStrategy
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">Optional</span>, <span class="hljs-type">Union</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">DataCollatorForMultipleChoice</span>:
<span class="hljs-meta">... </span> <span class="hljs-string">"""
<span class="hljs-meta">... </span> Data collator that will dynamically pad the inputs for multiple choice received.
<span class="hljs-meta">... </span> """</span>
<span class="hljs-meta">... </span> tokenizer: PreTrainedTokenizerBase
<span class="hljs-meta">... </span> padding: <span class="hljs-type">Union</span>[<span class="hljs-built_in">bool</span>, <span class="hljs-built_in">str</span>, PaddingStrategy] = <span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> max_length: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">int</span>] = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> pad_to_multiple_of: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">int</span>] = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, features</span>):
<span class="hljs-meta">... </span> label_name = <span class="hljs-string">"label"</span> <span class="hljs-keyword">if</span> <span class="hljs-string">"label"</span> <span class="hljs-keyword">in</span> features[<span class="hljs-number">0</span>].keys() <span class="hljs-keyword">else</span> <span class="hljs-string">"labels"</span>
<span class="hljs-meta">... </span> labels = [feature.pop(label_name) <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span> batch_size = <span class="hljs-built_in">len</span>(features)
<span class="hljs-meta">... </span> num_choices = <span class="hljs-built_in">len</span>(features[<span class="hljs-number">0</span>][<span class="hljs-string">"input_ids"</span>])
<span class="hljs-meta">... </span> flattened_features = [
<span class="hljs-meta">... </span> [{k: v[i] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> feature.items()} <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_choices)] <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span> flattened_features = <span class="hljs-built_in">sum</span>(flattened_features, [])
<span class="hljs-meta">... </span> batch = self.tokenizer.pad(
<span class="hljs-meta">... </span> flattened_features,
<span class="hljs-meta">... </span> padding=self.padding,
<span class="hljs-meta">... </span> max_length=self.max_length,
<span class="hljs-meta">... </span> pad_to_multiple_of=self.pad_to_multiple_of,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"tf"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> batch = {k: tf.reshape(v, (batch_size, num_choices, -<span class="hljs-number">1</span>)) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"labels"</span>] = tf.convert_to_tensor(labels, dtype=tf.int64)
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> batch`}}}),gs=new Te({}),js=new A({props:{code:`from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
model = AutoModelForMultipleChoice.from_pretrained("bert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMultipleChoice, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)`}}),K=new zl({props:{$$slots:{default:[Pl]},$$scope:{ctx:ts}}}),vs=new A({props:{code:`training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_swag["train"],
eval_dataset=tokenized_swag["validation"],
tokenizer=tokenizer,
data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">5e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_swag[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_swag[<span class="hljs-string">"validation"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),ys=new Te({}),Q=new zl({props:{$$slots:{default:[Dl]},$$scope:{ctx:ts}}}),Es=new A({props:{code:`data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
tf_train_set = tokenized_swag["train"].to_tf_dataset(
columns=["attention_mask", "input_ids"],
label_cols=["labels"],
shuffle=True,
batch_size=batch_size,
collate_fn=data_collator,
)
tf_validation_set = tokenized_swag["validation"].to_tf_dataset(
columns=["attention_mask", "input_ids"],
label_cols=["labels"],
shuffle=False,
batch_size=batch_size,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
<span class="hljs-meta">>>> </span>tf_train_set = tokenized_swag[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=batch_size,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_swag[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=batch_size,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),zs=new A({props:{code:`from transformers import create_optimizer
batch_size = 16
num_train_epochs = 2
total_train_steps = (len(tokenized_swag["train"]) // batch_size) * num_train_epochs
optimizer, schedule = create_optimizer(init_lr=5e-5, num_warmup_steps=0, num_train_steps=total_train_steps)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_train_epochs = <span class="hljs-number">2</span>
<span class="hljs-meta">>>> </span>total_train_steps = (<span class="hljs-built_in">len</span>(tokenized_swag[<span class="hljs-string">"train"</span>]) // batch_size) * num_train_epochs
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(init_lr=<span class="hljs-number">5e-5</span>, num_warmup_steps=<span class="hljs-number">0</span>, num_train_steps=total_train_steps)`}}),qs=new A({props:{code:`from transformers import TFAutoModelForMultipleChoice
model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMultipleChoice
<span class="hljs-meta">>>> </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)`}}),Ts=new A({props:{code:`model.compile(
optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
)`,highlighted:`<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(
<span class="hljs-meta">... </span> optimizer=optimizer,
<span class="hljs-meta">... </span> loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=<span class="hljs-literal">True</span>),
<span class="hljs-meta">... </span>)`}}),As=new A({props:{code:"model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">2</span>)'}}),{c(){d=o("meta"),$=c(),m=o("h1"),k=o("a"),x=o("span"),u(v.$$.fragment),y=c(),P=o("span"),xa=t("Multiple choice"),Ce=c(),Ds=o("p"),Ta=t("A multiple choice task is similar to question answering, except several candidate answers are provided along with a context. The model is trained to select the correct answer from multiple inputs given a context."),Ae=c(),T=o("p"),Ca=t("This guide will show you how to fine-tune "),ns=o("a"),Aa=t("BERT"),Pa=t(" on the "),Qs=o("code"),Da=t("regular"),Ma=t(" configuration of the "),ls=o("a"),Fa=t("SWAG"),Oa=t(" dataset to select the best answer given multiple options and some context."),Pe=c(),B=o("h2"),G=o("a"),Vs=o("span"),u(os.$$.fragment),Sa=c(),Xs=o("span"),La=t("Load SWAG dataset"),De=c(),Ms=o("p"),Ba=t("Load the SWAG dataset from the \u{1F917} Datasets library:"),Me=c(),u(rs.$$.fragment),Fe=c(),Fs=o("p"),Na=t("Then take a look at an example:"),Oe=c(),u(ps.$$.fragment),Se=c(),E=o("p"),Ia=t("The "),Zs=o("code"),Wa=t("sent1"),Ua=t(" and "),se=o("code"),Ga=t("sent2"),Ra=t(" fields show how a sentence begins, and each "),ee=o("code"),Ha=t("ending"),Ya=t(" field shows how a sentence could end. 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You can speed up the "),fe=o("code"),bt=t("map"),kt=t(" function by setting "),de=o("code"),vt=t("batched=True"),yt=t(" to process multiple elements of the dataset at once:"),Re=c(),u(ms.$$.fragment),He=c(),z=o("p"),$t=t("\u{1F917} Transformers doesn\u2019t have a data collator for multiple choice, so you will need to create one. You can adapt the "),Ls=o("a"),Et=t("DataCollatorWithPadding"),zt=t(" to create a batch of examples for multiple choice. It will also "),me=o("em"),qt=t("dynamically pad"),xt=t(" your text and labels to the length of the longest element in its batch, so they are a uniform length. 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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/image_classification.mdx-e10d3b71.js | import{S as zs,i as Ss,s as Ls,e as o,k as m,w as $,t as l,M as Ns,c as n,d as a,m as f,a as i,x as v,h as r,b as c,F as t,g as p,y as b,q as w,o as j,B as y}from"../../chunks/vendor-4833417e.js";import{T as Xt}from"../../chunks/Tip-fffd6df1.js";import{Y as Os}from"../../chunks/Youtube-27813aed.js";import{I as Va}from"../../chunks/IconCopyLink-4b81c553.js";import{C as P}from"../../chunks/CodeBlock-6a3d1b46.js";import"../../chunks/CopyButton-dacfbfaf.js";function Rs(z){let h,k,d,g,E;return{c(){h=o("p"),k=l("See the image classification "),d=o("a"),g=l("task page"),E=l(" for more information about its associated models, datasets, and metrics."),this.h()},l(u){h=n(u,"P",{});var _=i(h);k=r(_,"See the image classification "),d=n(_,"A",{href:!0,rel:!0});var x=i(d);g=r(x,"task page"),x.forEach(a),E=r(_," for more information about its associated models, datasets, and metrics."),_.forEach(a),this.h()},h(){c(d,"href","https://huggingface.co/tasks/audio-classification"),c(d,"rel","nofollow")},m(u,_){p(u,h,_),t(h,k),t(h,d),t(d,g),t(h,E)},d(u){u&&a(h)}}}function Ms(z){let h,k,d,g,E,u,_,x;return{c(){h=o("p"),k=l("If you aren\u2019t familiar with fine-tuning a model with the "),d=o("a"),g=l("Trainer"),E=l(", take a look at the basic tutorial "),u=o("a"),_=l("here"),x=l("!"),this.h()},l(S){h=n(S,"P",{});var A=i(h);k=r(A,"If you aren\u2019t familiar with fine-tuning a model with the "),d=n(A,"A",{href:!0});var D=i(d);g=r(D,"Trainer"),D.forEach(a),E=r(A,", take a look at the basic tutorial "),u=n(A,"A",{href:!0});var X=i(u);_=r(X,"here"),X.forEach(a),x=r(A,"!"),A.forEach(a),this.h()},h(){c(d,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),c(u,"href","training#finetune-with-trainer")},m(S,A){p(S,h,A),t(h,k),t(h,d),t(d,g),t(h,E),t(h,u),t(u,_),t(h,x)},d(S){S&&a(h)}}}function Us(z){let h,k,d,g,E;return{c(){h=o("p"),k=l("For a more in-depth example of how to fine-tune a model for image classification, take a look at the corresponding "),d=o("a"),g=l("PyTorch notebook"),E=l("."),this.h()},l(u){h=n(u,"P",{});var _=i(h);k=r(_,"For a more in-depth example of how to fine-tune a model for image classification, take a look at the corresponding "),d=n(_,"A",{href:!0,rel:!0});var x=i(d);g=r(x,"PyTorch notebook"),x.forEach(a),E=r(_,"."),_.forEach(a),this.h()},h(){c(d,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/image_classification.ipynb"),c(d,"rel","nofollow")},m(u,_){p(u,h,_),t(h,k),t(h,d),t(d,g),t(h,E)},d(u){u&&a(h)}}}function Bs(z){let h,k,d,g,E,u,_,x,S,A,D,X,we,Ga,ea,q,Ja,Z,Ya,Wa,ee,Ka,Qa,aa,R,ta,L,M,Se,ae,Xa,Le,Za,sa,je,et,la,te,ra,ye,at,oa,se,na,ke,tt,ia,le,pa,F,st,Ne,lt,rt,Oe,ot,nt,ma,re,fa,Ee,it,ha,oe,ca,U,pt,Re,mt,ft,da,N,B,Me,ne,ht,Ue,ct,ua,xe,dt,ga,ie,_a,H,ut,pe,Be,gt,_t,$a,me,va,V,$t,He,vt,bt,ba,fe,wa,G,wt,he,Ve,jt,yt,ja,ce,ya,J,kt,Te,Et,xt,ka,de,Ea,O,Y,Ge,ue,Tt,Je,At,xa,W,Ct,Ae,Pt,Dt,Ta,ge,Aa,K,Ca,Ce,qt,Pa,I,T,Ft,Pe,It,zt,Ye,St,Lt,We,Nt,Ot,Ke,Rt,Mt,Qe,Ut,Bt,Ht,_e,Vt,De,Gt,Jt,Yt,$e,Wt,qe,Kt,Qt,Da,ve,qa,Q,Fa;return u=new Va({}),D=new Os({props:{id:"tjAIM7BOYhw"}}),R=new Xt({props:{$$slots:{default:[Rs]},$$scope:{ctx:z}}}),ae=new Va({}),te=new P({props:{code:`from datasets import load_dataset
food = load_dataset("food101", split="train[:5000]")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>food = load_dataset(<span class="hljs-string">"food101"</span>, split=<span class="hljs-string">"train[:5000]"</span>)`}}),se=new P({props:{code:"food = food.train_test_split(test_size=0.2)",highlighted:'<span class="hljs-meta">>>> </span>food = food.train_test_split(test_size=<span class="hljs-number">0.2</span>)'}}),le=new P({props:{code:'food["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>food[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'image'</span>: <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at <span class="hljs-number">0x7F52AFC8AC50</span>>,
<span class="hljs-string">'label'</span>: <span class="hljs-number">79</span>}`}}),re=new P({props:{code:`labels = food["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
label2id[label] = str(i)
id2label[str(i)] = label`,highlighted:`<span class="hljs-meta">>>> </span>labels = food[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">"label"</span>].names
<span class="hljs-meta">>>> </span>label2id, id2label = <span class="hljs-built_in">dict</span>(), <span class="hljs-built_in">dict</span>()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(labels):
<span class="hljs-meta">... </span> label2id[label] = <span class="hljs-built_in">str</span>(i)
<span class="hljs-meta">... </span> id2label[<span class="hljs-built_in">str</span>(i)] = label`}}),oe=new P({props:{code:"id2label[str(79)]",highlighted:`<span class="hljs-meta">>>> </span>id2label[<span class="hljs-built_in">str</span>(<span class="hljs-number">79</span>)]
<span class="hljs-string">'prime_rib'</span>`}}),ne=new Va({}),ie=new P({props:{code:`from transformers import AutoFeatureExtractor
feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">>>> </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">"google/vit-base-patch16-224-in21k"</span>)`}}),me=new P({props:{code:`from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
_transforms = Compose([RandomResizedCrop(feature_extractor.size), ToTensor(), normalize])`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torchvision.transforms <span class="hljs-keyword">import</span> RandomResizedCrop, Compose, Normalize, ToTensor
<span class="hljs-meta">>>> </span>normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
<span class="hljs-meta">>>> </span>_transforms = Compose([RandomResizedCrop(feature_extractor.size), ToTensor(), normalize])`}}),fe=new P({props:{code:`def transforms(examples):
examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
del examples["image"]
return examples`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">transforms</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> examples[<span class="hljs-string">"pixel_values"</span>] = [_transforms(img.convert(<span class="hljs-string">"RGB"</span>)) <span class="hljs-keyword">for</span> img <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"image"</span>]]
<span class="hljs-meta">... </span> <span class="hljs-keyword">del</span> examples[<span class="hljs-string">"image"</span>]
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> examples`}}),ce=new P({props:{code:"food = food.with_transform(transforms)",highlighted:'<span class="hljs-meta">>>> </span>food = food.with_transform(transforms)'}}),de=new P({props:{code:`from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator
<span class="hljs-meta">>>> </span>data_collator = DefaultDataCollator()`}}),ue=new Va({}),ge=new P({props:{code:`from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
model = AutoModelForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(labels),
id2label=id2label,
label2id=label2id,
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForImageClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"google/vit-base-patch16-224-in21k"</span>,
<span class="hljs-meta">... </span> num_labels=<span class="hljs-built_in">len</span>(labels),
<span class="hljs-meta">... </span> id2label=id2label,
<span class="hljs-meta">... </span> label2id=label2id,
<span class="hljs-meta">... </span>)`}}),K=new Xt({props:{$$slots:{default:[Ms]},$$scope:{ctx:z}}}),ve=new P({props:{code:`training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=16,
evaluation_strategy="steps",
num_train_epochs=4,
fp16=True,
save_steps=100,
eval_steps=100,
logging_steps=10,
learning_rate=2e-4,
save_total_limit=2,
remove_unused_columns=False,
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=food["train"],
eval_dataset=food["test"],
tokenizer=feature_extractor,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"steps"</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">4</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> save_steps=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span> eval_steps=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span> logging_steps=<span class="hljs-number">10</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-4</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span> remove_unused_columns=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span> train_dataset=food[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=food[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=feature_extractor,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),Q=new Xt({props:{$$slots:{default:[Us]},$$scope:{ctx:z}}}),{c(){h=o("meta"),k=m(),d=o("h1"),g=o("a"),E=o("span"),$(u.$$.fragment),_=m(),x=o("span"),S=l("Image classification"),A=m(),$(D.$$.fragment),X=m(),we=o("p"),Ga=l("Image classification assigns a label or class to an image. Unlike text or audio classification, the inputs are the pixel values that represent an image. 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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/question_answering.mdx-8babb2cc.js | import{S as No,i as Ro,s as Uo,e as r,k as h,w,t as a,M as Ho,c as l,d as e,m as f,a as i,x as j,h as n,b as c,F as t,g as p,y as $,q as v,o as x,B as q}from"../../chunks/vendor-4833417e.js";import{T as He}from"../../chunks/Tip-fffd6df1.js";import{Y as Bo}from"../../chunks/Youtube-27813aed.js";import{I as Nt}from"../../chunks/IconCopyLink-4b81c553.js";import{C}from"../../chunks/CodeBlock-6a3d1b46.js";import{C as Vo}from"../../chunks/CodeBlockFw-27a176a0.js";import"../../chunks/CopyButton-dacfbfaf.js";function Yo(F){let u,b,d,_,y;return{c(){u=r("p"),b=a("See the question answering "),d=r("a"),_=a("task page"),y=a(" for more information about other forms of question answering and their associated models, datasets, and metrics."),this.h()},l(m){u=l(m,"P",{});var g=i(u);b=n(g,"See the question answering "),d=l(g,"A",{href:!0,rel:!0});var E=i(d);_=n(E,"task page"),E.forEach(e),y=n(g," for more information about other forms of question answering and their associated models, datasets, and metrics."),g.forEach(e),this.h()},h(){c(d,"href","https://huggingface.co/tasks/question-answering"),c(d,"rel","nofollow")},m(m,g){p(m,u,g),t(u,b),t(u,d),t(d,_),t(u,y)},d(m){m&&e(u)}}}function Jo(F){let u,b,d,_,y,m,g,E;return{c(){u=r("p"),b=a("If you aren\u2019t familiar with fine-tuning a model with the "),d=r("a"),_=a("Trainer"),y=a(", take a look at the basic tutorial "),m=r("a"),g=a("here"),E=a("!"),this.h()},l(A){u=l(A,"P",{});var k=i(u);b=n(k,"If you aren\u2019t familiar with fine-tuning a model with the "),d=l(k,"A",{href:!0});var T=i(d);_=n(T,"Trainer"),T.forEach(e),y=n(k,", take a look at the basic tutorial "),m=l(k,"A",{href:!0});var O=i(m);g=n(O,"here"),O.forEach(e),E=n(k,"!"),k.forEach(e),this.h()},h(){c(d,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),c(m,"href","training#finetune-with-trainer")},m(A,k){p(A,u,k),t(u,b),t(u,d),t(d,_),t(u,y),t(u,m),t(m,g),t(u,E)},d(A){A&&e(u)}}}function Go(F){let u,b,d,_,y;return{c(){u=r("p"),b=a("If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),d=r("a"),_=a("here"),y=a("!"),this.h()},l(m){u=l(m,"P",{});var g=i(u);b=n(g,"If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),d=l(g,"A",{href:!0});var E=i(d);_=n(E,"here"),E.forEach(e),y=n(g,"!"),g.forEach(e),this.h()},h(){c(d,"href","training#finetune-with-keras")},m(m,g){p(m,u,g),t(u,b),t(u,d),t(d,_),t(u,y)},d(m){m&&e(u)}}}function Ko(F){let u,b,d,_,y,m,g,E;return{c(){u=r("p"),b=a(`For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding
`),d=r("a"),_=a("PyTorch notebook"),y=a(`
or `),m=r("a"),g=a("TensorFlow notebook"),E=a("."),this.h()},l(A){u=l(A,"P",{});var k=i(u);b=n(k,`For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding
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or `),m=l(k,"A",{href:!0,rel:!0});var O=i(m);g=n(O,"TensorFlow notebook"),O.forEach(e),E=n(k,"."),k.forEach(e),this.h()},h(){c(d,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb"),c(d,"rel","nofollow"),c(m,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb"),c(m,"rel","nofollow")},m(A,k){p(A,u,k),t(u,b),t(u,d),t(d,_),t(u,y),t(u,m),t(m,g),t(u,E)},d(A){A&&e(u)}}}function Wo(F){let u,b,d,_,y,m,g,E,A,k,T,O,Bs,Ve,Rt,J,lt,Ye,Je,it,Ge,Ut,Q,Ke,cs,We,Xe,us,Ze,sa,Ht,G,Vt,R,K,pt,ds,ta,ht,ea,Yt,Ns,aa,Jt,ms,Gt,Rs,na,Kt,_s,Wt,L,oa,ft,ra,la,ct,ia,pa,Xt,U,W,ut,gs,ha,dt,fa,Zt,ws,se,M,ca,mt,ua,da,_t,ma,_a,te,js,ee,Us,ga,ae,I,P,wa,gt,ja,$a,wt,va,xa,jt,qa,ba,ka,H,ya,$t,Ea,Aa,vt,Ta,Da,za,S,Ca,$s,xt,Fa,Pa,qt,Sa,Oa,bt,Qa,La,ne,X,Ma,kt,Ia,Ba,oe,vs,re,D,Na,xs,yt,Ra,Ua,Et,Ha,Va,At,Ya,Ja,le,qs,ie,B,Ga,Hs,Ka,Wa,Tt,Xa,Za,pe,bs,he,V,Z,Dt,ks,sn,zt,tn,fe,ss,en,Vs,an,nn,ce,ys,ue,ts,de,Ys,on,me,N,Es,rn,Js,ln,pn,hn,As,fn,Gs,cn,un,dn,Ts,mn,Ks,_n,gn,_e,Ds,ge,Y,es,Ct,zs,wn,Ft,jn,we,Ws,$n,je,as,$e,z,vn,Pt,xn,qn,Cs,St,bn,kn,Ot,yn,En,ve,Fs,xe,Xs,An,qe,Ps,be,ns,Tn,Zs,Dn,zn,ke,Ss,ye,os,Cn,Os,Qt,Fn,Pn,Ee,Qs,Ae,rs,Sn,Ls,Lt,On,Qn,Te,Ms,De,ls,ze;return m=new Nt({}),T=new Bo({props:{id:"ajPx5LwJD-I"}}),G=new He({props:{$$slots:{default:[Yo]},$$scope:{ctx:F}}}),ds=new Nt({}),ms=new C({props:{code:`from datasets import load_dataset
squad = load_dataset("squad")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>squad = load_dataset(<span class="hljs-string">"squad"</span>)`}}),_s=new C({props:{code:'squad["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>squad[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'answers'</span>: {<span class="hljs-string">'answer_start'</span>: [<span class="hljs-number">515</span>], <span class="hljs-string">'text'</span>: [<span class="hljs-string">'Saint Bernadette Soubirous'</span>]},
<span class="hljs-string">'context'</span>: <span class="hljs-string">'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'</span>,
<span class="hljs-string">'id'</span>: <span class="hljs-string">'5733be284776f41900661182'</span>,
<span class="hljs-string">'question'</span>: <span class="hljs-string">'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'University_of_Notre_Dame'</span>
}`}}),gs=new Nt({}),ws=new Bo({props:{id:"qgaM0weJHpA"}}),js=new C({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),vs=new C({props:{code:`def preprocess_function(examples):
questions = [q.strip() for q in examples["question"]]
inputs = tokenizer(
questions,
examples["context"],
max_length=384,
truncation="only_second",
return_offsets_mapping=True,
padding="max_length",
)
offset_mapping = inputs.pop("offset_mapping")
answers = examples["answers"]
start_positions = []
end_positions = []
for i, offset in enumerate(offset_mapping):
answer = answers[i]
start_char = answer["answer_start"][0]
end_char = answer["answer_start"][0] + len(answer["text"][0])
sequence_ids = inputs.sequence_ids(i)
# Find the start and end of the context
idx = 0
while sequence_ids[idx] != 1:
idx += 1
context_start = idx
while sequence_ids[idx] == 1:
idx += 1
context_end = idx - 1
# If the answer is not fully inside the context, label it (0, 0)
if offset[context_start][0] > end_char or offset[context_end][1] < start_char:
start_positions.append(0)
end_positions.append(0)
else:
# Otherwise it's the start and end token positions
idx = context_start
while idx <= context_end and offset[idx][0] <= start_char:
idx += 1
start_positions.append(idx - 1)
idx = context_end
while idx >= context_start and offset[idx][1] >= end_char:
idx -= 1
end_positions.append(idx + 1)
inputs["start_positions"] = start_positions
inputs["end_positions"] = end_positions
return inputs`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> questions = [q.strip() <span class="hljs-keyword">for</span> q <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"question"</span>]]
<span class="hljs-meta">... </span> inputs = tokenizer(
<span class="hljs-meta">... </span> questions,
<span class="hljs-meta">... </span> examples[<span class="hljs-string">"context"</span>],
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">384</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-string">"only_second"</span>,
<span class="hljs-meta">... </span> return_offsets_mapping=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> padding=<span class="hljs-string">"max_length"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> offset_mapping = inputs.pop(<span class="hljs-string">"offset_mapping"</span>)
<span class="hljs-meta">... </span> answers = examples[<span class="hljs-string">"answers"</span>]
<span class="hljs-meta">... </span> start_positions = []
<span class="hljs-meta">... </span> end_positions = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> i, offset <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(offset_mapping):
<span class="hljs-meta">... </span> answer = answers[i]
<span class="hljs-meta">... </span> start_char = answer[<span class="hljs-string">"answer_start"</span>][<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span> end_char = answer[<span class="hljs-string">"answer_start"</span>][<span class="hljs-number">0</span>] + <span class="hljs-built_in">len</span>(answer[<span class="hljs-string">"text"</span>][<span class="hljs-number">0</span>])
<span class="hljs-meta">... </span> sequence_ids = inputs.sequence_ids(i)
<span class="hljs-meta">... </span> <span class="hljs-comment"># Find the start and end of the context</span>
<span class="hljs-meta">... </span> idx = <span class="hljs-number">0</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> sequence_ids[idx] != <span class="hljs-number">1</span>:
<span class="hljs-meta">... </span> idx += <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> context_start = idx
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> sequence_ids[idx] == <span class="hljs-number">1</span>:
<span class="hljs-meta">... </span> idx += <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> context_end = idx - <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> <span class="hljs-comment"># If the answer is not fully inside the context, label it (0, 0)</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> offset[context_start][<span class="hljs-number">0</span>] > end_char <span class="hljs-keyword">or</span> offset[context_end][<span class="hljs-number">1</span>] < start_char:
<span class="hljs-meta">... </span> start_positions.append(<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span> end_positions.append(<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span> <span class="hljs-comment"># Otherwise it's the start and end token positions</span>
<span class="hljs-meta">... </span> idx = context_start
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> idx <= context_end <span class="hljs-keyword">and</span> offset[idx][<span class="hljs-number">0</span>] <= start_char:
<span class="hljs-meta">... </span> idx += <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> start_positions.append(idx - <span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> idx = context_end
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> idx >= context_start <span class="hljs-keyword">and</span> offset[idx][<span class="hljs-number">1</span>] >= end_char:
<span class="hljs-meta">... </span> idx -= <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> end_positions.append(idx + <span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> inputs[<span class="hljs-string">"start_positions"</span>] = start_positions
<span class="hljs-meta">... </span> inputs[<span class="hljs-string">"end_positions"</span>] = end_positions
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> inputs`}}),qs=new C({props:{code:'tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad["train"].column_names)',highlighted:'<span class="hljs-meta">>>> </span>tokenized_squad = squad.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>, remove_columns=squad[<span class="hljs-string">"train"</span>].column_names)'}}),bs=new Vo({props:{group1:{id:"pt",code:`from transformers import DefaultDataCollator
data_collator = DefaultDataCollator()`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator
<span class="hljs-meta">>>> </span>data_collator = DefaultDataCollator()`},group2:{id:"tf",code:`from transformers import DefaultDataCollator
data_collator = DefaultDataCollator(return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator
<span class="hljs-meta">>>> </span>data_collator = DefaultDataCollator(return_tensors=<span class="hljs-string">"tf"</span>)`}}}),ks=new Nt({}),ys=new C({props:{code:`from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForQuestionAnswering, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),ts=new He({props:{$$slots:{default:[Jo]},$$scope:{ctx:F}}}),Ds=new C({props:{code:`training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_squad["train"],
eval_dataset=tokenized_squad["validation"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_squad[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_squad[<span class="hljs-string">"validation"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),zs=new Nt({}),as=new He({props:{$$slots:{default:[Go]},$$scope:{ctx:F}}}),Fs=new C({props:{code:`tf_train_set = tokenized_squad["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
dummy_labels=True,
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_validation_set = tokenized_squad["validation"].to_tf_dataset(
columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
dummy_labels=True,
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = tokenized_squad[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"start_positions"</span>, <span class="hljs-string">"end_positions"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_squad[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"start_positions"</span>, <span class="hljs-string">"end_positions"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),Ps=new C({props:{code:`from transformers import create_optimizer
batch_size = 16
num_epochs = 2
total_train_steps = (len(tokenized_squad["train"]) // batch_size) * num_epochs
optimizer, schedule = create_optimizer(
init_lr=2e-5,
num_warmup_steps=0,
num_train_steps=total_train_steps,
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">2</span>
<span class="hljs-meta">>>> </span>total_train_steps = (<span class="hljs-built_in">len</span>(tokenized_squad[<span class="hljs-string">"train"</span>]) // batch_size) * num_epochs
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(
<span class="hljs-meta">... </span> init_lr=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_warmup_steps=<span class="hljs-number">0</span>,
<span class="hljs-meta">... </span> num_train_steps=total_train_steps,
<span class="hljs-meta">... </span>)`}}),Ss=new C({props:{code:`from transformers import TFAutoModelForQuestionAnswering
model = TFAutoModelForQuestionAnswering("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForQuestionAnswering
<span class="hljs-meta">>>> </span>model = TFAutoModelForQuestionAnswering(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),Qs=new C({props:{code:`import tensorflow as tf
model.compile(optimizer=optimizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)`}}),Ms=new C({props:{code:"model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)'}}),ls=new He({props:{$$slots:{default:[Ko]},$$scope:{ctx:F}}}),{c(){u=r("meta"),b=h(),d=r("h1"),_=r("a"),y=r("span"),w(m.$$.fragment),g=h(),E=r("span"),A=a("Question answering"),k=h(),w(T.$$.fragment),O=h(),Bs=r("p"),Ve=a("Question answering tasks return an answer given a question. There are two common forms of question answering:"),Rt=h(),J=r("ul"),lt=r("li"),Ye=a("Extractive: extract the answer from the given context."),Je=h(),it=r("li"),Ge=a("Abstractive: generate an answer from the context that correctly answers the question."),Ut=h(),Q=r("p"),Ke=a("This guide will show you how to fine-tune "),cs=r("a"),We=a("DistilBERT"),Xe=a(" on the "),us=r("a"),Ze=a("SQuAD"),sa=a(" dataset for extractive question answering."),Ht=h(),w(G.$$.fragment),Vt=h(),R=r("h2"),K=r("a"),pt=r("span"),w(ds.$$.fragment),ta=h(),ht=r("span"),ea=a("Load SQuAD dataset"),Yt=h(),Ns=r("p"),aa=a("Load the SQuAD dataset from the \u{1F917} Datasets library:"),Jt=h(),w(ms.$$.fragment),Gt=h(),Rs=r("p"),na=a("Then take a look at an example:"),Kt=h(),w(_s.$$.fragment),Wt=h(),L=r("p"),oa=a("The "),ft=r("code"),ra=a("answers"),la=a(" field is a dictionary containing the starting position of the answer and the "),ct=r("code"),ia=a("text"),pa=a(" of the answer."),Xt=h(),U=r("h2"),W=r("a"),ut=r("span"),w(gs.$$.fragment),ha=h(),dt=r("span"),fa=a("Preprocess"),Zt=h(),w(ws.$$.fragment),se=h(),M=r("p"),ca=a("Load the DistilBERT tokenizer to process the "),mt=r("code"),ua=a("question"),da=a(" and "),_t=r("code"),ma=a("context"),_a=a(" fields:"),te=h(),w(js.$$.fragment),ee=h(),Us=r("p"),ga=a("There are a few preprocessing steps particular to question answering that you should be aware of:"),ae=h(),I=r("ol"),P=r("li"),wa=a("Some examples in a dataset may have a very long "),gt=r("code"),ja=a("context"),$a=a(" that exceeds the maximum input length of the model. Truncate only the "),wt=r("code"),va=a("context"),xa=a(" by setting "),jt=r("code"),qa=a('truncation="only_second"'),ba=a("."),ka=h(),H=r("li"),ya=a("Next, map the start and end positions of the answer to the original "),$t=r("code"),Ea=a("context"),Aa=a(` by setting
`),vt=r("code"),Ta=a("return_offset_mapping=True"),Da=a("."),za=h(),S=r("li"),Ca=a("With the mapping in hand, you can find the start and end tokens of the answer. Use the "),$s=r("a"),xt=r("code"),Fa=a("sequence_ids"),Pa=a(` method to
find which part of the offset corresponds to the `),qt=r("code"),Sa=a("question"),Oa=a(" and which corresponds to the "),bt=r("code"),Qa=a("context"),La=a("."),ne=h(),X=r("p"),Ma=a("Here is how you can create a function to truncate and map the start and end tokens of the answer to the "),kt=r("code"),Ia=a("context"),Ba=a(":"),oe=h(),w(vs.$$.fragment),re=h(),D=r("p"),Na=a("Use \u{1F917} Datasets "),xs=r("a"),yt=r("code"),Ra=a("map"),Ua=a(" function to apply the preprocessing function over the entire dataset. You can speed up the "),Et=r("code"),Ha=a("map"),Va=a(" function by setting "),At=r("code"),Ya=a("batched=True"),Ja=a(" to process multiple elements of the dataset at once. Remove the columns you don\u2019t need:"),le=h(),w(qs.$$.fragment),ie=h(),B=r("p"),Ga=a("Use "),Hs=r("a"),Ka=a("DefaultDataCollator"),Wa=a(" to create a batch of examples. Unlike other data collators in \u{1F917} Transformers, the "),Tt=r("code"),Xa=a("DefaultDataCollator"),Za=a(" does not apply additional preprocessing such as padding."),pe=h(),w(bs.$$.fragment),he=h(),V=r("h2"),Z=r("a"),Dt=r("span"),w(ks.$$.fragment),sn=h(),zt=r("span"),tn=a("Fine-tune with Trainer"),fe=h(),ss=r("p"),en=a("Load DistilBERT with "),Vs=r("a"),an=a("AutoModelForQuestionAnswering"),nn=a(":"),ce=h(),w(ys.$$.fragment),ue=h(),w(ts.$$.fragment),de=h(),Ys=r("p"),on=a("At this point, only three steps remain:"),me=h(),N=r("ol"),Es=r("li"),rn=a("Define your training hyperparameters in "),Js=r("a"),ln=a("TrainingArguments"),pn=a("."),hn=h(),As=r("li"),fn=a("Pass the training arguments to "),Gs=r("a"),cn=a("Trainer"),un=a(" along with the model, dataset, tokenizer, and data collator."),dn=h(),Ts=r("li"),mn=a("Call "),Ks=r("a"),_n=a("train()"),gn=a(" to fine-tune your model."),_e=h(),w(Ds.$$.fragment),ge=h(),Y=r("h2"),es=r("a"),Ct=r("span"),w(zs.$$.fragment),wn=h(),Ft=r("span"),jn=a("Fine-tune with TensorFlow"),we=h(),Ws=r("p"),$n=a("To fine-tune a model in TensorFlow is just as easy, with only a few differences."),je=h(),w(as.$$.fragment),$e=h(),z=r("p"),vn=a("Convert your datasets to the "),Pt=r("code"),xn=a("tf.data.Dataset"),qn=a(" format with "),Cs=r("a"),St=r("code"),bn=a("to_tf_dataset"),kn=a(". 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Xo={local:"question-answering",sections:[{local:"load-squad-dataset",title:"Load SQuAD dataset"},{local:"preprocess",title:"Preprocess"},{local:"finetune-with-trainer",title:"Fine-tune with Trainer"},{local:"finetune-with-tensorflow",title:"Fine-tune with TensorFlow"}],title:"Question answering"};function Zo(F,u,b){let{fw:d}=u;return F.$$set=_=>{"fw"in _&&b(0,d=_.fw)},[d]}class lr extends No{constructor(u){super();Ro(this,u,Zo,Wo,Uo,{fw:0})}}export{lr as default,Xo as metadata};
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/sequence_classification.mdx-d07399b9.js | import{S as Xo,i as Zo,s as er,e as l,k as h,w as k,t as o,M as tr,c as n,d as a,m as c,a as i,x as y,h as r,b as m,F as t,g as p,y as j,q as E,o as T,B as x}from"../../chunks/vendor-4833417e.js";import{T as xt}from"../../chunks/Tip-fffd6df1.js";import{Y as ar}from"../../chunks/Youtube-27813aed.js";import{I as zt}from"../../chunks/IconCopyLink-4b81c553.js";import{C as F}from"../../chunks/CodeBlock-6a3d1b46.js";import{C as sr}from"../../chunks/CodeBlockFw-27a176a0.js";import"../../chunks/CopyButton-dacfbfaf.js";function or(C){let f,g,d,_,b;return{c(){f=l("p"),g=o("See the text classification "),d=l("a"),_=o("task page"),b=o(" for more information about other forms of text classification and their associated models, datasets, and metrics."),this.h()},l(u){f=n(u,"P",{});var $=i(f);g=r($,"See the text classification "),d=n($,"A",{href:!0,rel:!0});var w=i(d);_=r(w,"task page"),w.forEach(a),b=r($," for more information about other forms of text classification and their associated models, datasets, and metrics."),$.forEach(a),this.h()},h(){m(d,"href","https://huggingface.co/tasks/text-classification"),m(d,"rel","nofollow")},m(u,$){p(u,f,$),t(f,g),t(f,d),t(d,_),t(f,b)},d(u){u&&a(f)}}}function rr(C){let f,g,d,_,b,u,$,w;return{c(){f=l("p"),g=o("If you aren\u2019t familiar with fine-tuning a model with the "),d=l("a"),_=o("Trainer"),b=o(", take a look at the basic tutorial "),u=l("a"),$=o("here"),w=o("!"),this.h()},l(z){f=n(z,"P",{});var v=i(f);g=r(v,"If you aren\u2019t familiar with fine-tuning a model with the "),d=n(v,"A",{href:!0});var q=i(d);_=r(q,"Trainer"),q.forEach(a),b=r(v,", take a look at the basic tutorial "),u=n(v,"A",{href:!0});var S=i(u);$=r(S,"here"),S.forEach(a),w=r(v,"!"),v.forEach(a),this.h()},h(){m(d,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),m(u,"href","training#finetune-with-trainer")},m(z,v){p(z,f,v),t(f,g),t(f,d),t(d,_),t(f,b),t(f,u),t(u,$),t(f,w)},d(z){z&&a(f)}}}function lr(C){let f,g,d,_,b,u,$;return{c(){f=l("p"),g=l("a"),d=o("Trainer"),_=o(" will apply dynamic padding by default when you pass "),b=l("code"),u=o("tokenizer"),$=o(" to it. In this case, you don\u2019t need to specify a data collator explicitly."),this.h()},l(w){f=n(w,"P",{});var z=i(f);g=n(z,"A",{href:!0});var v=i(g);d=r(v,"Trainer"),v.forEach(a),_=r(z," will apply dynamic padding by default when you pass "),b=n(z,"CODE",{});var q=i(b);u=r(q,"tokenizer"),q.forEach(a),$=r(z," to it. In this case, you don\u2019t need to specify a data collator explicitly."),z.forEach(a),this.h()},h(){m(g,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer")},m(w,z){p(w,f,z),t(f,g),t(g,d),t(f,_),t(f,b),t(b,u),t(f,$)},d(w){w&&a(f)}}}function nr(C){let f,g,d,_,b;return{c(){f=l("p"),g=o("If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),d=l("a"),_=o("here"),b=o("!"),this.h()},l(u){f=n(u,"P",{});var $=i(f);g=r($,"If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),d=n($,"A",{href:!0});var w=i(d);_=r(w,"here"),w.forEach(a),b=r($,"!"),$.forEach(a),this.h()},h(){m(d,"href","training#finetune-with-keras")},m(u,$){p(u,f,$),t(f,g),t(f,d),t(d,_),t(f,b)},d(u){u&&a(f)}}}function ir(C){let f,g,d,_,b,u,$,w;return{c(){f=l("p"),g=o(`For a more in-depth example of how to fine-tune a model for text classification, take a look at the corresponding
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imdb = load_dataset("imdb")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>imdb = load_dataset(<span class="hljs-string">"imdb"</span>)`}}),ce=new F({props:{code:'imdb["test"][0]',highlighted:`<span class="hljs-meta">>>> </span>imdb[<span class="hljs-string">"test"</span>][<span class="hljs-number">0</span>]
{
<span class="hljs-string">"label"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"text"</span>: <span class="hljs-string">"I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clich\xE9d and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \\"Gene Roddenberry's Earth...\\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again."</span>,
}`}}),me=new zt({}),de=new F({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),ue=new F({props:{code:`def preprocess_function(examples):
return tokenizer(examples["text"], truncation=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenizer(examples[<span class="hljs-string">"text"</span>], truncation=<span class="hljs-literal">True</span>)`}}),ge=new F({props:{code:"tokenized_imdb = imdb.map(preprocess_function, batched=True)",highlighted:'tokenized_imdb = imdb.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)'}}),$e=new sr({props:{group1:{id:"pt",code:`from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding
<span class="hljs-meta">>>> </span>data_collator = DataCollatorWithPadding(tokenizer=tokenizer)`},group2:{id:"tf",code:`from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding
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<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)`}}),X=new xt({props:{$$slots:{default:[rr]},$$scope:{ctx:C}}}),je=new F({props:{code:`training_args = TrainingArguments(
output_dir="./results",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_imdb["train"],
eval_dataset=tokenized_imdb["test"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">5</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_imdb[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_imdb[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),Z=new xt({props:{$$slots:{default:[lr]},$$scope:{ctx:C}}}),Ee=new zt({}),te=new xt({props:{$$slots:{default:[nr]},$$scope:{ctx:C}}}),xe=new F({props:{code:`tf_train_dataset = tokenized_imdb["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "label"],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_validation_dataset = tokenized_imdb["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "label"],
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_dataset = tokenized_imdb[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_dataset = tokenized_imdb[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),ze=new F({props:{code:`from transformers import create_optimizer
import tensorflow as tf
batch_size = 16
num_epochs = 5
batches_per_epoch = len(tokenized_imdb["train"]) // batch_size
total_train_steps = int(batches_per_epoch * num_epochs)
optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">5</span>
<span class="hljs-meta">>>> </span>batches_per_epoch = <span class="hljs-built_in">len</span>(tokenized_imdb[<span class="hljs-string">"train"</span>]) // batch_size
<span class="hljs-meta">>>> </span>total_train_steps = <span class="hljs-built_in">int</span>(batches_per_epoch * num_epochs)
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(init_lr=<span class="hljs-number">2e-5</span>, num_warmup_steps=<span class="hljs-number">0</span>, num_train_steps=total_train_steps)`}}),qe=new F({props:{code:`from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)`}}),Ce=new F({props:{code:`import tensorflow as tf
model.compile(optimizer=optimizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
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eli5 = load_dataset("eli5", split="train_asks[:5000]")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>eli5 = load_dataset(<span class="hljs-string">"eli5"</span>, split=<span class="hljs-string">"train_asks[:5000]"</span>)`}}),Qe=new q({props:{code:"eli5 = eli5.train_test_split(test_size=0.2)",highlighted:'eli5 = eli5.train_test_split(test_size=<span class="hljs-number">0.2</span>)'}}),Ve=new q({props:{code:'eli5["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>eli5[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'answers'</span>: {<span class="hljs-string">'a_id'</span>: [<span class="hljs-string">'c3d1aib'</span>, <span class="hljs-string">'c3d4lya'</span>],
<span class="hljs-string">'score'</span>: [<span class="hljs-number">6</span>, <span class="hljs-number">3</span>],
<span class="hljs-string">'text'</span>: [<span class="hljs-string">"The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\\n\\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."</span>,
<span class="hljs-string">"Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"</span>]},
<span class="hljs-string">'answers_urls'</span>: {<span class="hljs-string">'url'</span>: []},
<span class="hljs-string">'document'</span>: <span class="hljs-string">''</span>,
<span class="hljs-string">'q_id'</span>: <span class="hljs-string">'nyxfp'</span>,
<span class="hljs-string">'selftext'</span>: <span class="hljs-string">'_URL_0_\\n\\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?'</span>,
<span class="hljs-string">'selftext_urls'</span>: {<span class="hljs-string">'url'</span>: [<span class="hljs-string">'http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'</span>]},
<span class="hljs-string">'subreddit'</span>: <span class="hljs-string">'askscience'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'Few questions about this space walk photograph.'</span>,
<span class="hljs-string">'title_urls'</span>: {<span class="hljs-string">'url'</span>: []}}`}}),Xe=new fe({}),Ze=new En({props:{id:"ma1TrR7gE7I"}}),et=new q({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)`}}),tt=new En({props:{id:"8PmhEIXhBvI"}}),st=new q({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilroberta-base"</span>)`}}),lt=new q({props:{code:`eli5 = eli5.flatten()
eli5["train"][0]`,highlighted:`<span class="hljs-meta">>>> </span>eli5 = eli5.flatten()
<span class="hljs-meta">>>> </span>eli5[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'answers.a_id'</span>: [<span class="hljs-string">'c3d1aib'</span>, <span class="hljs-string">'c3d4lya'</span>],
<span class="hljs-string">'answers.score'</span>: [<span class="hljs-number">6</span>, <span class="hljs-number">3</span>],
<span class="hljs-string">'answers.text'</span>: [<span class="hljs-string">"The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\\n\\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."</span>,
<span class="hljs-string">"Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"</span>],
<span class="hljs-string">'answers_urls.url'</span>: [],
<span class="hljs-string">'document'</span>: <span class="hljs-string">''</span>,
<span class="hljs-string">'q_id'</span>: <span class="hljs-string">'nyxfp'</span>,
<span class="hljs-string">'selftext'</span>: <span class="hljs-string">'_URL_0_\\n\\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?'</span>,
<span class="hljs-string">'selftext_urls.url'</span>: [<span class="hljs-string">'http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'</span>],
<span class="hljs-string">'subreddit'</span>: <span class="hljs-string">'askscience'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'Few questions about this space walk photograph.'</span>,
<span class="hljs-string">'title_urls.url'</span>: []}`}}),nt=new q({props:{code:`def preprocess_function(examples):
return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenizer([<span class="hljs-string">" "</span>.join(x) <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"answers.text"</span>]], truncation=<span class="hljs-literal">True</span>)`}}),ot=new q({props:{code:`tokenized_eli5 = eli5.map(
preprocess_function,
batched=True,
num_proc=4,
remove_columns=eli5["train"].column_names,
)`,highlighted:`<span class="hljs-meta">>>> </span>tokenized_eli5 = eli5.<span class="hljs-built_in">map</span>(
<span class="hljs-meta">... </span> preprocess_function,
<span class="hljs-meta">... </span> batched=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> num_proc=<span class="hljs-number">4</span>,
<span class="hljs-meta">... </span> remove_columns=eli5[<span class="hljs-string">"train"</span>].column_names,
<span class="hljs-meta">... </span>)`}}),pt=new q({props:{code:`block_size = 128
def group_texts(examples):
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result`,highlighted:`<span class="hljs-meta">>>> </span>block_size = <span class="hljs-number">128</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">group_texts</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> concatenated_examples = {k: <span class="hljs-built_in">sum</span>(examples[k], []) <span class="hljs-keyword">for</span> k <span class="hljs-keyword">in</span> examples.keys()}
<span class="hljs-meta">... </span> total_length = <span class="hljs-built_in">len</span>(concatenated_examples[<span class="hljs-built_in">list</span>(examples.keys())[<span class="hljs-number">0</span>]])
<span class="hljs-meta">... </span> result = {
<span class="hljs-meta">... </span> k: [t[i : i + block_size] <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, total_length, block_size)]
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> k, t <span class="hljs-keyword">in</span> concatenated_examples.items()
<span class="hljs-meta">... </span> }
<span class="hljs-meta">... </span> result[<span class="hljs-string">"labels"</span>] = result[<span class="hljs-string">"input_ids"</span>].copy()
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> result`}}),ft=new q({props:{code:"lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)",highlighted:'<span class="hljs-meta">>>> </span>lm_dataset = tokenized_eli5.<span class="hljs-built_in">map</span>(group_texts, batched=<span class="hljs-literal">True</span>, num_proc=<span class="hljs-number">4</span>)'}}),ht=new lf({props:{group1:{id:"pt",code:`from transformers import DataCollatorForLanguageModeling
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForLanguageModeling
<span class="hljs-meta">>>> </span>tokenizer.pad_token = tokenizer.eos_token
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=<span class="hljs-literal">False</span>)`},group2:{id:"tf",code:`from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForLanguageModeling
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">"tf"</span>)`}}}),mt=new lf({props:{group1:{id:"pt",code:`from transformers import DataCollatorForLanguageModeling
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForLanguageModeling
<span class="hljs-meta">>>> </span>tokenizer.pad_token = tokenizer.eos_token
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=<span class="hljs-number">0.15</span>)`},group2:{id:"tf",code:`from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForLanguageModeling
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">"tf"</span>)`}}}),ut=new fe({}),dt=new fe({}),gt=new q({props:{code:`from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
model = AutoModelForCausalLM.from_pretrained("distilgpt2")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)`}}),ye=new Es({props:{$$slots:{default:[hf]},$$scope:{ctx:P}}}),kt=new q({props:{code:`training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=lm_dataset["train"],
eval_dataset=lm_dataset["test"],
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=lm_dataset[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=lm_dataset[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),jt=new fe({}),Ee=new Es({props:{$$slots:{default:[mf]},$$scope:{ctx:P}}}),yt=new q({props:{code:`tf_train_set = lm_dataset["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
dummy_labels=True,
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_test_set = lm_dataset["test"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
dummy_labels=True,
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = lm_dataset[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = lm_dataset[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),bt=new q({props:{code:`from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)`}}),Et=new q({props:{code:`from transformers import TFAutoModelForCausalLM
model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForCausalLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)`}}),Tt=new q({props:{code:`import tensorflow as tf
model.compile(optimizer=optimizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)`}}),qt=new q({props:{code:"model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)'}}),Ft=new fe({}),Ct=new fe({}),Dt=new q({props:{code:`from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM
<span class="hljs-meta">>>> </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">"distilroberta-base"</span>)`}}),De=new Es({props:{$$slots:{default:[uf]},$$scope:{ctx:P}}}),It=new q({props:{code:`training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=lm_dataset["train"],
eval_dataset=lm_dataset["test"],
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=lm_dataset[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=lm_dataset[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),St=new fe({}),Pe=new Es({props:{$$slots:{default:[cf]},$$scope:{ctx:P}}}),Nt=new q({props:{code:`tf_train_set = lm_dataset["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
dummy_labels=True,
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_test_set = lm_dataset["test"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
dummy_labels=True,
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = lm_dataset[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = lm_dataset[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),Rt=new q({props:{code:`from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)`}}),Bt=new q({props:{code:`from transformers import TFAutoModelForMaskedLM
model = TFAutoModelForCausalLM.from_pretrained("distilroberta-base")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMaskedLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilroberta-base"</span>)`}}),Ht=new q({props:{code:`import tensorflow as tf
model.compile(optimizer=optimizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)`}}),Yt=new q({props:{code:"model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)'}}),Oe=new Es({props:{$$slots:{default:[df]},$$scope:{ctx:P}}}),{c(){u=r("meta"),E=f(),c=r("h1"),v=r("a"),x=r("span"),g(d.$$.fragment),b=f(),A=r("span"),T=l("Language modeling"),y=f(),F=r("p"),C=l("Language modeling predicts words in a sentence. There are two forms of language modeling."),L=f(),g(W.$$.fragment),N=f(),U=r("p"),Ge=l("Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on the left."),R=f(),g(Y.$$.fragment),he=f(),M=r("p"),z=l("Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally."),He=f(),D=r("p"),Jt=l("This guide will show you how to fine-tune "),I=r("a"),Kt=l("DistilGPT2"),Qt=l(" for causal language modeling and "),We=r("a"),xn=l("DistilRoBERTa"),Tn=l(" for masked language modeling on the "),Ye=r("a"),An=l("r/askscience"),qn=l(" subset of the "),Ue=r("a"),Fn=l("ELI5"),Mn=l(" dataset."),ba=f(),g(me.$$.fragment),Ea=f(),se=r("h2"),ue=r("a"),xs=r("span"),g(Je.$$.fragment),Cn=f(),Ts=r("span"),Dn=l("Load ELI5 dataset"),xa=f(),Vt=r("p"),zn=l("Load only the first 5000 rows of the ELI5 dataset from the \u{1F917} Datasets library since it is pretty large:"),Ta=f(),g(Ke.$$.fragment),Aa=f(),Xt=r("p"),Pn=l("Split this dataset into a train and test set:"),qa=f(),g(Qe.$$.fragment),Fa=f(),Zt=r("p"),Ln=l("Then take a look at an example:"),Ma=f(),g(Ve.$$.fragment),Ca=f(),B=r("p"),In=l("Notice "),As=r("code"),Sn=l("text"),On=l(" is a subfield nested inside the "),qs=r("code"),Nn=l("answers"),Rn=l(" dictionary. When you preprocess the dataset, you will need to extract the "),Fs=r("code"),Bn=l("text"),Gn=l(" subfield into a separate column."),Da=f(),ae=r("h2"),ce=r("a"),Ms=r("span"),g(Xe.$$.fragment),Hn=f(),Cs=r("span"),Wn=l("Preprocess"),za=f(),g(Ze.$$.fragment),Pa=f(),de=r("p"),Yn=l("For causal language modeling, load the DistilGPT2 tokenizer to process the "),Ds=r("code"),Un=l("text"),Jn=l(" subfield:"),La=f(),g(et.$$.fragment),Ia=f(),g(tt.$$.fragment),Sa=f(),es=r("p"),Kn=l("For masked language modeling, load the DistilRoBERTa tokenizer instead:"),Oa=f(),g(st.$$.fragment),Na=f(),J=r("p"),Qn=l("Extract the "),zs=r("code"),Vn=l("text"),Xn=l(" subfield from its nested structure with the "),at=r("a"),Ps=r("code"),Zn=l("flatten"),er=l(" method:"),Ra=f(),g(lt.$$.fragment),Ba=f(),K=r("p"),tr=l("Each subfield is now a separate column as indicated by the "),Ls=r("code"),sr=l("answers"),ar=l(" prefix. Notice that "),Is=r("code"),lr=l("answers.text"),nr=l(" is a list. Instead of tokenizing each sentence separately, convert the list to a string to jointly tokenize them."),Ga=f(),ts=r("p"),rr=l("Here is how you can create a preprocessing function to convert the list to a string and truncate sequences to be no longer than DistilGPT2\u2019s maximum input length:"),Ha=f(),g(nt.$$.fragment),Wa=f(),S=r("p"),or=l("Use \u{1F917} Datasets "),rt=r("a"),Ss=r("code"),ir=l("map"),pr=l(" function to apply the preprocessing function over the entire dataset. You can speed up the "),Os=r("code"),fr=l("map"),hr=l(" function by setting "),Ns=r("code"),mr=l("batched=True"),ur=l(" to process multiple elements of the dataset at once and increasing the number of processes with "),Rs=r("code"),cr=l("num_proc"),dr=l(". Remove the columns you don\u2019t need:"),Ya=f(),g(ot.$$.fragment),Ua=f(),ss=r("p"),gr=l("Now you need a second preprocessing function to capture text truncated from any lengthy examples to prevent loss of information. This preprocessing function should:"),Ja=f(),ge=r("ul"),Bs=r("li"),_r=l("Concatenate all the text."),$r=f(),it=r("li"),wr=l("Split the concatenated text into smaller chunks defined by "),Gs=r("code"),kr=l("block_size"),jr=l("."),Ka=f(),g(pt.$$.fragment),Qa=f(),_e=r("p"),vr=l("Apply the "),Hs=r("code"),yr=l("group_texts"),br=l(" function over the entire dataset:"),Va=f(),g(ft.$$.fragment),Xa=f(),O=r("p"),Er=l("For causal language modeling, use "),as=r("a"),xr=l("DataCollatorForLanguageModeling"),Tr=l(" to create a batch of examples. It will also "),Ws=r("em"),Ar=l("dynamically pad"),qr=l(" your text to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the "),Ys=r("code"),Fr=l("tokenizer"),Mr=l(" function by setting "),Us=r("code"),Cr=l("padding=True"),Dr=l(", dynamic padding is more efficient."),Za=f(),$e=r("p"),zr=l("You can use the end of sequence token as the padding token, and set "),Js=r("code"),Pr=l("mlm=False"),Lr=l(". This will use the inputs as labels shifted to the right by one element:"),el=f(),g(ht.$$.fragment),tl=f(),Q=r("p"),Ir=l("For masked language modeling, use the same "),ls=r("a"),Sr=l("DataCollatorForLanguageModeling"),Or=l(" except you should specify "),Ks=r("code"),Nr=l("mlm_probability"),Rr=l(" to randomly mask tokens each time you iterate over the data."),sl=f(),g(mt.$$.fragment),al=f(),le=r("h2"),we=r("a"),Qs=r("span"),g(ut.$$.fragment),Br=f(),Vs=r("span"),Gr=l("Causal language modeling"),ll=f(),ke=r("p"),Hr=l("Causal language modeling is frequently used for text generation. This section shows you how to fine-tune "),ct=r("a"),Wr=l("DistilGPT2"),Yr=l(" to generate new text."),nl=f(),ne=r("h3"),je=r("a"),Xs=r("span"),g(dt.$$.fragment),Ur=f(),Zs=r("span"),Jr=l("Fine-tune with Trainer"),rl=f(),ve=r("p"),Kr=l("Load DistilGPT2 with "),ns=r("a"),Qr=l("AutoModelForCausalLM"),Vr=l(":"),ol=f(),g(gt.$$.fragment),il=f(),g(ye.$$.fragment),pl=f(),rs=r("p"),Xr=l("At this point, only three steps remain:"),fl=f(),V=r("ol"),_t=r("li"),Zr=l("Define your training hyperparameters in "),os=r("a"),eo=l("TrainingArguments"),to=l("."),so=f(),$t=r("li"),ao=l("Pass the training arguments to "),is=r("a"),lo=l("Trainer"),no=l(" along with the model, datasets, and data collator."),ro=f(),wt=r("li"),oo=l("Call "),ps=r("a"),io=l("train()"),po=l(" to fine-tune your model."),hl=f(),g(kt.$$.fragment),ml=f(),re=r("h3"),be=r("a"),ea=r("span"),g(jt.$$.fragment),fo=f(),ta=r("span"),ho=l("Fine-tune with TensorFlow"),ul=f(),fs=r("p"),mo=l("To fine-tune a model in TensorFlow is just as easy, with only a few differences."),cl=f(),g(Ee.$$.fragment),dl=f(),G=r("p"),uo=l("Convert your datasets to the "),sa=r("code"),co=l("tf.data.Dataset"),go=l(" format with "),vt=r("a"),aa=r("code"),_o=l("to_tf_dataset"),$o=l(". Specify inputs and labels in "),la=r("code"),wo=l("columns"),ko=l(", whether to shuffle the dataset order, batch size, and the data collator:"),gl=f(),g(yt.$$.fragment),_l=f(),hs=r("p"),jo=l("Set up an optimizer function, learning rate, and some training hyperparameters:"),$l=f(),g(bt.$$.fragment),wl=f(),xe=r("p"),vo=l("Load DistilGPT2 with "),ms=r("a"),yo=l("TFAutoModelForCausalLM"),bo=l(":"),kl=f(),g(Et.$$.fragment),jl=f(),Te=r("p"),Eo=l("Configure the model for training with "),xt=r("a"),na=r("code"),xo=l("compile"),To=l(":"),vl=f(),g(Tt.$$.fragment),yl=f(),Ae=r("p"),Ao=l("Call "),At=r("a"),ra=r("code"),qo=l("fit"),Fo=l(" to fine-tune the model:"),bl=f(),g(qt.$$.fragment),El=f(),oe=r("h2"),qe=r("a"),oa=r("span"),g(Ft.$$.fragment),Mo=f(),ia=r("span"),Co=l("Masked language modeling"),xl=f(),Fe=r("p"),Do=l("Masked language modeling is also known as a fill-mask task because it predicts a masked token in a sequence. Models for masked language modeling require a good contextual understanding of an entire sequence instead of only the left context. 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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/asr.mdx-b010f320.js | import{S as _n,i as gn,s as jn,e as o,k as c,w as j,t as e,M as $n,c as r,d as t,m as h,a as i,x as $,h as n,b as u,F as a,g as p,y as v,q as b,o as w,B as y}from"../../chunks/vendor-4833417e.js";import{T as Ee}from"../../chunks/Tip-fffd6df1.js";import{Y as vn}from"../../chunks/Youtube-27813aed.js";import{I as tt}from"../../chunks/IconCopyLink-4b81c553.js";import{C as P}from"../../chunks/CodeBlock-6a3d1b46.js";import"../../chunks/CopyButton-dacfbfaf.js";function bn(N){let f,x,d,_,E;return{c(){f=o("p"),x=e("See the automatic speech recognition "),d=o("a"),_=e("task page"),E=e(" for more information about its associated models, datasets, and metrics."),this.h()},l(m){f=r(m,"P",{});var k=i(f);x=n(k,"See the automatic speech recognition "),d=r(k,"A",{href:!0,rel:!0});var T=i(d);_=n(T,"task page"),T.forEach(t),E=n(k," for more information about its associated models, datasets, and metrics."),k.forEach(t),this.h()},h(){u(d,"href","https://huggingface.co/tasks/automatic-speech-recognition"),u(d,"rel","nofollow")},m(m,k){p(m,f,k),a(f,x),a(f,d),a(d,_),a(f,E)},d(m){m&&t(f)}}}function wn(N){let f,x,d,_,E,m,k,T;return{c(){f=o("p"),x=e("If you aren\u2019t familiar with fine-tuning a model with the "),d=o("a"),_=e("Trainer"),E=e(", take a look at the basic tutorial "),m=o("a"),k=e("here"),T=e("!"),this.h()},l(q){f=r(q,"P",{});var g=i(f);x=n(g,"If you aren\u2019t familiar with fine-tuning a model with the "),d=r(g,"A",{href:!0});var C=i(d);_=n(C,"Trainer"),C.forEach(t),E=n(g,", take a look at the basic tutorial "),m=r(g,"A",{href:!0});var I=i(m);k=n(I,"here"),I.forEach(t),T=n(g,"!"),g.forEach(t),this.h()},h(){u(d,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),u(m,"href","training#finetune-with-trainer")},m(q,g){p(q,f,g),a(f,x),a(f,d),a(d,_),a(f,E),a(f,m),a(m,k),a(f,T)},d(q){q&&t(f)}}}function yn(N){let f,x,d,_,E,m,k,T;return{c(){f=o("p"),x=e("For a more in-depth example of how to fine-tune a model for automatic speech recognition, take a look at this blog "),d=o("a"),_=e("post"),E=e(" for English ASR and this "),m=o("a"),k=e("post"),T=e(" for multilingual ASR."),this.h()},l(q){f=r(q,"P",{});var g=i(f);x=n(g,"For a more in-depth example of how to fine-tune a model for automatic speech recognition, take a look at this blog "),d=r(g,"A",{href:!0,rel:!0});var C=i(d);_=n(C,"post"),C.forEach(t),E=n(g," for English ASR and this "),m=r(g,"A",{href:!0,rel:!0});var I=i(m);k=n(I,"post"),I.forEach(t),T=n(g," for multilingual ASR."),g.forEach(t),this.h()},h(){u(d,"href","https://huggingface.co/blog/fine-tune-wav2vec2-english"),u(d,"rel","nofollow"),u(m,"href","https://huggingface.co/blog/fine-tune-xlsr-wav2vec2"),u(m,"rel","nofollow")},m(q,g){p(q,f,g),a(f,x),a(f,d),a(d,_),a(f,E),a(f,m),a(m,k),a(f,T)},d(q){q&&t(f)}}}function xn(N){let f,x,d,_,E,m,k,T,q,g,C,I,ys,et,ua,S,nt,ss,lt,ot,as,rt,it,da,H,ma,U,B,Ns,ts,pt,Us,ct,_a,xs,ht,ga,es,ja,ks,ft,$a,ns,va,A,ut,Vs,dt,mt,Ys,_t,gt,Hs,jt,$t,Bs,vt,bt,ba,ls,wa,Es,wt,ya,os,xa,L,yt,Js,xt,kt,Gs,Et,Tt,ka,V,J,Ks,rs,qt,Qs,At,Ea,Ts,Ct,Ta,is,qa,qs,Dt,Aa,M,ps,Pt,Xs,It,St,Lt,cs,Mt,Zs,Wt,Ot,Ft,sa,zt,Ca,hs,Da,W,Rt,fs,aa,Nt,Ut,ta,Vt,Yt,Pa,us,Ia,D,Ht,As,Bt,Jt,ea,Gt,Kt,na,Qt,Xt,Sa,O,Zt,la,se,ae,oa,te,ee,La,ds,Ma,G,ne,ra,le,oe,Wa,ms,Oa,Y,K,ia,_s,re,pa,ie,Fa,F,pe,Cs,ce,he,ca,fe,ue,za,gs,Ra,Q,Na,Ds,de,Ua,z,js,me,Ps,_e,ge,je,$s,$e,Is,ve,be,we,vs,ye,Ss,xe,ke,Va,bs,Ya,X,Ha;return m=new tt({}),C=new vn({props:{id:"TksaY_FDgnk"}}),H=new Ee({props:{$$slots:{default:[bn]},$$scope:{ctx:N}}}),ts=new tt({}),es=new P({props:{code:`from datasets import load_dataset
timit = load_dataset("timit_asr")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>timit = load_dataset(<span class="hljs-string">"timit_asr"</span>)`}}),ns=new P({props:{code:"timit",highlighted:`<span class="hljs-meta">>>> </span>timit
DatasetDict({
train: Dataset({
features: [<span class="hljs-string">'file'</span>, <span class="hljs-string">'audio'</span>, <span class="hljs-string">'text'</span>, <span class="hljs-string">'phonetic_detail'</span>, <span class="hljs-string">'word_detail'</span>, <span class="hljs-string">'dialect_region'</span>, <span class="hljs-string">'sentence_type'</span>, <span class="hljs-string">'speaker_id'</span>, <span class="hljs-string">'id'</span>],
num_rows: <span class="hljs-number">4620</span>
})
test: Dataset({
features: [<span class="hljs-string">'file'</span>, <span class="hljs-string">'audio'</span>, <span class="hljs-string">'text'</span>, <span class="hljs-string">'phonetic_detail'</span>, <span class="hljs-string">'word_detail'</span>, <span class="hljs-string">'dialect_region'</span>, <span class="hljs-string">'sentence_type'</span>, <span class="hljs-string">'speaker_id'</span>, <span class="hljs-string">'id'</span>],
num_rows: <span class="hljs-number">1680</span>
})
})`}}),ls=new P({props:{code:`timit = timit.remove_columns(
["phonetic_detail", "word_detail", "dialect_region", "id", "sentence_type", "speaker_id"]
)`,highlighted:`<span class="hljs-meta">>>> </span>timit = timit.remove_columns(
<span class="hljs-meta">... </span> [<span class="hljs-string">"phonetic_detail"</span>, <span class="hljs-string">"word_detail"</span>, <span class="hljs-string">"dialect_region"</span>, <span class="hljs-string">"id"</span>, <span class="hljs-string">"sentence_type"</span>, <span class="hljs-string">"speaker_id"</span>]
<span class="hljs-meta">... </span>)`}}),os=new P({props:{code:'timit["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>timit[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'audio'</span>: {<span class="hljs-string">'array'</span>: array([-<span class="hljs-number">2.1362305e-04</span>, <span class="hljs-number">6.1035156e-05</span>, <span class="hljs-number">3.0517578e-05</span>, ...,
-<span class="hljs-number">3.0517578e-05</span>, -<span class="hljs-number">9.1552734e-05</span>, -<span class="hljs-number">6.1035156e-05</span>], dtype=float32),
<span class="hljs-string">'path'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'</span>,
<span class="hljs-string">'sampling_rate'</span>: <span class="hljs-number">16000</span>},
<span class="hljs-string">'file'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'</span>,
<span class="hljs-string">'text'</span>: <span class="hljs-string">'Would such an act of refusal be useful?'</span>}`}}),rs=new tt({}),is=new P({props:{code:`from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
<span class="hljs-meta">>>> </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">"facebook/wav2vec2-base"</span>)`}}),hs=new P({props:{code:`def prepare_dataset(batch):
audio = batch["audio"]
batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
batch["input_length"] = len(batch["input_values"])
with processor.as_target_processor():
batch["labels"] = processor(batch["text"]).input_ids
return batch`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">prepare_dataset</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span> audio = batch[<span class="hljs-string">"audio"</span>]
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"input_values"</span>] = processor(audio[<span class="hljs-string">"array"</span>], sampling_rate=audio[<span class="hljs-string">"sampling_rate"</span>]).input_values[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"input_length"</span>] = <span class="hljs-built_in">len</span>(batch[<span class="hljs-string">"input_values"</span>])
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> processor.as_target_processor():
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"labels"</span>] = processor(batch[<span class="hljs-string">"text"</span>]).input_ids
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> batch`}}),us=new P({props:{code:'timit = timit.map(prepare_dataset, remove_columns=timit.column_names["train"], num_proc=4)',highlighted:'<span class="hljs-meta">>>> </span>timit = timit.<span class="hljs-built_in">map</span>(prepare_dataset, remove_columns=timit.column_names[<span class="hljs-string">"train"</span>], num_proc=<span class="hljs-number">4</span>)'}}),ds=new P({props:{code:`import torch
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
@dataclass
class DataCollatorCTCWithPadding:
processor: AutoProcessor
padding: Union[bool, str] = True
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# split inputs and labels since they have to be of different lengths and need
# different padding methods
input_features = [{"input_values": feature["input_values"]} for feature in features]
label_features = [{"input_ids": feature["labels"]} for feature in features]
batch = self.processor.pad(
input_features,
padding=self.padding,
return_tensors="pt",
)
with self.processor.as_target_processor():
labels_batch = self.processor.pad(
label_features,
padding=self.padding,
return_tensors="pt",
)
# replace padding with -100 to ignore loss correctly
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
batch["labels"] = labels
return batch`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass, field
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">Any</span>, <span class="hljs-type">Dict</span>, <span class="hljs-type">List</span>, <span class="hljs-type">Optional</span>, <span class="hljs-type">Union</span>
<span class="hljs-meta">>>> </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">DataCollatorCTCWithPadding</span>:
<span class="hljs-meta">... </span> processor: AutoProcessor
<span class="hljs-meta">... </span> padding: <span class="hljs-type">Union</span>[<span class="hljs-built_in">bool</span>, <span class="hljs-built_in">str</span>] = <span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, features: <span class="hljs-type">List</span>[<span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, <span class="hljs-type">Union</span>[<span class="hljs-type">List</span>[<span class="hljs-built_in">int</span>], torch.Tensor]]]</span>) -> <span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, torch.Tensor]:
<span class="hljs-meta">... </span> <span class="hljs-comment"># split inputs and labels since they have to be of different lengths and need</span>
<span class="hljs-meta">... </span> <span class="hljs-comment"># different padding methods</span>
<span class="hljs-meta">... </span> input_features = [{<span class="hljs-string">"input_values"</span>: feature[<span class="hljs-string">"input_values"</span>]} <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span> label_features = [{<span class="hljs-string">"input_ids"</span>: feature[<span class="hljs-string">"labels"</span>]} <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span> batch = self.processor.pad(
<span class="hljs-meta">... </span> input_features,
<span class="hljs-meta">... </span> padding=self.padding,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> self.processor.as_target_processor():
<span class="hljs-meta">... </span> labels_batch = self.processor.pad(
<span class="hljs-meta">... </span> label_features,
<span class="hljs-meta">... </span> padding=self.padding,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> <span class="hljs-comment"># replace padding with -100 to ignore loss correctly</span>
<span class="hljs-meta">... </span> labels = labels_batch[<span class="hljs-string">"input_ids"</span>].masked_fill(labels_batch.attention_mask.ne(<span class="hljs-number">1</span>), -<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"labels"</span>] = labels
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> batch`}}),ms=new P({props:{code:"data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)",highlighted:'<span class="hljs-meta">>>> </span>data_collator = DataCollatorCTCWithPadding(processor=processor, padding=<span class="hljs-literal">True</span>)'}}),_s=new tt({}),gs=new P({props:{code:`from transformers import AutoModelForCTC, TrainingArguments, Trainer
model = AutoModelForCTC.from_pretrained(
"facebook/wav2vec-base",
ctc_loss_reduction="mean",
pad_token_id=processor.tokenizer.pad_token_id,
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCTC, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForCTC.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"facebook/wav2vec-base"</span>,
<span class="hljs-meta">... </span> ctc_loss_reduction=<span class="hljs-string">"mean"</span>,
<span class="hljs-meta">... </span> pad_token_id=processor.tokenizer.pad_token_id,
<span class="hljs-meta">... </span>)`}}),Q=new Ee({props:{$$slots:{default:[wn]},$$scope:{ctx:N}}}),bs=new P({props:{code:`training_args = TrainingArguments(
output_dir="./results",
group_by_length=True,
per_device_train_batch_size=16,
evaluation_strategy="steps",
num_train_epochs=3,
fp16=True,
gradient_checkpointing=True,
learning_rate=1e-4,
weight_decay=0.005,
save_total_limit=2,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=timit["train"],
eval_dataset=timit["test"],
tokenizer=processor.feature_extractor,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> group_by_length=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"steps"</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> gradient_checkpointing=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">1e-4</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.005</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=timit[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=timit[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=processor.feature_extractor,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),X=new Ee({props:{$$slots:{default:[yn]},$$scope:{ctx:N}}}),{c(){f=o("meta"),x=c(),d=o("h1"),_=o("a"),E=o("span"),j(m.$$.fragment),k=c(),T=o("span"),q=e("Automatic speech recognition"),g=c(),j(C.$$.fragment),I=c(),ys=o("p"),et=e("Automatic speech recognition (ASR) converts a speech signal to text. It is an example of a sequence-to-sequence task, going from a sequence of audio inputs to textual outputs. Voice assistants like Siri and Alexa utilize ASR models to assist users."),ua=c(),S=o("p"),nt=e("This guide will show you how to fine-tune "),ss=o("a"),lt=e("Wav2Vec2"),ot=e(" on the "),as=o("a"),rt=e("TIMIT"),it=e(" dataset to transcribe audio to text."),da=c(),j(H.$$.fragment),ma=c(),U=o("h2"),B=o("a"),Ns=o("span"),j(ts.$$.fragment),pt=c(),Us=o("span"),ct=e("Load TIMIT dataset"),_a=c(),xs=o("p"),ht=e("Load the TIMIT dataset from the \u{1F917} Datasets library:"),ga=c(),j(es.$$.fragment),ja=c(),ks=o("p"),ft=e("Then take a look at an example:"),$a=c(),j(ns.$$.fragment),va=c(),A=o("p"),ut=e("While the dataset contains a lot of helpful information, like "),Vs=o("code"),dt=e("dialect_region"),mt=e(" and "),Ys=o("code"),_t=e("sentence_type"),gt=e(", you will focus on the "),Hs=o("code"),jt=e("audio"),$t=e(" and "),Bs=o("code"),vt=e("text"),bt=e(" fields in this guide. Remove the other columns:"),ba=c(),j(ls.$$.fragment),wa=c(),Es=o("p"),wt=e("Take a look at the example again:"),ya=c(),j(os.$$.fragment),xa=c(),L=o("p"),yt=e("The "),Js=o("code"),xt=e("audio"),kt=e(" column contains a 1-dimensional "),Gs=o("code"),Et=e("array"),Tt=e(" of the speech signal that must be called to load and resample the audio file."),ka=c(),V=o("h2"),J=o("a"),Ks=o("span"),j(rs.$$.fragment),qt=c(),Qs=o("span"),At=e("Preprocess"),Ea=c(),Ts=o("p"),Ct=e("Load the Wav2Vec2 processor to process the audio signal and transcribed text:"),Ta=c(),j(is.$$.fragment),qa=c(),qs=o("p"),Dt=e("The preprocessing function needs to:"),Aa=c(),M=o("ol"),ps=o("li"),Pt=e("Call the "),Xs=o("code"),It=e("audio"),St=e(" column to load and resample the audio file."),Lt=c(),cs=o("li"),Mt=e("Extract the "),Zs=o("code"),Wt=e("input_values"),Ot=e(" from the audio file."),Ft=c(),sa=o("li"),zt=e("Typically, when you call the processor, you call the feature extractor. Since you also want to tokenize text, instruct the processor to call the tokenizer instead with a context manager."),Ca=c(),j(hs.$$.fragment),Da=c(),W=o("p"),Rt=e("Use \u{1F917} Datasets "),fs=o("a"),aa=o("code"),Nt=e("map"),Ut=e(" function to apply the preprocessing function over the entire dataset. You can speed up the map function by increasing the number of processes with "),ta=o("code"),Vt=e("num_proc"),Yt=e(". Remove the columns you don\u2019t need:"),Pa=c(),j(us.$$.fragment),Ia=c(),D=o("p"),Ht=e("\u{1F917} Transformers doesn\u2019t have a data collator for automatic speech recognition, so you will need to create one. You can adapt the "),As=o("a"),Bt=e("DataCollatorWithPadding"),Jt=e(" to create a batch of examples for automatic speech recognition. It will also dynamically pad your text and labels to the length of the longest element in its batch, so they are a uniform length. 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kn={local:"automatic-speech-recognition",sections:[{local:"load-timit-dataset",title:"Load TIMIT dataset"},{local:"preprocess",title:"Preprocess"},{local:"finetune-with-trainer",title:"Fine-tune with Trainer"}],title:"Automatic speech recognition"};function En(N,f,x){let{fw:d}=f;return N.$$set=_=>{"fw"in _&&x(0,d=_.fw)},[d]}class In extends _n{constructor(f){super();gn(this,f,En,xn,jn,{fw:0})}}export{In as default,kn as metadata};
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/translation.mdx-9674fec0.js | import{S as Ro,i as Xo,s as Go,e as r,k as f,w as $,t as o,M as Qo,c as l,d as t,m as h,a as i,x as k,h as n,b as u,F as s,g as p,y as w,q as v,o as b,B as q}from"../../chunks/vendor-4833417e.js";import{T as Ts}from"../../chunks/Tip-fffd6df1.js";import{Y as Ko}from"../../chunks/Youtube-27813aed.js";import{I as yt}from"../../chunks/IconCopyLink-4b81c553.js";import{C as A}from"../../chunks/CodeBlock-6a3d1b46.js";import{C as Vo}from"../../chunks/CodeBlockFw-27a176a0.js";import"../../chunks/CopyButton-dacfbfaf.js";function en(C){let m,j,c,_,E;return{c(){m=r("p"),j=o("See the translation "),c=r("a"),_=o("task page"),E=o(" for more information about its associated models, datasets, and metrics."),this.h()},l(d){m=l(d,"P",{});var g=i(m);j=n(g,"See the translation "),c=l(g,"A",{href:!0,rel:!0});var S=i(c);_=n(S,"task page"),S.forEach(t),E=n(g," for more information about its associated models, datasets, and metrics."),g.forEach(t),this.h()},h(){u(c,"href","https://huggingface.co/tasks/translation"),u(c,"rel","nofollow")},m(d,g){p(d,m,g),s(m,j),s(m,c),s(c,_),s(m,E)},d(d){d&&t(m)}}}function tn(C){let m,j,c,_,E,d,g,S;return{c(){m=r("p"),j=o("If you aren\u2019t familiar with fine-tuning a model with the "),c=r("a"),_=o("Trainer"),E=o(", take a look at the basic tutorial "),d=r("a"),g=o("here"),S=o("!"),this.h()},l(T){m=l(T,"P",{});var y=i(m);j=n(y,"If you aren\u2019t familiar with fine-tuning a model with the "),c=l(y,"A",{href:!0});var z=i(c);_=n(z,"Trainer"),z.forEach(t),E=n(y,", take a look at the basic tutorial "),d=l(y,"A",{href:!0});var D=i(d);g=n(D,"here"),D.forEach(t),S=n(y,"!"),y.forEach(t),this.h()},h(){u(c,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),u(d,"href","training#finetune-with-trainer")},m(T,y){p(T,m,y),s(m,j),s(m,c),s(c,_),s(m,E),s(m,d),s(d,g),s(m,S)},d(T){T&&t(m)}}}function sn(C){let m,j,c,_,E;return{c(){m=r("p"),j=o("If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),c=r("a"),_=o("here"),E=o("!"),this.h()},l(d){m=l(d,"P",{});var g=i(m);j=n(g,"If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),c=l(g,"A",{href:!0});var S=i(c);_=n(S,"here"),S.forEach(t),E=n(g,"!"),g.forEach(t),this.h()},h(){u(c,"href","training#finetune-with-keras")},m(d,g){p(d,m,g),s(m,j),s(m,c),s(c,_),s(m,E)},d(d){d&&t(m)}}}function an(C){let m,j,c,_,E,d,g,S;return{c(){m=r("p"),j=o(`For a more in-depth example of how to fine-tune a model for translation, take a look at the corresponding
`),c=r("a"),_=o("PyTorch notebook"),E=o(`
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books = load_dataset("opus_books", "en-fr")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>books = load_dataset(<span class="hljs-string">"opus_books"</span>, <span class="hljs-string">"en-fr"</span>)`}}),pe=new A({props:{code:'books = books["train"].train_test_split(test_size=0.2)',highlighted:'books = books[<span class="hljs-string">"train"</span>].train_test_split(test_size=<span class="hljs-number">0.2</span>)'}}),fe=new A({props:{code:'books["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>books[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'id'</span>: <span class="hljs-string">'90560'</span>,
<span class="hljs-string">'translation'</span>: {<span class="hljs-string">'en'</span>: <span class="hljs-string">'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.'</span>,
<span class="hljs-string">'fr'</span>: <span class="hljs-string">'Mais ce plateau \xE9lev\xE9 ne mesurait que quelques toises, et bient\xF4t nous f\xFBmes rentr\xE9s dans notre \xE9l\xE9ment.'</span>}}`}}),he=new yt({}),me=new Ko({props:{id:"XAR8jnZZuUs"}}),ue=new A({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("t5-small")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-small"</span>)`}}),de=new A({props:{code:`source_lang = "en"
target_lang = "fr"
prefix = "translate English to French: "
def preprocess_function(examples):
inputs = [prefix + example[source_lang] for example in examples["translation"]]
targets = [example[target_lang] for example in examples["translation"]]
model_inputs = tokenizer(inputs, max_length=128, truncation=True)
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=128, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs`,highlighted:`<span class="hljs-meta">>>> </span>source_lang = <span class="hljs-string">"en"</span>
<span class="hljs-meta">>>> </span>target_lang = <span class="hljs-string">"fr"</span>
<span class="hljs-meta">>>> </span>prefix = <span class="hljs-string">"translate English to French: "</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> inputs = [prefix + example[source_lang] <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"translation"</span>]]
<span class="hljs-meta">... </span> targets = [example[target_lang] <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"translation"</span>]]
<span class="hljs-meta">... </span> model_inputs = tokenizer(inputs, max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span> labels = tokenizer(targets, max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> model_inputs[<span class="hljs-string">"labels"</span>] = labels[<span class="hljs-string">"input_ids"</span>]
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> model_inputs`}}),ge=new A({props:{code:"tokenized_books = books.map(preprocess_function, batched=True)",highlighted:'<span class="hljs-meta">>>> </span>tokenized_books = books.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)'}}),$e=new Vo({props:{group1:{id:"pt",code:`from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)`},group2:{id:"tf",code:`from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, return_tensors=<span class="hljs-string">"tf"</span>)`}}}),ke=new yt({}),we=new A({props:{code:`from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
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output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=1,
fp16=True,
)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_books["train"],
eval_dataset=tokenized_books["test"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = Seq2SeqTrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Seq2SeqTrainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_books[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_books[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),ye=new yt({}),Q=new Ts({props:{$$slots:{default:[sn]},$$scope:{ctx:C}}}),Se=new A({props:{code:`tf_train_set = tokenized_books["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_test_set = tokenized_books["test"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = tokenized_books[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = tokenized_books[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),Te=new A({props:{code:`from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)`}}),xe=new A({props:{code:`from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-small"</span>)`}}),Ae=new A({props:{code:"model.compile(optimizer=optimizer)",highlighted:'<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)'}}),Pe=new A({props:{code:"model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)'}}),se=new Ts({props:{$$slots:{default:[an]},$$scope:{ctx:C}}}),{c(){m=r("meta"),j=f(),c=r("h1"),_=r("a"),E=r("span"),$(d.$$.fragment),g=f(),S=r("span"),T=o("Translation"),y=f(),$(z.$$.fragment),D=f(),De=r("p"),xs=o("Translation converts a sequence of text from one language to another. It is one of several tasks you can formulate as a sequence-to-sequence problem, a powerful framework that extends to vision and audio tasks."),Et=f(),L=r("p"),zs=o("This guide will show you how to fine-tune "),ne=r("a"),As=o("T5"),Fs=o(" on the English-French subset of the "),re=r("a"),Ps=o("OPUS Books"),Cs=o(" dataset to translate English text to French."),St=f(),$(Y.$$.fragment),Tt=f(),N=r("h2"),H=r("a"),Ve=r("span"),$(le.$$.fragment),Ds=f(),et=r("span"),Ls=o("Load OPUS Books dataset"),xt=f(),Le=r("p"),Ms=o("Load the OPUS Books dataset from the \u{1F917} Datasets library:"),zt=f(),$(ie.$$.fragment),At=f(),Me=r("p"),Os=o("Split this dataset into a train and test set:"),Ft=f(),$(pe.$$.fragment),Pt=f(),Oe=r("p"),Is=o("Then take a look at an example:"),Ct=f(),$(fe.$$.fragment),Dt=f(),Z=r("p"),Ns=o("The "),tt=r("code"),Us=o("translation"),Bs=o(" field is a dictionary containing the English and French translations of the text."),Lt=f(),U=r("h2"),J=r("a"),st=r("span"),$(he.$$.fragment),Ws=f(),at=r("span"),Ys=o("Preprocess"),Mt=f(),$(me.$$.fragment),Ot=f(),Ie=r("p"),Hs=o("Load the T5 tokenizer to process the language pairs:"),It=f(),$(ue.$$.fragment),Nt=f(),Ne=r("p"),Zs=o("The preprocessing function needs to:"),Ut=f(),M=r("ol"),ot=r("li"),Js=o("Prefix the input with a prompt so T5 knows this is a translation task. Some models capable of multiple NLP tasks require prompting for specific tasks."),Ks=f(),nt=r("li"),Rs=o("Tokenize the input (English) and target (French) separately. You can\u2019t tokenize French text with a tokenizer pretrained on an English vocabulary. A context manager will help set the tokenizer to French first before tokenizing it."),Xs=f(),ce=r("li"),Gs=o("Truncate sequences to be no longer than the maximum length set by the "),rt=r("code"),Qs=o("max_length"),Vs=o(" parameter."),Bt=f(),$(de.$$.fragment),Wt=f(),F=r("p"),ea=o("Use \u{1F917} Datasets "),_e=r("a"),lt=r("code"),ta=o("map"),sa=o(" function to apply the preprocessing function over the entire dataset. You can speed up the "),it=r("code"),aa=o("map"),oa=o(" function by setting "),pt=r("code"),na=o("batched=True"),ra=o(" to process multiple elements of the dataset at once:"),Yt=f(),$(ge.$$.fragment),Ht=f(),x=r("p"),la=o("Use "),Ue=r("a"),ia=o("DataCollatorForSeq2Seq"),pa=o(" to create a batch of examples. 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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_16143/en/_app/pages/tasks/summarization.mdx-34e69920.js | import{S as $n,i as wn,s as vn,e as r,k as c,w as b,t as o,M as yn,c as i,d as a,m as h,a as l,x as $,h as n,b as m,F as t,g as p,y as w,q as v,o as y,B as q}from"../../chunks/vendor-4833417e.js";import{T as Ma}from"../../chunks/Tip-fffd6df1.js";import{Y as qn}from"../../chunks/Youtube-27813aed.js";import{I as Ft}from"../../chunks/IconCopyLink-4b81c553.js";import{C as A}from"../../chunks/CodeBlock-6a3d1b46.js";import{C as kn}from"../../chunks/CodeBlockFw-27a176a0.js";import"../../chunks/CopyButton-dacfbfaf.js";function jn(P){let f,k,u,g,S;return{c(){f=r("p"),k=o("See the summarization "),u=r("a"),g=o("task page"),S=o(" for more information about its associated models, datasets, and metrics."),this.h()},l(d){f=i(d,"P",{});var _=l(f);k=n(_,"See the summarization "),u=i(_,"A",{href:!0,rel:!0});var E=l(u);g=n(E,"task page"),E.forEach(a),S=n(_," for more information about its associated models, datasets, and metrics."),_.forEach(a),this.h()},h(){m(u,"href","https://huggingface.co/tasks/summarization"),m(u,"rel","nofollow")},m(d,_){p(d,f,_),t(f,k),t(f,u),t(u,g),t(f,S)},d(d){d&&a(f)}}}function Sn(P){let f,k,u,g,S,d,_,E;return{c(){f=r("p"),k=o("If you aren\u2019t familiar with fine-tuning a model with the "),u=r("a"),g=o("Trainer"),S=o(", take a look at the basic tutorial "),d=r("a"),_=o("here"),E=o("!"),this.h()},l(z){f=i(z,"P",{});var j=l(f);k=n(j,"If you aren\u2019t familiar with fine-tuning a model with the "),u=i(j,"A",{href:!0});var x=l(u);g=n(x,"Trainer"),x.forEach(a),S=n(j,", take a look at the basic tutorial "),d=i(j,"A",{href:!0});var D=l(d);_=n(D,"here"),D.forEach(a),E=n(j,"!"),j.forEach(a),this.h()},h(){m(u,"href","/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer"),m(d,"href","training#finetune-with-trainer")},m(z,j){p(z,f,j),t(f,k),t(f,u),t(u,g),t(f,S),t(f,d),t(d,_),t(f,E)},d(z){z&&a(f)}}}function En(P){let f,k,u,g,S;return{c(){f=r("p"),k=o("If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),u=r("a"),g=o("here"),S=o("!"),this.h()},l(d){f=i(d,"P",{});var _=l(f);k=n(_,"If you aren\u2019t familiar with fine-tuning a model with Keras, take a look at the basic tutorial "),u=i(_,"A",{href:!0});var E=l(u);g=n(E,"here"),E.forEach(a),S=n(_,"!"),_.forEach(a),this.h()},h(){m(u,"href","training#finetune-with-keras")},m(d,_){p(d,f,_),t(f,k),t(f,u),t(u,g),t(f,S)},d(d){d&&a(f)}}}function zn(P){let f,k,u,g,S,d,_,E;return{c(){f=r("p"),k=o(`For a more in-depth example of how to fine-tune a model for summarization, take a look at the corresponding
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billsum = load_dataset("billsum", split="ca_test")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>billsum = load_dataset(<span class="hljs-string">"billsum"</span>, split=<span class="hljs-string">"ca_test"</span>)`}}),he=new A({props:{code:"billsum = billsum.train_test_split(test_size=0.2)",highlighted:'<span class="hljs-meta">>>> </span>billsum = billsum.train_test_split(test_size=<span class="hljs-number">0.2</span>)'}}),fe=new A({props:{code:'billsum["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>billsum[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'summary'</span>: <span class="hljs-string">'Existing law authorizes state agencies to enter into contracts for the acquisition of goods or services upon approval by the Department of General Services. Existing law sets forth various requirements and prohibitions for those contracts, including, but not limited to, a prohibition on entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between spouses and domestic partners or same-sex and different-sex couples in the provision of benefits. Existing law provides that a contract entered into in violation of those requirements and prohibitions is void and authorizes the state or any person acting on behalf of the state to bring a civil action seeking a determination that a contract is in violation and therefore void. Under existing law, a willful violation of those requirements and prohibitions is a misdemeanor.\\nThis bill would also prohibit a state agency from entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between employees on the basis of gender identity in the provision of benefits, as specified. By expanding the scope of a crime, this bill would impose a state-mandated local program.\\nThe California Constitution requires the state to reimburse local agencies and school districts for certain costs mandated by the state. Statutory provisions establish procedures for making that reimbursement.\\nThis bill would provide that no reimbursement is required by this act for a specified reason.'</span>,
<span class="hljs-string">'text'</span>: <span class="hljs-string">'The people of the State of California do enact as follows:\\n\\n\\nSECTION 1.\\nSection 10295.35 is added to the Public Contract Code, to read:\\n10295.35.\\n(a) (1) Notwithstanding any other law, a state agency shall not enter into any contract for the acquisition of goods or services in the amount of one hundred thousand dollars ($100,000) or more with a contractor that, in the provision of benefits, discriminates between employees on the basis of an employee\u2019s or dependent\u2019s actual or perceived gender identity, including, but not limited to, the employee\u2019s or dependent\u2019s identification as transgender.\\n(2) For purposes of this section, \u201Ccontract\u201D includes contracts with a cumulative amount of one hundred thousand dollars ($100,000) or more per contractor in each fiscal year.\\n(3) For purposes of this section, an employee health plan is discriminatory if the plan is not consistent with Section 1365.5 of the Health and Safety Code and Section 10140 of the Insurance Code.\\n(4) The requirements of this section shall apply only to those portions of a contractor\u2019s operations that occur under any of the following conditions:\\n(A) Within the state.\\n(B) On real property outside the state if the property is owned by the state or if the state has a right to occupy the property, and if the contractor\u2019s presence at that location is connected to a contract with the state.\\n(C) Elsewhere in the United States where work related to a state contract is being performed.\\n(b) Contractors shall treat as confidential, to the maximum extent allowed by law or by the requirement of the contractor\u2019s insurance provider, any request by an employee or applicant for employment benefits or any documentation of eligibility for benefits submitted by an employee or applicant for employment.\\n(c) After taking all reasonable measures to find a contractor that complies with this section, as determined by the state agency, the requirements of this section may be waived under any of the following circumstances:\\n(1) There is only one prospective contractor willing to enter into a specific contract with the state agency.\\n(2) The contract is necessary to respond to an emergency, as determined by the state agency, that endangers the public health, welfare, or safety, or the contract is necessary for the provision of essential services, and no entity that complies with the requirements of this section capable of responding to the emergency is immediately available.\\n(3) The requirements of this section violate, or are inconsistent with, the terms or conditions of a grant, subvention, or agreement, if the agency has made a good faith attempt to change the terms or conditions of any grant, subvention, or agreement to authorize application of this section.\\n(4) The contractor is providing wholesale or bulk water, power, or natural gas, the conveyance or transmission of the same, or ancillary services, as required for ensuring reliable services in accordance with good utility practice, if the purchase of the same cannot practically be accomplished through the standard competitive bidding procedures and the contractor is not providing direct retail services to end users.\\n(d) (1) A contractor shall not be deemed to discriminate in the provision of benefits if the contractor, in providing the benefits, pays the actual costs incurred in obtaining the benefit.\\n(2) If a contractor is unable to provide a certain benefit, despite taking reasonable measures to do so, the contractor shall not be deemed to discriminate in the provision of benefits.\\n(e) (1) Every contract subject to this chapter shall contain a statement by which the contractor certifies that the contractor is in compliance with this section.\\n(2) The department or other contracting agency shall enforce this section pursuant to its existing enforcement powers.\\n(3) (A) If a contractor falsely certifies that it is in compliance with this section, the contract with that contractor shall be subject to Article 9 (commencing with Section 10420), unless, within a time period specified by the department or other contracting agency, the contractor provides to the department or agency proof that it has complied, or is in the process of complying, with this section.\\n(B) The application of the remedies or penalties contained in Article 9 (commencing with Section 10420) to a contract subject to this chapter shall not preclude the application of any existing remedies otherwise available to the department or other contracting agency under its existing enforcement powers.\\n(f) Nothing in this section is intended to regulate the contracting practices of any local jurisdiction.\\n(g) This section shall be construed so as not to conflict with applicable federal laws, rules, or regulations. In the event that a court or agency of competent jurisdiction holds that federal law, rule, or regulation invalidates any clause, sentence, paragraph, or section of this code or the application thereof to any person or circumstances, it is the intent of the state that the court or agency sever that clause, sentence, paragraph, or section so that the remainder of this section shall remain in effect.\\nSEC. 2.\\nSection 10295.35 of the Public Contract Code shall not be construed to create any new enforcement authority or responsibility in the Department of General Services or any other contracting agency.\\nSEC. 3.\\nNo reimbursement is required by this act pursuant to Section 6 of Article XIII\\u2009B of the California Constitution because the only costs that may be incurred by a local agency or school district will be incurred because this act creates a new crime or infraction, eliminates a crime or infraction, or changes the penalty for a crime or infraction, within the meaning of Section 17556 of the Government Code, or changes the definition of a crime within the meaning of Section 6 of Article XIII\\u2009B of the California Constitution.'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'An act to add Section 10295.35 to the Public Contract Code, relating to public contracts.'</span>}`}}),me=new Ft({}),ue=new A({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("t5-small")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-small"</span>)`}}),_e=new A({props:{code:`prefix = "summarize: "
def preprocess_function(examples):
inputs = [prefix + doc for doc in examples["text"]]
model_inputs = tokenizer(inputs, max_length=1024, truncation=True)
with tokenizer.as_target_tokenizer():
labels = tokenizer(examples["summary"], max_length=128, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs`,highlighted:`<span class="hljs-meta">>>> </span>prefix = <span class="hljs-string">"summarize: "</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> inputs = [prefix + doc <span class="hljs-keyword">for</span> doc <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"text"</span>]]
<span class="hljs-meta">... </span> model_inputs = tokenizer(inputs, max_length=<span class="hljs-number">1024</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span> labels = tokenizer(examples[<span class="hljs-string">"summary"</span>], max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> model_inputs[<span class="hljs-string">"labels"</span>] = labels[<span class="hljs-string">"input_ids"</span>]
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> model_inputs`}}),$e=new A({props:{code:"tokenized_billsum = billsum.map(preprocess_function, batched=True)",highlighted:'<span class="hljs-meta">>>> </span>tokenized_billsum = billsum.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)'}}),we=new kn({props:{group1:{id:"pt",code:`from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)`},group2:{id:"tf",code:`from transformers import DataCollatorForSeq2Seq
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, return_tensors=<span class="hljs-string">"tf"</span>)`}}}),ve=new Ft({}),ye=new A({props:{code:`from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-small"</span>)`}}),V=new Ma({props:{$$slots:{default:[Sn]},$$scope:{ctx:P}}}),Se=new A({props:{code:`training_args = Seq2SeqTrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=1,
fp16=True,
)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_billsum["train"],
eval_dataset=tokenized_billsum["test"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = Seq2SeqTrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Seq2SeqTrainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_billsum[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_billsum[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),Ee=new Ft({}),ee=new Ma({props:{$$slots:{default:[En]},$$scope:{ctx:P}}}),Te=new A({props:{code:`tf_train_set = tokenized_billsum["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_test_set = tokenized_billsum["test"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = tokenized_billsum[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = tokenized_billsum[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),xe=new A({props:{code:`from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)`}}),Ae=new A({props:{code:`from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-small"</span>)`}}),Fe=new A({props:{code:"model.compile(optimizer=optimizer)",highlighted:'<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)'}}),De=new A({props:{code:"model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)'}}),oe=new Ma({props:{$$slots:{default:[zn]},$$scope:{ctx:P}}}),{c(){f=r("meta"),k=c(),u=r("h1"),g=r("a"),S=r("span"),b(d.$$.fragment),_=c(),E=r("span"),z=o("Summarization"),j=c(),b(x.$$.fragment),D=c(),Me=r("p"),Ia=o("Summarization creates a shorter version of a document or an article that captures all the important information. Along with translation, it is another example of a task that can be formulated as a sequence-to-sequence task. 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inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
model = GPT2LMHeadModel.from_pretrained(<span class="hljs-string">"gpt2"</span>)
inputs = tokenizer(<span class="hljs-string">"Hello, my dog is cute and "</span>, return_tensors=<span class="hljs-string">"pt"</span>)
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. <code>(max_length-input_ids.shape[-1],)</code>-shaped tuple of <code>torch.FloatTensor</code> with each
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
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Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences, generated_length, hidden_size)</code>.`,name:"decoder_hidden_states"}]}}),pr=new b({props:{name:"class transformers.generation_flax_utils.FlaxSampleOutput",anchor:"transformers.generation_flax_utils.FlaxSampleOutput",parameters:[{name:"sequences",val:": ndarray = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_utils.py#L59",parametersDescription:[{anchor:"transformers.generation_flax_utils.FlaxSampleOutput.sequences",description:`<strong>sequences</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, max_length)</code>) —
The generated sequences.`,name:"sequences"}]}}),fr=new b({props:{name:"replace",anchor:"None",parameters:[{name:"**updates",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120"}}),mr=new qe({}),hr=new b({props:{name:"class transformers.generation_utils.BeamSearchDecoderOnlyOutput",anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L204",parametersDescription:[{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-input_ids.shape[-1],)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape
<code>(batch_size*num_beams*num_return_sequences, config.vocab_size)</code>).`,name:"scores"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-input_ids.shape[-1],)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.`,name:"attentions"},{anchor:"transformers.generation_utils.BeamSearchDecoderOnlyOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)</code>.`,name:"hidden_states"}]}}),gr=new b({props:{name:"class transformers.generation_utils.BeamSearchEncoderDecoderOutput",anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None"},{name:"encoder_attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"encoder_hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"decoder_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"cross_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"decoder_hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L239",parametersDescription:[{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-1,)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape <code>(batch_size*num_beams, config.vocab_size)</code>).`,name:"scores"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-1,)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.attentions",description:"<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —",name:"attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_attentions",description:`<strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.`,name:"encoder_attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)</code>.`,name:"encoder_hidden_states"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_attentions",description:`<strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, num_heads, generated_length, sequence_length)</code>.`,name:"decoder_attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.`,name:"cross_attentions"},{anchor:"transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_hidden_states",description:`<strong>decoder_hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)</code>.`,name:"decoder_hidden_states"}]}}),ur=new qe({}),_r=new b({props:{name:"class transformers.generation_utils.BeamSampleDecoderOnlyOutput",anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None"},{name:"attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L290",parametersDescription:[{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_return_sequence)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-input_ids.shape[-1],)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape
<code>(batch_size*num_beams*num_return_sequences, config.vocab_size)</code>).`,name:"scores"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-input_ids.shape[-1],)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.attentions",description:`<strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.`,name:"attentions"},{anchor:"transformers.generation_utils.BeamSampleDecoderOnlyOutput.hidden_states",description:`<strong>hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, generated_length, hidden_size)</code>.`,name:"hidden_states"}]}}),vr=new b({props:{name:"class transformers.generation_utils.BeamSampleEncoderDecoderOutput",anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput",parameters:[{name:"sequences",val:": LongTensor = None"},{name:"sequences_scores",val:": typing.Optional[torch.FloatTensor] = None"},{name:"scores",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"beam_indices",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None"},{name:"encoder_attentions",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"encoder_hidden_states",val:": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"},{name:"decoder_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"cross_attentions",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"decoder_hidden_states",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L325",parametersDescription:[{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences",description:`<strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_beams, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.`,name:"sequences"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences_scores",description:`<strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_return_sequence)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.`,name:"sequences_scores"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.scores",description:`<strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-1,)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape <code>(batch_size*num_beams, config.vocab_size)</code>).`,name:"scores"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.beam_indices",description:`<strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-1,)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.`,name:"beam_indices"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_attentions",description:`<strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.`,name:"encoder_attentions"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size*num_beams, sequence_length, hidden_size)</code>.`,name:"encoder_hidden_states"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_attentions",description:`<strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.`,name:"decoder_attentions"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.cross_attentions",description:`<strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.`,name:"cross_attentions"},{anchor:"transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_hidden_states",description:`<strong>decoder_hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, generated_length, hidden_size)</code>.`,name:"decoder_hidden_states"}]}}),br=new qe({}),$r=new b({props:{name:"class transformers.LogitsProcessor",anchor:"transformers.LogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L51"}}),Tr=new b({props:{name:"__call__",anchor:"transformers.LogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L54",parametersDescription:[{anchor:"transformers.LogitsProcessor.__call__.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LogitsProcessor.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),yr=new b({props:{name:"class transformers.LogitsProcessorList",anchor:"transformers.LogitsProcessorList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L73"}}),kr=new b({props:{name:"__call__",anchor:"transformers.LogitsProcessorList.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L80",parametersDescription:[{anchor:"transformers.LogitsProcessorList.__call__.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LogitsProcessorList.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),wr=new b({props:{name:"class transformers.LogitsWarper",anchor:"transformers.LogitsWarper",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L62"}}),Lr=new b({props:{name:"__call__",anchor:"transformers.LogitsWarper.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L65",parametersDescription:[{anchor:"transformers.LogitsWarper.__call__.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LogitsWarper.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),xr=new b({props:{name:"class transformers.MinLengthLogitsProcessor",anchor:"transformers.MinLengthLogitsProcessor",parameters:[{name:"min_length",val:": int"},{name:"eos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L96",parametersDescription:[{anchor:"transformers.MinLengthLogitsProcessor.min_length",description:`<strong>min_length</strong> (<code>int</code>) —
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float("Inf")</code>.`,name:"min_length"},{anchor:"transformers.MinLengthLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}]}}),Er=new b({props:{name:"__call__",anchor:"transformers.MinLengthLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L117"}}),Pr=new b({props:{name:"class transformers.TemperatureLogitsWarper",anchor:"transformers.TemperatureLogitsWarper",parameters:[{name:"temperature",val:": float"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L124",parametersDescription:[{anchor:"transformers.TemperatureLogitsWarper.temperature",description:`<strong>temperature</strong> (<code>float</code>) —
The value used to module the logits distribution.`,name:"temperature"}]}}),Fr=new b({props:{name:"__call__",anchor:"transformers.TemperatureLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L139"}}),Dr=new b({props:{name:"class transformers.RepetitionPenaltyLogitsProcessor",anchor:"transformers.RepetitionPenaltyLogitsProcessor",parameters:[{name:"penalty",val:": float"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L144",parametersDescription:[{anchor:"transformers.RepetitionPenaltyLogitsProcessor.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"}]}}),zr=new b({props:{name:"__call__",anchor:"transformers.RepetitionPenaltyLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L160"}}),Sr=new b({props:{name:"class transformers.TopPLogitsWarper",anchor:"transformers.TopPLogitsWarper",parameters:[{name:"top_p",val:": float"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L170",parametersDescription:[{anchor:"transformers.TopPLogitsWarper.top_p",description:`<strong>top_p</strong> (<code>float</code>) —
If set to < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher are kept
for generation.`,name:"top_p"},{anchor:"transformers.TopPLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TopPLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}]}}),Or=new b({props:{name:"__call__",anchor:"transformers.TopPLogitsWarper.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L193"}}),qr=new b({props:{name:"class transformers.TopKLogitsWarper",anchor:"transformers.TopKLogitsWarper",parameters:[{name:"top_k",val:": int"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L212",parametersDescription:[{anchor:"transformers.TopKLogitsWarper.top_k",description:`<strong>top_k</strong> (<code>int</code>) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.TopKLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TopKLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}]}}),Br=new b({props:{name:"__call__",anchor:"transformers.TopKLogitsWarper.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L233"}}),Ir=new b({props:{name:"class transformers.NoRepeatNGramLogitsProcessor",anchor:"transformers.NoRepeatNGramLogitsProcessor",parameters:[{name:"ngram_size",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L309",parametersDescription:[{anchor:"transformers.NoRepeatNGramLogitsProcessor.ngram_size",description:`<strong>ngram_size</strong> (<code>int</code>) —
All ngrams of size <code>ngram_size</code> can only occur once.`,name:"ngram_size"}]}}),Cr=new b({props:{name:"__call__",anchor:"transformers.NoRepeatNGramLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L324"}}),Nr=new b({props:{name:"class transformers.NoBadWordsLogitsProcessor",anchor:"transformers.NoBadWordsLogitsProcessor",parameters:[{name:"bad_words_ids",val:": typing.List[typing.List[int]]"},{name:"eos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L376",parametersDescription:[{anchor:"transformers.NoBadWordsLogitsProcessor.bad_words_ids",description:`<strong>bad_words_ids</strong> (<code>List[List[int]]</code>) —
List of list of token ids that are not allowed to be generated. In order to get the token ids of the words
that should not appear in the generated text, use <code>tokenizer(bad_words, add_prefix_space=True, add_special_tokens=False).input_ids</code>.`,name:"bad_words_ids"},{anchor:"transformers.NoBadWordsLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}]}}),Wr=new b({props:{name:"__call__",anchor:"transformers.NoBadWordsLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L418"}}),Vr=new b({props:{name:"class transformers.PrefixConstrainedLogitsProcessor",anchor:"transformers.PrefixConstrainedLogitsProcessor",parameters:[{name:"prefix_allowed_tokens_fn",val:": typing.Callable[[int, torch.Tensor], typing.List[int]]"},{name:"num_beams",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L504"}}),Gr=new b({props:{name:"__call__",anchor:"transformers.PrefixConstrainedLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L521"}}),jr=new b({props:{name:"class transformers.HammingDiversityLogitsProcessor",anchor:"transformers.HammingDiversityLogitsProcessor",parameters:[{name:"diversity_penalty",val:": float"},{name:"num_beams",val:": int"},{name:"num_beam_groups",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L530",parametersDescription:[{anchor:"transformers.HammingDiversityLogitsProcessor.diversity_penalty",description:`<strong>diversity_penalty</strong> (<code>float</code>) —
This value is subtracted from a beam’s score if it generates a token same as any beam from other group at a
particular time. Note that <code>diversity_penalty</code> is only effective if <code>group beam search</code> is enabled.`,name:"diversity_penalty"},{anchor:"transformers.HammingDiversityLogitsProcessor.num_beams",description:`<strong>num_beams</strong> (<code>int</code>) —
Number of beams used for group beam search. See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more
details.`,name:"num_beams"},{anchor:"transformers.HammingDiversityLogitsProcessor.num_beam_groups",description:`<strong>num_beam_groups</strong> (<code>int</code>) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.`,name:"num_beam_groups"}]}}),Rr=new b({props:{name:"__call__",anchor:"transformers.HammingDiversityLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"current_tokens",val:": LongTensor"},{name:"beam_group_idx",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L561"}}),Kr=new b({props:{name:"class transformers.ForcedBOSTokenLogitsProcessor",anchor:"transformers.ForcedBOSTokenLogitsProcessor",parameters:[{name:"bos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L590",parametersDescription:[{anchor:"transformers.ForcedBOSTokenLogitsProcessor.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>) —
The id of the token to force as the first generated token.`,name:"bos_token_id"}]}}),Ur=new b({props:{name:"__call__",anchor:"transformers.ForcedBOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L602"}}),Yr=new b({props:{name:"class transformers.ForcedEOSTokenLogitsProcessor",anchor:"transformers.ForcedEOSTokenLogitsProcessor",parameters:[{name:"max_length",val:": int"},{name:"eos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L611",parametersDescription:[{anchor:"transformers.ForcedEOSTokenLogitsProcessor.max_length",description:`<strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.ForcedEOSTokenLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.`,name:"eos_token_id"}]}}),Xr=new b({props:{name:"__call__",anchor:"transformers.ForcedEOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L626"}}),Jr=new b({props:{name:"class transformers.InfNanRemoveLogitsProcessor",anchor:"transformers.InfNanRemoveLogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L635"}}),Qr=new b({props:{name:"__call__",anchor:"transformers.InfNanRemoveLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L642"}}),Zr=new b({props:{name:"class transformers.TFLogitsProcessor",anchor:"transformers.TFLogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L50"}}),eo=new b({props:{name:"__call__",anchor:"transformers.TFLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L53",parametersDescription:[{anchor:"transformers.TFLogitsProcessor.__call__.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFLogitsProcessor.__call__.scores",description:`<strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),to=new b({props:{name:"class transformers.TFLogitsProcessorList",anchor:"transformers.TFLogitsProcessorList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L72"}}),ro=new b({props:{name:"__call__",anchor:"transformers.TFLogitsProcessorList.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L79",parametersDescription:[{anchor:"transformers.TFLogitsProcessorList.__call__.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFLogitsProcessorList.__call__.scores",description:`<strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),oo=new b({props:{name:"class transformers.TFLogitsWarper",anchor:"transformers.TFLogitsWarper",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L61"}}),no=new b({props:{name:"__call__",anchor:"transformers.TFLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L64",parametersDescription:[{anchor:"transformers.TFLogitsWarper.__call__.input_ids",description:`<strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFLogitsWarper.__call__.scores",description:`<strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),so=new b({props:{name:"class transformers.TFTemperatureLogitsWarper",anchor:"transformers.TFTemperatureLogitsWarper",parameters:[{name:"temperature",val:": float"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L95",parametersDescription:[{anchor:"transformers.TFTemperatureLogitsWarper.temperature",description:`<strong>temperature</strong> (<code>float</code>) —
The value used to module the logits distribution.`,name:"temperature"}]}}),ao=new b({props:{name:"__call__",anchor:"transformers.TFTemperatureLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L110"}}),io=new b({props:{name:"class transformers.TFTopPLogitsWarper",anchor:"transformers.TFTopPLogitsWarper",parameters:[{name:"top_p",val:": float"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L144",parametersDescription:[{anchor:"transformers.TFTopPLogitsWarper.top_p",description:`<strong>top_p</strong> (<code>float</code>) —
If set to < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher are kept
for generation.`,name:"top_p"},{anchor:"transformers.TFTopPLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TFTopPLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}]}}),co=new b({props:{name:"__call__",anchor:"transformers.TFTopPLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L166"}}),lo=new b({props:{name:"class transformers.TFTopKLogitsWarper",anchor:"transformers.TFTopKLogitsWarper",parameters:[{name:"top_k",val:": int"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L115",parametersDescription:[{anchor:"transformers.TFTopKLogitsWarper.top_k",description:`<strong>top_k</strong> (<code>int</code>) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.TFTopKLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.TFTopKLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}]}}),po=new b({props:{name:"__call__",anchor:"transformers.TFTopKLogitsWarper.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L136"}}),fo=new b({props:{name:"class transformers.TFMinLengthLogitsProcessor",anchor:"transformers.TFMinLengthLogitsProcessor",parameters:[{name:"min_length",val:": int"},{name:"eos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L198",parametersDescription:[{anchor:"transformers.TFMinLengthLogitsProcessor.min_length",description:`<strong>min_length</strong> (<code>int</code>) —
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float("Inf")</code>.`,name:"min_length"},{anchor:"transformers.TFMinLengthLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}]}}),mo=new b({props:{name:"__call__",anchor:"transformers.TFMinLengthLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L219"}}),ho=new b({props:{name:"class transformers.TFNoBadWordsLogitsProcessor",anchor:"transformers.TFNoBadWordsLogitsProcessor",parameters:[{name:"bad_words_ids",val:": typing.List[typing.List[int]]"},{name:"eos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L271",parametersDescription:[{anchor:"transformers.TFNoBadWordsLogitsProcessor.bad_words_ids",description:`<strong>bad_words_ids</strong> (<code>List[List[int]]</code>) —
List of list of token ids that are not allowed to be generated. In order to get the tokens of the words
that should not appear in the generated text, use <code>tokenizer(bad_word, add_prefix_space=True).input_ids</code>.`,name:"bad_words_ids"},{anchor:"transformers.TFNoBadWordsLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}]}}),go=new b({props:{name:"__call__",anchor:"transformers.TFNoBadWordsLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L334"}}),uo=new b({props:{name:"class transformers.TFNoRepeatNGramLogitsProcessor",anchor:"transformers.TFNoRepeatNGramLogitsProcessor",parameters:[{name:"ngram_size",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L354",parametersDescription:[{anchor:"transformers.TFNoRepeatNGramLogitsProcessor.ngram_size",description:`<strong>ngram_size</strong> (<code>int</code>) —
All ngrams of size <code>ngram_size</code> can only occur once.`,name:"ngram_size"}]}}),vo=new b({props:{name:"__call__",anchor:"transformers.TFNoRepeatNGramLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L392"}}),bo=new b({props:{name:"class transformers.TFRepetitionPenaltyLogitsProcessor",anchor:"transformers.TFRepetitionPenaltyLogitsProcessor",parameters:[{name:"penalty",val:": float"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L233",parametersDescription:[{anchor:"transformers.TFRepetitionPenaltyLogitsProcessor.repetition_penalty",description:`<strong>repetition_penalty</strong> (<code>float</code>) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.`,name:"repetition_penalty"}]}}),$o=new b({props:{name:"__call__",anchor:"transformers.TFRepetitionPenaltyLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": Tensor"},{name:"scores",val:": Tensor"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L262"}}),To=new b({props:{name:"class transformers.FlaxLogitsProcessor",anchor:"transformers.FlaxLogitsProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L50"}}),yo=new b({props:{name:"__call__",anchor:"transformers.FlaxLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L53",parametersDescription:[{anchor:"transformers.FlaxLogitsProcessor.__call__.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxLogitsProcessor.__call__.scores",description:`<strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),ko=new b({props:{name:"class transformers.FlaxLogitsProcessorList",anchor:"transformers.FlaxLogitsProcessorList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L72"}}),wo=new b({props:{name:"__call__",anchor:"transformers.FlaxLogitsProcessorList.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L79",parametersDescription:[{anchor:"transformers.FlaxLogitsProcessorList.__call__.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxLogitsProcessorList.__call__.scores",description:`<strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),Lo=new b({props:{name:"class transformers.FlaxLogitsWarper",anchor:"transformers.FlaxLogitsWarper",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L61"}}),xo=new b({props:{name:"__call__",anchor:"transformers.FlaxLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L64",parametersDescription:[{anchor:"transformers.FlaxLogitsWarper.__call__.input_ids",description:`<strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxLogitsWarper.__call__.scores",description:`<strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.`,name:"scores"}],returnDescription:`
<p>The processed prediction scores.</p>
`,returnType:`
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
`}}),Eo=new b({props:{name:"class transformers.FlaxTemperatureLogitsWarper",anchor:"transformers.FlaxTemperatureLogitsWarper",parameters:[{name:"temperature",val:": float"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L95",parametersDescription:[{anchor:"transformers.FlaxTemperatureLogitsWarper.temperature",description:`<strong>temperature</strong> (<code>float</code>) —
The value used to module the logits distribution.`,name:"temperature"}]}}),Po=new b({props:{name:"__call__",anchor:"transformers.FlaxTemperatureLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L110"}}),Fo=new b({props:{name:"class transformers.FlaxTopPLogitsWarper",anchor:"transformers.FlaxTopPLogitsWarper",parameters:[{name:"top_p",val:": float"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L115",parametersDescription:[{anchor:"transformers.FlaxTopPLogitsWarper.top_p",description:`<strong>top_p</strong> (<code>float</code>) —
If set to < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher are kept
for generation.`,name:"top_p"},{anchor:"transformers.FlaxTopPLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.FlaxTopPLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}]}}),Do=new b({props:{name:"__call__",anchor:"transformers.FlaxTopPLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L137"}}),zo=new b({props:{name:"class transformers.FlaxTopKLogitsWarper",anchor:"transformers.FlaxTopKLogitsWarper",parameters:[{name:"top_k",val:": int"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L156",parametersDescription:[{anchor:"transformers.FlaxTopKLogitsWarper.top_k",description:`<strong>top_k</strong> (<code>int</code>) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.`,name:"top_k"},{anchor:"transformers.FlaxTopKLogitsWarper.filter_value",description:`<strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.`,name:"filter_value"},{anchor:"transformers.FlaxTopKLogitsWarper.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.`,name:"min_tokens_to_keep"}]}}),So=new b({props:{name:"__call__",anchor:"transformers.FlaxTopKLogitsWarper.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L177"}}),Oo=new b({props:{name:"class transformers.FlaxForcedBOSTokenLogitsProcessor",anchor:"transformers.FlaxForcedBOSTokenLogitsProcessor",parameters:[{name:"bos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L192",parametersDescription:[{anchor:"transformers.FlaxForcedBOSTokenLogitsProcessor.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>) —
The id of the token to force as the first generated token.`,name:"bos_token_id"}]}}),qo=new b({props:{name:"__call__",anchor:"transformers.FlaxForcedBOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L204"}}),Bo=new b({props:{name:"class transformers.FlaxForcedEOSTokenLogitsProcessor",anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor",parameters:[{name:"max_length",val:": int"},{name:"eos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L216",parametersDescription:[{anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor.max_length",description:`<strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.`,name:"eos_token_id"}]}}),Io=new b({props:{name:"__call__",anchor:"transformers.FlaxForcedEOSTokenLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L231"}}),Ao=new b({props:{name:"class transformers.FlaxMinLengthLogitsProcessor",anchor:"transformers.FlaxMinLengthLogitsProcessor",parameters:[{name:"min_length",val:": int"},{name:"eos_token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L243",parametersDescription:[{anchor:"transformers.FlaxMinLengthLogitsProcessor.min_length",description:`<strong>min_length</strong> (<code>int</code>) —
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float("Inf")</code>.`,name:"min_length"},{anchor:"transformers.FlaxMinLengthLogitsProcessor.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}]}}),Co=new b({props:{name:"__call__",anchor:"transformers.FlaxMinLengthLogitsProcessor.__call__",parameters:[{name:"input_ids",val:": ndarray"},{name:"scores",val:": ndarray"},{name:"cur_len",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L264"}}),No=new qe({}),Wo=new b({props:{name:"class transformers.StoppingCriteria",anchor:"transformers.StoppingCriteria",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L33"}}),Vo=new b({props:{name:"__call__",anchor:"transformers.StoppingCriteria.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L36",parametersDescription:[{anchor:"transformers.StoppingCriteria.__call__.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.StoppingCriteria.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.`,name:"scores"}],returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),Mo=new b({props:{name:"class transformers.StoppingCriteriaList",anchor:"transformers.StoppingCriteriaList",parameters:[{name:"iterable",val:" = ()"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L110"}}),Go=new b({props:{name:"__call__",anchor:"transformers.StoppingCriteriaList.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L111",parametersDescription:[{anchor:"transformers.StoppingCriteriaList.__call__.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.StoppingCriteriaList.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.`,name:"scores"}],returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),jo=new b({props:{name:"class transformers.MaxLengthCriteria",anchor:"transformers.MaxLengthCriteria",parameters:[{name:"max_length",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L41",parametersDescription:[{anchor:"transformers.MaxLengthCriteria.max_length",description:`<strong>max_length</strong> (<code>int</code>) —
The maximum length that the output sequence can have in number of tokens.`,name:"max_length"}]}}),Ro=new b({props:{name:"__call__",anchor:"transformers.MaxLengthCriteria.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L54",parametersDescription:[{anchor:"transformers.MaxLengthCriteria.__call__.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.MaxLengthCriteria.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.`,name:"scores"}],returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),Ko=new b({props:{name:"class transformers.MaxTimeCriteria",anchor:"transformers.MaxTimeCriteria",parameters:[{name:"max_time",val:": float"},{name:"initial_timestamp",val:": typing.Optional[float] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L88",parametersDescription:[{anchor:"transformers.MaxTimeCriteria.max_time",description:`<strong>max_time</strong> (<code>float</code>) —
The maximum allowed time in seconds for the generation.`,name:"max_time"},{anchor:"transformers.MaxTimeCriteria.initial_time",description:`<strong>initial_time</strong> (<code>float</code>, <em>optional</em>, defaults to <code>time.time()</code>) —
The start of the generation allowed time.`,name:"initial_time"}]}}),Yo=new b({props:{name:"__call__",anchor:"transformers.MaxTimeCriteria.__call__",parameters:[{name:"input_ids",val:": LongTensor"},{name:"scores",val:": FloatTensor"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L105",parametersDescription:[{anchor:"transformers.MaxTimeCriteria.__call__.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.MaxTimeCriteria.__call__.scores",description:`<strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.`,name:"scores"}],returnDescription:`
<p><code>bool</code>. <code>False</code> indicates we should continue, <code>True</code> indicates we should stop.</p>
`}}),Xo=new qe({}),Jo=new b({props:{name:"class transformers.Constraint",anchor:"transformers.Constraint",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L5"}}),Qo=new m$({props:{code:`completed = False
while not completed:
_, completed = constraint.update(constraint.advance())`,highlighted:`completed = <span class="hljs-literal">False</span>
<span class="hljs-keyword">while</span> <span class="hljs-keyword">not</span> completed:
_, completed = constraint.update(constraint.advance())`}}),Zo=new b({props:{name:"advance",anchor:"transformers.Constraint.advance",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L48",returnDescription:`
<p>Must be a tensor of a list of indexable tokens, not some integer.</p>
`,returnType:`
<p>token_ids(<code>torch.tensor</code>)</p>
`}}),en=new b({props:{name:"copy",anchor:"transformers.Constraint.copy",parameters:[{name:"stateful",val:" = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L113",returnDescription:`
<p>The same constraint as the one being called from.</p>
`,returnType:`
<p>constraint(<code>Constraint</code>)</p>
`}}),tn=new b({props:{name:"does_advance",anchor:"transformers.Constraint.does_advance",parameters:[{name:"token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L60"}}),rn=new b({props:{name:"remaining",anchor:"transformers.Constraint.remaining",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L104"}}),nn=new b({props:{name:"reset",anchor:"transformers.Constraint.reset",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L94"}}),sn=new b({props:{name:"test",anchor:"transformers.Constraint.test",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L24"}}),an=new b({props:{name:"update",anchor:"transformers.Constraint.update",parameters:[{name:"token_id",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L69",returnDescription:`
<p>Whether this constraint has become one step closer to being fulfuilled.
completed(<code>bool</code>):
Whether this constraint has been completely fulfilled by this token being generated.
reset (<code>bool</code>):
Whether this constraint has reset its progress by this token being generated.</p>
`,returnType:`
<p>stepped(<code>bool</code>)</p>
`}}),ln=new b({props:{name:"class transformers.PhrasalConstraint",anchor:"transformers.PhrasalConstraint",parameters:[{name:"token_ids",val:": typing.List[int]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L129",parametersDescription:[{anchor:"transformers.PhrasalConstraint.token_ids",description:`<strong>token_ids</strong> (<code>List[int]</code>) —
The id of the token that must be generated by the output.`,name:"token_ids"}]}}),dn=new b({props:{name:"class transformers.DisjunctiveConstraint",anchor:"transformers.DisjunctiveConstraint",parameters:[{name:"nested_token_ids",val:": typing.List[typing.List[int]]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L260",parametersDescription:[{anchor:"transformers.DisjunctiveConstraint.nested_token_ids",description:"<strong>nested_token_ids</strong> (<code>List[List[int]]</code>) — a list of words, where each word is a list of ids. This constraint",name:"nested_token_ids"},{anchor:"transformers.DisjunctiveConstraint.is",description:"<strong>is</strong> fulfilled by generating just one from the list of words. —",name:"is"}]}}),fn=new b({props:{name:"class transformers.ConstraintListState",anchor:"transformers.ConstraintListState",parameters:[{name:"constraints",val:": typing.List[transformers.generation_beam_constraints.Constraint]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L349",parametersDescription:[{anchor:"transformers.ConstraintListState.constraints",description:`<strong>constraints</strong> (<code>List[Constraint]</code>) —
A list of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.Constraint">Constraint</a> objects that must be fulfilled by the beam scorer.`,name:"constraints"}]}}),mn=new b({props:{name:"advance",anchor:"transformers.ConstraintListState.advance",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L381"}}),hn=new b({props:{name:"reset",anchor:"transformers.ConstraintListState.reset",parameters:[{name:"token_ids",val:": typing.Optional[typing.List[int]]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L416"}}),gn=new qe({}),un=new b({props:{name:"class transformers.BeamScorer",anchor:"transformers.BeamScorer",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L88"}}),_n=new b({props:{name:"process",anchor:"transformers.BeamScorer.process",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L94",parametersDescription:[{anchor:"transformers.BeamScorer.process.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BeamScorer.process.next_scores",description:`<strong>next_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Current scores of the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_scores"},{anchor:"transformers.BeamScorer.process.next_tokens",description:`<strong>next_tokens</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
<code>input_ids</code> of the tokens corresponding to the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_tokens"},{anchor:"transformers.BeamScorer.process.next_indices",description:`<strong>next_indices</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Beam indices indicating to which beam hypothesis the <code>next_tokens</code> correspond.`,name:"next_indices"},{anchor:"transformers.BeamScorer.process.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.BeamScorer.process.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],returnDescription:`
<p>A dictionary composed of the fields as defined above:</p>
<ul>
<li><strong>next_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Updated scores of all
non-finished beams.</li>
<li><strong>next_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Next tokens to be added
to the non-finished beam_hypotheses.</li>
<li><strong>next_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Beam indices
indicating to which beam the next tokens shall be added.</li>
</ul>
`,returnType:`
<p><code>UserDict</code></p>
`}}),vn=new b({props:{name:"finalize",anchor:"transformers.BeamScorer.finalize",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"max_length",val:": int"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L106",parametersDescription:[{anchor:"transformers.BeamScorer.finalize.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.BeamScorer.finalize.final_beam_scores",description:`<strong>final_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) —
The final scores of all non-finished beams.`,name:"final_beam_scores"},{anchor:"transformers.BeamScorer.finalize.final_beam_tokens",description:`<strong>final_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) —
The last tokens to be added to the non-finished beam_hypotheses.`,name:"final_beam_tokens"},{anchor:"transformers.BeamScorer.finalize.final_beam_indices",description:`<strong>final_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) —
The beam indices indicating to which beam the <code>final_beam_tokens</code> shall be added.`,name:"final_beam_indices"},{anchor:"transformers.BeamScorer.finalize.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.BeamScorer.finalize.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],returnDescription:`
<p>The generated sequences.
The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter if all batches finished early
due to the <code>eos_token_id</code>.</p>
`,returnType:`
<p><code>torch.LongTensor</code> of shape <code>(batch_size * num_return_sequences, sequence_length)</code></p>
`}}),bn=new b({props:{name:"class transformers.BeamSearchScorer",anchor:"transformers.BeamSearchScorer",parameters:[{name:"batch_size",val:": int"},{name:"num_beams",val:": int"},{name:"device",val:": device"},{name:"length_penalty",val:": typing.Optional[float] = 1.0"},{name:"do_early_stopping",val:": typing.Optional[bool] = False"},{name:"num_beam_hyps_to_keep",val:": typing.Optional[int] = 1"},{name:"num_beam_groups",val:": typing.Optional[int] = 1"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L120",parametersDescription:[{anchor:"transformers.BeamSearchScorer.batch_size",description:`<strong>batch_size</strong> (<code>int</code>) —
Batch Size of <code>input_ids</code> for which standard beam search decoding is run in parallel.`,name:"batch_size"},{anchor:"transformers.BeamSearchScorer.max_length",description:`<strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.BeamSearchScorer.num_beams",description:`<strong>num_beams</strong> (<code>int</code>) —
Number of beams for beam search.`,name:"num_beams"},{anchor:"transformers.BeamSearchScorer.device",description:`<strong>device</strong> (<code>torch.device</code>) —
Defines the device type (<em>e.g.</em>, <code>"cpu"</code> or <code>"cuda"</code>) on which this instance of <code>BeamSearchScorer</code> will be
allocated.`,name:"device"},{anchor:"transformers.BeamSearchScorer.length_penalty",description:`<strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the
model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer
sequences.`,name:"length_penalty"},{anchor:"transformers.BeamSearchScorer.do_early_stopping",description:`<strong>do_early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.`,name:"do_early_stopping"},{anchor:"transformers.BeamSearchScorer.num_beam_hyps_to_keep",description:`<strong>num_beam_hyps_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The number of beam hypotheses that shall be returned upon calling
<code>finalize</code>.`,name:"num_beam_hyps_to_keep"},{anchor:"transformers.BeamSearchScorer.num_beam_groups",description:`<strong>num_beam_groups</strong> (<code>int</code>) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.`,name:"num_beam_groups"}]}}),kn=new b({props:{name:"process",anchor:"transformers.BeamSearchScorer.process",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L206"}}),wn=new b({props:{name:"finalize",anchor:"transformers.BeamSearchScorer.finalize",parameters:[{name:"input_ids",val:": LongTensor"},{name:"final_beam_scores",val:": FloatTensor"},{name:"final_beam_tokens",val:": LongTensor"},{name:"final_beam_indices",val:": LongTensor"},{name:"max_length",val:": int"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L291"}}),Ln=new b({props:{name:"class transformers.ConstrainedBeamSearchScorer",anchor:"transformers.ConstrainedBeamSearchScorer",parameters:[{name:"batch_size",val:": int"},{name:"num_beams",val:": int"},{name:"constraints",val:": typing.List[transformers.generation_beam_constraints.Constraint]"},{name:"device",val:": device"},{name:"length_penalty",val:": typing.Optional[float] = 1.0"},{name:"do_early_stopping",val:": typing.Optional[bool] = False"},{name:"num_beam_hyps_to_keep",val:": typing.Optional[int] = 1"},{name:"num_beam_groups",val:": typing.Optional[int] = 1"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L356",parametersDescription:[{anchor:"transformers.ConstrainedBeamSearchScorer.batch_size",description:`<strong>batch_size</strong> (<code>int</code>) —
Batch Size of <code>input_ids</code> for which standard beam search decoding is run in parallel.`,name:"batch_size"},{anchor:"transformers.ConstrainedBeamSearchScorer.max_length",description:`<strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.`,name:"max_length"},{anchor:"transformers.ConstrainedBeamSearchScorer.num_beams",description:`<strong>num_beams</strong> (<code>int</code>) —
Number of beams for beam search.`,name:"num_beams"},{anchor:"transformers.ConstrainedBeamSearchScorer.constraints",description:`<strong>constraints</strong> (<code>List[Constraint]</code>) —
A list of positive constraints represented as <code>Constraint</code> objects that must be fulfilled in the generation
output. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.Constraint">Constraint</a> should be read.`,name:"constraints"},{anchor:"transformers.ConstrainedBeamSearchScorer.device",description:`<strong>device</strong> (<code>torch.device</code>) —
Defines the device type (<em>e.g.</em>, <code>"cpu"</code> or <code>"cuda"</code>) on which this instance of <code>BeamSearchScorer</code> will be
allocated.`,name:"device"},{anchor:"transformers.ConstrainedBeamSearchScorer.length_penalty",description:`<strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the
model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer
sequences.`,name:"length_penalty"},{anchor:"transformers.ConstrainedBeamSearchScorer.do_early_stopping",description:`<strong>do_early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.`,name:"do_early_stopping"},{anchor:"transformers.ConstrainedBeamSearchScorer.num_beam_hyps_to_keep",description:`<strong>num_beam_hyps_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The number of beam hypotheses that shall be returned upon calling
<code>finalize</code>.`,name:"num_beam_hyps_to_keep"},{anchor:"transformers.ConstrainedBeamSearchScorer.num_beam_groups",description:`<strong>num_beam_groups</strong> (<code>int</code>) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.`,name:"num_beam_groups"}]}}),xn=new b({props:{name:"process",anchor:"transformers.ConstrainedBeamSearchScorer.process",parameters:[{name:"input_ids",val:": LongTensor"},{name:"next_scores",val:": FloatTensor"},{name:"next_tokens",val:": LongTensor"},{name:"next_indices",val:": LongTensor"},{name:"scores_for_all_vocab",val:": FloatTensor"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L450",parametersDescription:[{anchor:"transformers.ConstrainedBeamSearchScorer.process.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.next_scores",description:`<strong>next_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Current scores of the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_scores"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.next_tokens",description:`<strong>next_tokens</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
<code>input_ids</code> of the tokens corresponding to the top <code>2 * num_beams</code> non-finished beam hypotheses.`,name:"next_tokens"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.next_indices",description:`<strong>next_indices</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Beam indices indicating to which beam hypothesis the <code>next_tokens</code> correspond.`,name:"next_indices"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.scores_for_all_vocab",description:`<strong>scores_for_all_vocab</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
The scores of all tokens in the vocabulary for each of the beam hypotheses.`,name:"scores_for_all_vocab"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.`,name:"pad_token_id"},{anchor:"transformers.ConstrainedBeamSearchScorer.process.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.`,name:"eos_token_id"}],returnDescription:`
<p>A dictionary composed of the fields as defined above:</p>
<ul>
<li>
<p><strong>next_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Updated scores of
all
non-finished beams.</p>
</li>
<li>
<p><strong>next_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Next tokens to be
added
to the non-finished beam_hypotheses.</p>
</li>
<li>
<p><strong>next_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) \u2014 Beam indices
indicating to which beam the next tokens shall be added.</p>
</li>
</ul>
`,returnType:`
<p><code>UserDict</code></p>
`}}),En=new b({props:{name:"finalize",anchor:"transformers.ConstrainedBeamSearchScorer.finalize",parameters:[{name:"input_ids",val:": LongTensor"},{name:"final_beam_scores",val:": FloatTensor"},{name:"final_beam_tokens",val:": LongTensor"},{name:"final_beam_indices",val:": LongTensor"},{name:"max_length",val:": int"},{name:"pad_token_id",val:": typing.Optional[int] = None"},{name:"eos_token_id",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L725"}}),Pn=new qe({}),Fn=new b({props:{name:"transformers.top_k_top_p_filtering",anchor:"transformers.top_k_top_p_filtering",parameters:[{name:"logits",val:": FloatTensor"},{name:"top_k",val:": int = 0"},{name:"top_p",val:": float = 1.0"},{name:"filter_value",val:": float = -inf"},{name:"min_tokens_to_keep",val:": int = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L3290",parametersDescription:[{anchor:"transformers.top_k_top_p_filtering.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If > 0, only keep the top k tokens with highest probability (top-k filtering)`,name:"top_k"},{anchor:"transformers.top_k_top_p_filtering.top_p",description:`<strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (<a href="http://arxiv.org/abs/1904.09751" rel="nofollow">http://arxiv.org/abs/1904.09751</a>)`,name:"top_p"},{anchor:"transformers.top_k_top_p_filtering.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimumber of tokens we keep per batch example in the output.`,name:"min_tokens_to_keep"}]}}),zn=new b({props:{name:"transformers.tf_top_k_top_p_filtering",anchor:"transformers.tf_top_k_top_p_filtering",parameters:[{name:"logits",val:""},{name:"top_k",val:" = 0"},{name:"top_p",val:" = 1.0"},{name:"filter_value",val:" = -inf"},{name:"min_tokens_to_keep",val:" = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_utils.py#L2270",parametersDescription:[{anchor:"transformers.tf_top_k_top_p_filtering.top_k",description:`<strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If > 0, only keep the top k tokens with highest probability (top-k filtering)`,name:"top_k"},{anchor:"transformers.tf_top_k_top_p_filtering.top_p",description:`<strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (<a href="http://arxiv.org/abs/1904.09751" rel="nofollow">http://arxiv.org/abs/1904.09751</a>)`,name:"top_p"},{anchor:"transformers.tf_top_k_top_p_filtering.min_tokens_to_keep",description:`<strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimumber of tokens we keep per batch example in the output.`,name:"min_tokens_to_keep"}]}}),{c(){Be=o("meta"),qn=c(),N=o("h1"),H=o("a"),Gi=o("span"),m(Zt.$$.fragment),kf=c(),ji=o("span"),wf=a("Utilities for Generation"),Jl=c(),y=o("p"),Lf=a("This page lists all the utility functions used by "),Bn=o("a"),xf=a("generate()"),Ef=a(`,
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`),Cn=o("a"),Sf=a("beam_search()"),Of=a(`,
`),Nn=o("a"),qf=a("beam_sample()"),Bf=a(`,
`),Wn=o("a"),If=a("group_beam_search()"),Af=a(`, and
`),Vn=o("a"),Cf=a("constrained_beam_search()"),Nf=a("."),Ql=c(),Mn=o("p"),Wf=a("Most of those are only useful if you are studying the code of the generate methods in the library."),Zl=c(),Ge=o("h2"),mt=o("a"),Hi=o("span"),m(er.$$.fragment),Vf=c(),Ri=o("span"),Mf=a("Generate Outputs"),ed=c(),W=o("p"),Gf=a("The output of "),Gn=o("a"),jf=a("generate()"),Hf=a(` is an instance of a subclass of
`),jn=o("a"),Rf=a("ModelOutput"),Kf=a(`. This output is a data structure containing all the information returned
by `),Hn=o("a"),Uf=a("generate()"),Yf=a(", but that can also be used as tuple or dictionary."),td=c(),Rn=o("p"),Xf=a("Here\u2019s an example:"),rd=c(),m(tr.$$.fragment),od=c(),Ie=o("p"),Jf=a("The "),Ki=o("code"),Qf=a("generation_output"),Zf=a(" object is a "),Kn=o("a"),em=a("GreedySearchDecoderOnlyOutput"),tm=a(`, as we can
see in the documentation of that class below, it means it has the following attributes:`),nd=c(),V=o("ul"),Un=o("li"),Ui=o("code"),rm=a("sequences"),om=a(": the generated sequences of tokens"),nm=c(),Yn=o("li"),Yi=o("code"),sm=a("scores"),am=a(" (optional): the prediction scores of the language modelling head, for each generation step"),im=c(),Xn=o("li"),Xi=o("code"),cm=a("hidden_states"),lm=a(" (optional): the hidden states of the model, for each generation step"),dm=c(),Jn=o("li"),Ji=o("code"),pm=a("attentions"),fm=a(" (optional): the attention weights of the model, for each generation step"),sd=c(),k=o("p"),mm=a("Here we have the "),Qi=o("code"),hm=a("scores"),gm=a(" since we passed along "),Zi=o("code"),um=a("output_scores=True"),_m=a(", but we don\u2019t have "),ec=o("code"),vm=a("hidden_states"),bm=a(` and
`),tc=o("code"),$m=a("attentions"),Tm=a(" because we didn\u2019t pass "),rc=o("code"),ym=a("output_hidden_states=True"),km=a(" or "),oc=o("code"),wm=a("output_attentions=True"),Lm=a("."),ad=c(),F=o("p"),xm=a(`You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get `),nc=o("code"),Em=a("None"),Pm=a(". Here for instance "),sc=o("code"),Fm=a("generation_output.scores"),Dm=a(` are all the generated prediction scores of the
language modeling head, and `),ac=o("code"),zm=a("generation_output.attentions"),Sm=a(" is "),ic=o("code"),Om=a("None"),qm=a("."),id=c(),D=o("p"),Bm=a("When using our "),cc=o("code"),Im=a("generation_output"),Am=a(" object as a tuple, it only keeps the attributes that don\u2019t have "),lc=o("code"),Cm=a("None"),Nm=a(` values.
Here, for instance, it has two elements, `),dc=o("code"),Wm=a("loss"),Vm=a(" then "),pc=o("code"),Mm=a("logits"),Gm=a(", so"),cd=c(),m(rr.$$.fragment),ld=c(),ht=o("p"),jm=a("will return the tuple "),fc=o("code"),Hm=a("(generation_output.sequences, generation_output.scores)"),Rm=a(" for instance."),dd=c(),z=o("p"),Km=a("When using our "),mc=o("code"),Um=a("generation_output"),Ym=a(" object as a dictionary, it only keeps the attributes that don\u2019t have "),hc=o("code"),Xm=a("None"),Jm=a(`
values. Here, for instance, it has two keys that are `),gc=o("code"),Qm=a("sequences"),Zm=a(" and "),uc=o("code"),eh=a("scores"),th=a("."),pd=c(),Qn=o("p"),rh=a("We document here all output types."),fd=c(),je=o("h3"),gt=o("a"),_c=o("span"),m(or.$$.fragment),oh=c(),vc=o("span"),nh=a("GreedySearchOutput"),md=c(),He=o("div"),m(nr.$$.fragment),sh=c(),bc=o("p"),ah=a("Base class for outputs of decoder-only generation models using greedy search."),hd=c(),Re=o("div"),m(sr.$$.fragment),ih=c(),$c=o("p"),ch=a(`Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),gd=c(),R=o("div"),m(ar.$$.fragment),lh=c(),Tc=o("p"),dh=a("Flax Base class for outputs of decoder-only generation models using greedy search."),ph=c(),ut=o("div"),m(ir.$$.fragment),fh=c(),yc=o("p"),mh=a("\u201CReturns a new object replacing the specified fields with new values."),ud=c(),Ke=o("h3"),_t=o("a"),kc=o("span"),m(cr.$$.fragment),hh=c(),wc=o("span"),gh=a("SampleOutput"),_d=c(),Ue=o("div"),m(lr.$$.fragment),uh=c(),Lc=o("p"),_h=a("Base class for outputs of decoder-only generation models using sampling."),vd=c(),Ye=o("div"),m(dr.$$.fragment),vh=c(),xc=o("p"),bh=a(`Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),bd=c(),K=o("div"),m(pr.$$.fragment),$h=c(),Ec=o("p"),Th=a("Flax Base class for outputs of decoder-only generation models using sampling."),yh=c(),vt=o("div"),m(fr.$$.fragment),kh=c(),Pc=o("p"),wh=a("\u201CReturns a new object replacing the specified fields with new values."),$d=c(),Xe=o("h3"),bt=o("a"),Fc=o("span"),m(mr.$$.fragment),Lh=c(),Dc=o("span"),xh=a("BeamSearchOutput"),Td=c(),Je=o("div"),m(hr.$$.fragment),Eh=c(),zc=o("p"),Ph=a("Base class for outputs of decoder-only generation models using beam search."),yd=c(),Qe=o("div"),m(gr.$$.fragment),Fh=c(),Sc=o("p"),Dh=a(`Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),kd=c(),Ze=o("h3"),$t=o("a"),Oc=o("span"),m(ur.$$.fragment),zh=c(),qc=o("span"),Sh=a("BeamSampleOutput"),wd=c(),et=o("div"),m(_r.$$.fragment),Oh=c(),Bc=o("p"),qh=a("Base class for outputs of decoder-only generation models using beam sample."),Ld=c(),tt=o("div"),m(vr.$$.fragment),Bh=c(),Ic=o("p"),Ih=a(`Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)`),xd=c(),rt=o("h2"),Tt=o("a"),Ac=o("span"),m(br.$$.fragment),Ah=c(),Cc=o("span"),Ch=a("LogitsProcessor"),Ed=c(),yt=o("p"),Nh=a("A "),Zn=o("a"),Wh=a("LogitsProcessor"),Vh=a(` can be used to modify the prediction scores of a language model head for
generation.`),Pd=c(),U=o("div"),m($r.$$.fragment),Mh=c(),Nc=o("p"),Gh=a("Abstract base class for all logit processors that can be applied during generation."),jh=c(),kt=o("div"),m(Tr.$$.fragment),Hh=c(),Wc=o("p"),Rh=a("Torch method for processing logits."),Fd=c(),Y=o("div"),m(yr.$$.fragment),Kh=c(),w=o("p"),Uh=a("This class can be used to create a list of "),es=o("a"),Yh=a("LogitsProcessor"),Xh=a(" or "),ts=o("a"),Jh=a("LogitsWarper"),Qh=a(` to subsequently process a
`),Vc=o("code"),Zh=a("scores"),eg=a(" input tensor. This class inherits from list and adds a specific "),Mc=o("em"),Gc=o("strong"),tg=a("call"),rg=a(` method to apply each
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`),Ar=o("a"),Ig=a("Fairseq"),Ag=a("."),Cg=c(),Ts=o("div"),m(Cr.$$.fragment),Ad=c(),oe=o("div"),m(Nr.$$.fragment),Ng=c(),ys=o("p"),ks=o("a"),Wg=a("LogitsProcessor"),Vg=a(" that enforces that specified sequences will never be sampled."),Mg=c(),ws=o("div"),m(Wr.$$.fragment),Cd=c(),ne=o("div"),m(Vr.$$.fragment),Gg=c(),xt=o("p"),Ls=o("a"),jg=a("LogitsProcessor"),Hg=a(` that enforces constrained generation and is useful for prefix-conditioned constrained
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`),Ps=o("a"),Qg=a("PreTrainedModel.group_beam_search()"),Zg=a(". See "),Hr=o("a"),eu=a(`Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models`),tu=a(" for more details."),ru=c(),Fs=o("div"),m(Rr.$$.fragment),Wd=c(),ae=o("div"),m(Kr.$$.fragment),ou=c(),Ds=o("p"),zs=o("a"),nu=a("LogitsProcessor"),su=a(" that enforces the specified token as the first generated token."),au=c(),Ss=o("div"),m(Ur.$$.fragment),Vd=c(),ie=o("div"),m(Yr.$$.fragment),iu=c(),Et=o("p"),Os=o("a"),cu=a("LogitsProcessor"),lu=a(" that enforces the specified token as the last generated token when "),Rc=o("code"),du=a("max_length"),pu=a(" is reached."),fu=c(),qs=o("div"),m(Xr.$$.fragment),Md=c(),ce=o("div"),m(Jr.$$.fragment),mu=c(),M=o("p"),Bs=o("a"),hu=a("LogitsProcessor"),gu=a(" that removes all "),Kc=o("code"),uu=a("nan"),_u=a(" and "),Uc=o("code"),vu=a("inf"),bu=a(` values to avoid the generation method to fail. Note that using
the logits processor should only be used if necessary since it can slow down the generation method. `),Yc=o("code"),$u=a("max_length"),Tu=a(` is
reached.`),yu=c(),Is=o("div"),m(Qr.$$.fragment),Gd=c(),le=o("div"),m(Zr.$$.fragment),ku=c(),Xc=o("p"),wu=a("Abstract base class for all logit processors that can be applied during generation."),Lu=c(),Pt=o("div"),m(eo.$$.fragment),xu=c(),Jc=o("p"),Eu=a("TF method for processing logits."),jd=c(),de=o("div"),m(to.$$.fragment),Pu=c(),B=o("p"),Fu=a("This class can be used to create a list of "),As=o("a"),Du=a("TFLogitsProcessor"),zu=a(" to subsequently process a "),Qc=o("code"),Su=a("scores"),Ou=a(` input tensor.
This class inherits from list and adds a specific `),Zc=o("em"),el=o("strong"),qu=a("call"),Bu=a(" method to apply each "),Cs=o("a"),Iu=a("TFLogitsProcessor"),Au=a(` to the
inputs.`),Cu=c(),Ns=o("div"),m(ro.$$.fragment),Hd=c(),pe=o("div"),m(oo.$$.fragment),Nu=c(),tl=o("p"),Wu=a("Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."),Vu=c(),Ft=o("div"),m(no.$$.fragment),Mu=c(),rl=o("p"),Gu=a("TF method for warping logits."),Rd=c(),fe=o("div"),m(so.$$.fragment),ju=c(),Ws=o("p"),Vs=o("a"),Hu=a("TFLogitsWarper"),Ru=a(" for temperature (exponential scaling output probability distribution)."),Ku=c(),Ms=o("div"),m(ao.$$.fragment),Kd=c(),me=o("div"),m(io.$$.fragment),Uu=c(),Gs=o("p"),js=o("a"),Yu=a("TFLogitsWarper"),Xu=a(" that performs top-p, i.e. restricting to top tokens summing to <= prob_cut_off."),Ju=c(),Hs=o("div"),m(co.$$.fragment),Ud=c(),he=o("div"),m(lo.$$.fragment),Qu=c(),Rs=o("p"),Ks=o("a"),Zu=a("TFLogitsWarper"),e_=a(" that performs top-k, i.e. restricting to the k highest probability elements."),t_=c(),Us=o("div"),m(po.$$.fragment),Yd=c(),ge=o("div"),m(fo.$$.fragment),r_=c(),Ys=o("p"),Xs=o("a"),o_=a("TFLogitsProcessor"),n_=a(" enforcing a min-length by setting EOS probability to 0."),s_=c(),Js=o("div"),m(mo.$$.fragment),Xd=c(),ue=o("div"),m(ho.$$.fragment),a_=c(),Qs=o("p"),Zs=o("a"),i_=a("TFLogitsProcessor"),c_=a(" that enforces that specified sequences will never be sampled."),l_=c(),ea=o("div"),m(go.$$.fragment),Jd=c(),_e=o("div"),m(uo.$$.fragment),d_=c(),Dt=o("p"),ta=o("a"),p_=a("TFLogitsProcessor"),f_=a(` that enforces no repetition of n-grams. See
`),_o=o("a"),m_=a("Fairseq"),h_=a("."),g_=c(),ra=o("div"),m(vo.$$.fragment),Qd=c(),ve=o("div"),m(bo.$$.fragment),u_=c(),oa=o("p"),na=o("a"),__=a("TFLogitsProcessor"),v_=a(" enforcing an exponential penalty on repeated sequences."),b_=c(),sa=o("div"),m($o.$$.fragment),Zd=c(),be=o("div"),m(To.$$.fragment),$_=c(),ol=o("p"),T_=a("Abstract base class for all logit processors that can be applied during generation."),y_=c(),zt=o("div"),m(yo.$$.fragment),k_=c(),nl=o("p"),w_=a("Flax method for processing logits."),ep=c(),$e=o("div"),m(ko.$$.fragment),L_=c(),L=o("p"),x_=a("This class can be used to create a list of "),aa=o("a"),E_=a("FlaxLogitsProcessor"),P_=a(" or "),ia=o("a"),F_=a("FlaxLogitsWarper"),D_=a(` to subsequently process
a `),sl=o("code"),z_=a("scores"),S_=a(" input tensor. This class inherits from list and adds a specific "),al=o("em"),il=o("strong"),O_=a("call"),q_=a(` method to apply each
`),ca=o("a"),B_=a("FlaxLogitsProcessor"),I_=a(" or "),la=o("a"),A_=a("FlaxLogitsWarper"),C_=a(" to the inputs."),N_=c(),da=o("div"),m(wo.$$.fragment),tp=c(),Te=o("div"),m(Lo.$$.fragment),W_=c(),cl=o("p"),V_=a("Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."),M_=c(),St=o("div"),m(xo.$$.fragment),G_=c(),ll=o("p"),j_=a("Flax method for warping logits."),rp=c(),ye=o("div"),m(Eo.$$.fragment),H_=c(),pa=o("p"),fa=o("a"),R_=a("FlaxLogitsWarper"),K_=a(" for temperature (exponential scaling output probability distribution)."),U_=c(),ma=o("div"),m(Po.$$.fragment),op=c(),ke=o("div"),m(Fo.$$.fragment),Y_=c(),ha=o("p"),ga=o("a"),X_=a("FlaxLogitsWarper"),J_=a(" that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off."),Q_=c(),ua=o("div"),m(Do.$$.fragment),np=c(),we=o("div"),m(zo.$$.fragment),Z_=c(),_a=o("p"),va=o("a"),ev=a("FlaxLogitsWarper"),tv=a(" that performs top-k, i.e. restricting to the k highest probability elements."),rv=c(),ba=o("div"),m(So.$$.fragment),sp=c(),Le=o("div"),m(Oo.$$.fragment),ov=c(),$a=o("p"),Ta=o("a"),nv=a("FlaxLogitsProcessor"),sv=a(" that enforces the specified token as the first generated token."),av=c(),ya=o("div"),m(qo.$$.fragment),ap=c(),xe=o("div"),m(Bo.$$.fragment),iv=c(),Ot=o("p"),ka=o("a"),cv=a("FlaxLogitsProcessor"),lv=a(" that enforces the specified token as the last generated token when "),dl=o("code"),dv=a("max_length"),pv=a(" is reached."),fv=c(),wa=o("div"),m(Io.$$.fragment),ip=c(),Ee=o("div"),m(Ao.$$.fragment),mv=c(),La=o("p"),xa=o("a"),hv=a("FlaxLogitsProcessor"),gv=a(" enforcing a min-length by setting EOS probability to 0."),uv=c(),Ea=o("div"),m(Co.$$.fragment),cp=c(),ot=o("h2"),qt=o("a"),pl=o("span"),m(No.$$.fragment),_v=c(),fl=o("span"),vv=a("StoppingCriteria"),lp=c(),Bt=o("p"),bv=a("A "),Pa=o("a"),$v=a("StoppingCriteria"),Tv=a(" can be used to change when to stop generation (other than EOS token)."),dp=c(),Pe=o("div"),m(Wo.$$.fragment),yv=c(),ml=o("p"),kv=a("Abstract base class for all stopping criteria that can be applied during generation."),wv=c(),Fa=o("div"),m(Vo.$$.fragment),pp=c(),nt=o("div"),m(Mo.$$.fragment),Lv=c(),Da=o("div"),m(Go.$$.fragment),fp=c(),Fe=o("div"),m(jo.$$.fragment),xv=c(),Ho=o("p"),Ev=a("This class can be used to stop generation whenever the full generated number of tokens exceeds "),hl=o("code"),Pv=a("max_length"),Fv=a(`. Keep
in mind for decoder-only type of transformers, this will include the initial prompted tokens.`),Dv=c(),za=o("div"),m(Ro.$$.fragment),mp=c(),De=o("div"),m(Ko.$$.fragment),zv=c(),Uo=o("p"),Sv=a(`This class can be used to stop generation whenever the full generation exceeds some amount of time. By default, the
time will start being counted when you initialize this function. You can override this by passing an
`),gl=o("code"),Ov=a("initial_time"),qv=a("."),Bv=c(),Sa=o("div"),m(Yo.$$.fragment),hp=c(),st=o("h2"),It=o("a"),ul=o("span"),m(Xo.$$.fragment),Iv=c(),_l=o("span"),Av=a("Constraints"),gp=c(),At=o("p"),Cv=a("A "),Oa=o("a"),Nv=a("Constraint"),Wv=a(" can be used to force the generation to include specific tokens or sequences in the output."),up=c(),$=o("div"),m(Jo.$$.fragment),Vv=c(),vl=o("p"),Mv=a(`Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.`),Gv=c(),bl=o("p"),jv=a("All classes that inherit Constraint must follow the requirement that"),Hv=c(),m(Qo.$$.fragment),Rv=c(),$l=o("p"),Kv=a("will always terminate (halt)."),Uv=c(),Ct=o("div"),m(Zo.$$.fragment),Yv=c(),Tl=o("p"),Xv=a("When called, returns the token that would take this constraint one step closer to being fulfilled."),Jv=c(),Nt=o("div"),m(en.$$.fragment),Qv=c(),yl=o("p"),Zv=a("Creates a new instance of this constraint."),eb=c(),Wt=o("div"),m(tn.$$.fragment),tb=c(),kl=o("p"),rb=a("Reads in a token and returns whether it creates progress."),ob=c(),Vt=o("div"),m(rn.$$.fragment),nb=c(),on=o("p"),sb=a("Returns the number of remaining steps of "),wl=o("code"),ab=a("advance()"),ib=a(" in order to complete this constraint."),cb=c(),Mt=o("div"),m(nn.$$.fragment),lb=c(),Ll=o("p"),db=a(`Resets the state of this constraint to its initialization. We would call this in cases where the fulfillment of
a constraint is abrupted by an unwanted token.`),pb=c(),Gt=o("div"),m(sn.$$.fragment),fb=c(),xl=o("p"),mb=a("Tests whether this constraint has been properly defined."),hb=c(),Ce=o("div"),m(an.$$.fragment),gb=c(),cn=o("p"),ub=a(`Reads in a token and returns booleans that indicate the progress made by it. This function will update the
state of this object unlikes `),El=o("code"),_b=a("does_advance(self, token_id: int)"),vb=a("."),bb=c(),Pl=o("p"),$b=a(`This isn\u2019t to test whether a certain token will advance the progress; it\u2019s to update its state as if it has
been generated. This becomes important if token_id != desired token (refer to else statement in
PhrasalConstraint)`),_p=c(),at=o("div"),m(ln.$$.fragment),Tb=c(),qa=o("p"),Ba=o("a"),yb=a("Constraint"),kb=a(" enforcing that an ordered sequence of tokens is included in the output."),vp=c(),it=o("div"),m(dn.$$.fragment),wb=c(),pn=o("p"),Lb=a("A special "),Ia=o("a"),xb=a("Constraint"),Eb=a(" that is fulfilled by fulfilling just one of several constraints."),bp=c(),I=o("div"),m(fn.$$.fragment),Pb=c(),Fl=o("p"),Fb=a("A class for beam scorers to track its progress through a list of constraints."),Db=c(),E=o("div"),m(mn.$$.fragment),zb=c(),Dl=o("p"),Sb=a(`The list of tokens to generate such that we can make progress.
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specific constraint `),Sl=o("code"),Ab=a("c_i"),Cb=a(", we return:"),Nb=c(),Ol=o("p"),ql=o("code"),Wb=a("[t_k1 for k in indices of unfulfilled constraints]"),Vb=c(),ze=o("p"),Mb=a(`If we are in the middle of a constraint, then we return:
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that\u2019s the only one we\u2019ll return.`),Jb=c(),jt=o("div"),m(hn.$$.fragment),Qb=c(),Nl=o("p"),Zb=a("token_ids: the tokens generated thus far to reset the state of the progress through constraints."),$p=c(),lt=o("h2"),Ht=o("a"),Wl=o("span"),m(gn.$$.fragment),e1=c(),Vl=o("span"),t1=a("BeamSearch"),Tp=c(),A=o("div"),m(un.$$.fragment),r1=c(),dt=o("p"),o1=a("Abstract base class for all beam scorers that are used for "),Aa=o("a"),n1=a("beam_search()"),s1=a(` and
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`),Ar=n(Rl,"A",{href:!0,rel:!0});var t4=s(Ar);Ig=i(t4,"Fairseq"),t4.forEach(r),Ag=i(Rl,"."),Rl.forEach(r),Cg=l(li),Ts=n(li,"DIV",{class:!0});var r4=s(Ts);h(Cr.$$.fragment,r4),r4.forEach(r),li.forEach(r),Ad=l(e),oe=n(e,"DIV",{class:!0});var di=s(oe);h(Nr.$$.fragment,di),Ng=l(di),ys=n(di,"P",{});var U1=s(ys);ks=n(U1,"A",{href:!0});var o4=s(ks);Wg=i(o4,"LogitsProcessor"),o4.forEach(r),Vg=i(U1," that enforces that specified sequences will never be sampled."),U1.forEach(r),Mg=l(di),ws=n(di,"DIV",{class:!0});var n4=s(ws);h(Wr.$$.fragment,n4),n4.forEach(r),di.forEach(r),Cd=l(e),ne=n(e,"DIV",{class:!0});var pi=s(ne);h(Vr.$$.fragment,pi),Gg=l(pi),xt=n(pi,"P",{});var Kl=s(xt);Ls=n(Kl,"A",{href:!0});var s4=s(Ls);jg=i(s4,"LogitsProcessor"),s4.forEach(r),Hg=i(Kl,` that enforces constrained generation and is useful for prefix-conditioned constrained
generation. See `),Mr=n(Kl,"A",{href:!0,rel:!0});var a4=s(Mr);Rg=i(a4,"Autoregressive Entity Retrieval"),a4.forEach(r),Kg=i(Kl," for more information."),Kl.forEach(r),Ug=l(pi),xs=n(pi,"DIV",{class:!0});var i4=s(xs);h(Gr.$$.fragment,i4),i4.forEach(r),pi.forEach(r),Nd=l(e),se=n(e,"DIV",{class:!0});var fi=s(se);h(jr.$$.fragment,fi),Yg=l(fi),Ae=n(fi,"P",{});var On=s(Ae);Es=n(On,"A",{href:!0});var c4=s(Es);Xg=i(c4,"LogitsProcessor"),c4.forEach(r),Jg=i(On,` that enforces diverse beam search. Note that this logits processor is only effective for
`),Ps=n(On,"A",{href:!0});var l4=s(Ps);Qg=i(l4,"PreTrainedModel.group_beam_search()"),l4.forEach(r),Zg=i(On,". See "),Hr=n(On,"A",{href:!0,rel:!0});var d4=s(Hr);eu=i(d4,`Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models`),d4.forEach(r),tu=i(On," for more details."),On.forEach(r),ru=l(fi),Fs=n(fi,"DIV",{class:!0});var p4=s(Fs);h(Rr.$$.fragment,p4),p4.forEach(r),fi.forEach(r),Wd=l(e),ae=n(e,"DIV",{class:!0});var mi=s(ae);h(Kr.$$.fragment,mi),ou=l(mi),Ds=n(mi,"P",{});var Y1=s(Ds);zs=n(Y1,"A",{href:!0});var f4=s(zs);nu=i(f4,"LogitsProcessor"),f4.forEach(r),su=i(Y1," that enforces the specified token as the first generated token."),Y1.forEach(r),au=l(mi),Ss=n(mi,"DIV",{class:!0});var m4=s(Ss);h(Ur.$$.fragment,m4),m4.forEach(r),mi.forEach(r),Vd=l(e),ie=n(e,"DIV",{class:!0});var hi=s(ie);h(Yr.$$.fragment,hi),iu=l(hi),Et=n(hi,"P",{});var Ul=s(Et);Os=n(Ul,"A",{href:!0});var h4=s(Os);cu=i(h4,"LogitsProcessor"),h4.forEach(r),lu=i(Ul," that enforces the specified token as the last generated token when "),Rc=n(Ul,"CODE",{});var g4=s(Rc);du=i(g4,"max_length"),g4.forEach(r),pu=i(Ul," is reached."),Ul.forEach(r),fu=l(hi),qs=n(hi,"DIV",{class:!0});var u4=s(qs);h(Xr.$$.fragment,u4),u4.forEach(r),hi.forEach(r),Md=l(e),ce=n(e,"DIV",{class:!0});var gi=s(ce);h(Jr.$$.fragment,gi),mu=l(gi),M=n(gi,"P",{});var ft=s(M);Bs=n(ft,"A",{href:!0});var _4=s(Bs);hu=i(_4,"LogitsProcessor"),_4.forEach(r),gu=i(ft," that removes all "),Kc=n(ft,"CODE",{});var v4=s(Kc);uu=i(v4,"nan"),v4.forEach(r),_u=i(ft," and "),Uc=n(ft,"CODE",{});var b4=s(Uc);vu=i(b4,"inf"),b4.forEach(r),bu=i(ft,` values to avoid the generation method to fail. Note that using
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reached.`),ft.forEach(r),yu=l(gi),Is=n(gi,"DIV",{class:!0});var T4=s(Is);h(Qr.$$.fragment,T4),T4.forEach(r),gi.forEach(r),Gd=l(e),le=n(e,"DIV",{class:!0});var ui=s(le);h(Zr.$$.fragment,ui),ku=l(ui),Xc=n(ui,"P",{});var y4=s(Xc);wu=i(y4,"Abstract base class for all logit processors that can be applied during generation."),y4.forEach(r),Lu=l(ui),Pt=n(ui,"DIV",{class:!0});var Yp=s(Pt);h(eo.$$.fragment,Yp),xu=l(Yp),Jc=n(Yp,"P",{});var k4=s(Jc);Eu=i(k4,"TF method for processing logits."),k4.forEach(r),Yp.forEach(r),ui.forEach(r),jd=l(e),de=n(e,"DIV",{class:!0});var _i=s(de);h(to.$$.fragment,_i),Pu=l(_i),B=n(_i,"P",{});var Me=s(B);Fu=i(Me,"This class can be used to create a list of "),As=n(Me,"A",{href:!0});var w4=s(As);Du=i(w4,"TFLogitsProcessor"),w4.forEach(r),zu=i(Me," to subsequently process a "),Qc=n(Me,"CODE",{});var L4=s(Qc);Su=i(L4,"scores"),L4.forEach(r),Ou=i(Me,` input tensor.
This class inherits from list and adds a specific `),Zc=n(Me,"EM",{});var x4=s(Zc);el=n(x4,"STRONG",{});var E4=s(el);qu=i(E4,"call"),E4.forEach(r),x4.forEach(r),Bu=i(Me," method to apply each "),Cs=n(Me,"A",{href:!0});var P4=s(Cs);Iu=i(P4,"TFLogitsProcessor"),P4.forEach(r),Au=i(Me,` to the
inputs.`),Me.forEach(r),Cu=l(_i),Ns=n(_i,"DIV",{class:!0});var F4=s(Ns);h(ro.$$.fragment,F4),F4.forEach(r),_i.forEach(r),Hd=l(e),pe=n(e,"DIV",{class:!0});var vi=s(pe);h(oo.$$.fragment,vi),Nu=l(vi),tl=n(vi,"P",{});var D4=s(tl);Wu=i(D4,"Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."),D4.forEach(r),Vu=l(vi),Ft=n(vi,"DIV",{class:!0});var Xp=s(Ft);h(no.$$.fragment,Xp),Mu=l(Xp),rl=n(Xp,"P",{});var z4=s(rl);Gu=i(z4,"TF method for warping logits."),z4.forEach(r),Xp.forEach(r),vi.forEach(r),Rd=l(e),fe=n(e,"DIV",{class:!0});var bi=s(fe);h(so.$$.fragment,bi),ju=l(bi),Ws=n(bi,"P",{});var X1=s(Ws);Vs=n(X1,"A",{href:!0});var S4=s(Vs);Hu=i(S4,"TFLogitsWarper"),S4.forEach(r),Ru=i(X1," for temperature (exponential scaling output probability distribution)."),X1.forEach(r),Ku=l(bi),Ms=n(bi,"DIV",{class:!0});var O4=s(Ms);h(ao.$$.fragment,O4),O4.forEach(r),bi.forEach(r),Kd=l(e),me=n(e,"DIV",{class:!0});var $i=s(me);h(io.$$.fragment,$i),Uu=l($i),Gs=n($i,"P",{});var J1=s(Gs);js=n(J1,"A",{href:!0});var q4=s(js);Yu=i(q4,"TFLogitsWarper"),q4.forEach(r),Xu=i(J1," that performs top-p, i.e. restricting to top tokens summing to <= prob_cut_off."),J1.forEach(r),Ju=l($i),Hs=n($i,"DIV",{class:!0});var B4=s(Hs);h(co.$$.fragment,B4),B4.forEach(r),$i.forEach(r),Ud=l(e),he=n(e,"DIV",{class:!0});var Ti=s(he);h(lo.$$.fragment,Ti),Qu=l(Ti),Rs=n(Ti,"P",{});var Q1=s(Rs);Ks=n(Q1,"A",{href:!0});var I4=s(Ks);Zu=i(I4,"TFLogitsWarper"),I4.forEach(r),e_=i(Q1," that performs top-k, i.e. restricting to the k highest probability elements."),Q1.forEach(r),t_=l(Ti),Us=n(Ti,"DIV",{class:!0});var A4=s(Us);h(po.$$.fragment,A4),A4.forEach(r),Ti.forEach(r),Yd=l(e),ge=n(e,"DIV",{class:!0});var yi=s(ge);h(fo.$$.fragment,yi),r_=l(yi),Ys=n(yi,"P",{});var Z1=s(Ys);Xs=n(Z1,"A",{href:!0});var C4=s(Xs);o_=i(C4,"TFLogitsProcessor"),C4.forEach(r),n_=i(Z1," enforcing a min-length by setting EOS probability to 0."),Z1.forEach(r),s_=l(yi),Js=n(yi,"DIV",{class:!0});var N4=s(Js);h(mo.$$.fragment,N4),N4.forEach(r),yi.forEach(r),Xd=l(e),ue=n(e,"DIV",{class:!0});var ki=s(ue);h(ho.$$.fragment,ki),a_=l(ki),Qs=n(ki,"P",{});var e$=s(Qs);Zs=n(e$,"A",{href:!0});var W4=s(Zs);i_=i(W4,"TFLogitsProcessor"),W4.forEach(r),c_=i(e$," that enforces that specified sequences will never be sampled."),e$.forEach(r),l_=l(ki),ea=n(ki,"DIV",{class:!0});var V4=s(ea);h(go.$$.fragment,V4),V4.forEach(r),ki.forEach(r),Jd=l(e),_e=n(e,"DIV",{class:!0});var wi=s(_e);h(uo.$$.fragment,wi),d_=l(wi),Dt=n(wi,"P",{});var Yl=s(Dt);ta=n(Yl,"A",{href:!0});var M4=s(ta);p_=i(M4,"TFLogitsProcessor"),M4.forEach(r),f_=i(Yl,` that enforces no repetition of n-grams. See
`),_o=n(Yl,"A",{href:!0,rel:!0});var G4=s(_o);m_=i(G4,"Fairseq"),G4.forEach(r),h_=i(Yl,"."),Yl.forEach(r),g_=l(wi),ra=n(wi,"DIV",{class:!0});var j4=s(ra);h(vo.$$.fragment,j4),j4.forEach(r),wi.forEach(r),Qd=l(e),ve=n(e,"DIV",{class:!0});var Li=s(ve);h(bo.$$.fragment,Li),u_=l(Li),oa=n(Li,"P",{});var t$=s(oa);na=n(t$,"A",{href:!0});var H4=s(na);__=i(H4,"TFLogitsProcessor"),H4.forEach(r),v_=i(t$," enforcing an exponential penalty on repeated sequences."),t$.forEach(r),b_=l(Li),sa=n(Li,"DIV",{class:!0});var R4=s(sa);h($o.$$.fragment,R4),R4.forEach(r),Li.forEach(r),Zd=l(e),be=n(e,"DIV",{class:!0});var xi=s(be);h(To.$$.fragment,xi),$_=l(xi),ol=n(xi,"P",{});var K4=s(ol);T_=i(K4,"Abstract base class for all logit processors that can be applied during generation."),K4.forEach(r),y_=l(xi),zt=n(xi,"DIV",{class:!0});var Jp=s(zt);h(yo.$$.fragment,Jp),k_=l(Jp),nl=n(Jp,"P",{});var U4=s(nl);w_=i(U4,"Flax method for processing logits."),U4.forEach(r),Jp.forEach(r),xi.forEach(r),ep=l(e),$e=n(e,"DIV",{class:!0});var Ei=s($e);h(ko.$$.fragment,Ei),L_=l(Ei),L=n(Ei,"P",{});var q=s(L);x_=i(q,"This class can be used to create a list of "),aa=n(q,"A",{href:!0});var Y4=s(aa);E_=i(Y4,"FlaxLogitsProcessor"),Y4.forEach(r),P_=i(q," or "),ia=n(q,"A",{href:!0});var X4=s(ia);F_=i(X4,"FlaxLogitsWarper"),X4.forEach(r),D_=i(q,` to subsequently process
a `),sl=n(q,"CODE",{});var J4=s(sl);z_=i(J4,"scores"),J4.forEach(r),S_=i(q," input tensor. This class inherits from list and adds a specific "),al=n(q,"EM",{});var Q4=s(al);il=n(Q4,"STRONG",{});var Z4=s(il);O_=i(Z4,"call"),Z4.forEach(r),Q4.forEach(r),q_=i(q,` method to apply each
`),ca=n(q,"A",{href:!0});var ey=s(ca);B_=i(ey,"FlaxLogitsProcessor"),ey.forEach(r),I_=i(q," or "),la=n(q,"A",{href:!0});var ty=s(la);A_=i(ty,"FlaxLogitsWarper"),ty.forEach(r),C_=i(q," to the inputs."),q.forEach(r),N_=l(Ei),da=n(Ei,"DIV",{class:!0});var ry=s(da);h(wo.$$.fragment,ry),ry.forEach(r),Ei.forEach(r),tp=l(e),Te=n(e,"DIV",{class:!0});var Pi=s(Te);h(Lo.$$.fragment,Pi),W_=l(Pi),cl=n(Pi,"P",{});var oy=s(cl);V_=i(oy,"Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."),oy.forEach(r),M_=l(Pi),St=n(Pi,"DIV",{class:!0});var Qp=s(St);h(xo.$$.fragment,Qp),G_=l(Qp),ll=n(Qp,"P",{});var ny=s(ll);j_=i(ny,"Flax method for warping logits."),ny.forEach(r),Qp.forEach(r),Pi.forEach(r),rp=l(e),ye=n(e,"DIV",{class:!0});var Fi=s(ye);h(Eo.$$.fragment,Fi),H_=l(Fi),pa=n(Fi,"P",{});var r$=s(pa);fa=n(r$,"A",{href:!0});var sy=s(fa);R_=i(sy,"FlaxLogitsWarper"),sy.forEach(r),K_=i(r$," for temperature (exponential scaling output probability distribution)."),r$.forEach(r),U_=l(Fi),ma=n(Fi,"DIV",{class:!0});var ay=s(ma);h(Po.$$.fragment,ay),ay.forEach(r),Fi.forEach(r),op=l(e),ke=n(e,"DIV",{class:!0});var Di=s(ke);h(Fo.$$.fragment,Di),Y_=l(Di),ha=n(Di,"P",{});var o$=s(ha);ga=n(o$,"A",{href:!0});var iy=s(ga);X_=i(iy,"FlaxLogitsWarper"),iy.forEach(r),J_=i(o$," that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off."),o$.forEach(r),Q_=l(Di),ua=n(Di,"DIV",{class:!0});var cy=s(ua);h(Do.$$.fragment,cy),cy.forEach(r),Di.forEach(r),np=l(e),we=n(e,"DIV",{class:!0});var zi=s(we);h(zo.$$.fragment,zi),Z_=l(zi),_a=n(zi,"P",{});var n$=s(_a);va=n(n$,"A",{href:!0});var ly=s(va);ev=i(ly,"FlaxLogitsWarper"),ly.forEach(r),tv=i(n$," that performs top-k, i.e. restricting to the k highest probability elements."),n$.forEach(r),rv=l(zi),ba=n(zi,"DIV",{class:!0});var dy=s(ba);h(So.$$.fragment,dy),dy.forEach(r),zi.forEach(r),sp=l(e),Le=n(e,"DIV",{class:!0});var Si=s(Le);h(Oo.$$.fragment,Si),ov=l(Si),$a=n(Si,"P",{});var s$=s($a);Ta=n(s$,"A",{href:!0});var py=s(Ta);nv=i(py,"FlaxLogitsProcessor"),py.forEach(r),sv=i(s$," that enforces the specified token as the first generated token."),s$.forEach(r),av=l(Si),ya=n(Si,"DIV",{class:!0});var fy=s(ya);h(qo.$$.fragment,fy),fy.forEach(r),Si.forEach(r),ap=l(e),xe=n(e,"DIV",{class:!0});var Oi=s(xe);h(Bo.$$.fragment,Oi),iv=l(Oi),Ot=n(Oi,"P",{});var Xl=s(Ot);ka=n(Xl,"A",{href:!0});var my=s(ka);cv=i(my,"FlaxLogitsProcessor"),my.forEach(r),lv=i(Xl," that enforces the specified token as the last generated token when "),dl=n(Xl,"CODE",{});var hy=s(dl);dv=i(hy,"max_length"),hy.forEach(r),pv=i(Xl," is reached."),Xl.forEach(r),fv=l(Oi),wa=n(Oi,"DIV",{class:!0});var gy=s(wa);h(Io.$$.fragment,gy),gy.forEach(r),Oi.forEach(r),ip=l(e),Ee=n(e,"DIV",{class:!0});var qi=s(Ee);h(Ao.$$.fragment,qi),mv=l(qi),La=n(qi,"P",{});var a$=s(La);xa=n(a$,"A",{href:!0});var uy=s(xa);hv=i(uy,"FlaxLogitsProcessor"),uy.forEach(r),gv=i(a$," enforcing a min-length by setting EOS probability to 0."),a$.forEach(r),uv=l(qi),Ea=n(qi,"DIV",{class:!0});var _y=s(Ea);h(Co.$$.fragment,_y),_y.forEach(r),qi.forEach(r),cp=l(e),ot=n(e,"H2",{class:!0});var Zp=s(ot);qt=n(Zp,"A",{id:!0,class:!0,href:!0});var vy=s(qt);pl=n(vy,"SPAN",{});var by=s(pl);h(No.$$.fragment,by),by.forEach(r),vy.forEach(r),_v=l(Zp),fl=n(Zp,"SPAN",{});var $y=s(fl);vv=i($y,"StoppingCriteria"),$y.forEach(r),Zp.forEach(r),lp=l(e),Bt=n(e,"P",{});var ef=s(Bt);bv=i(ef,"A "),Pa=n(ef,"A",{href:!0});var Ty=s(Pa);$v=i(Ty,"StoppingCriteria"),Ty.forEach(r),Tv=i(ef," can be used to change when to stop generation (other than EOS token)."),ef.forEach(r),dp=l(e),Pe=n(e,"DIV",{class:!0});var Bi=s(Pe);h(Wo.$$.fragment,Bi),yv=l(Bi),ml=n(Bi,"P",{});var yy=s(ml);kv=i(yy,"Abstract base class for all stopping criteria that can be applied during generation."),yy.forEach(r),wv=l(Bi),Fa=n(Bi,"DIV",{class:!0});var ky=s(Fa);h(Vo.$$.fragment,ky),ky.forEach(r),Bi.forEach(r),pp=l(e),nt=n(e,"DIV",{class:!0});var tf=s(nt);h(Mo.$$.fragment,tf),Lv=l(tf),Da=n(tf,"DIV",{class:!0});var wy=s(Da);h(Go.$$.fragment,wy),wy.forEach(r),tf.forEach(r),fp=l(e),Fe=n(e,"DIV",{class:!0});var Ii=s(Fe);h(jo.$$.fragment,Ii),xv=l(Ii),Ho=n(Ii,"P",{});var rf=s(Ho);Ev=i(rf,"This class can be used to stop generation whenever the full generated number of tokens exceeds "),hl=n(rf,"CODE",{});var Ly=s(hl);Pv=i(Ly,"max_length"),Ly.forEach(r),Fv=i(rf,`. Keep
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<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wi_0 Linear
<span class="hljs-attribute">2</span>.<span class="hljs-number">17</span>e-<span class="hljs-number">07</span> <span class="hljs-number">4</span>.<span class="hljs-number">50</span>e+<span class="hljs-number">00</span> weight
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">2</span>.<span class="hljs-number">68</span>e-<span class="hljs-number">06</span> <span class="hljs-number">3</span>.<span class="hljs-number">70</span>e+<span class="hljs-number">01</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wi_1 Linear
<span class="hljs-attribute">8</span>.<span class="hljs-number">08</span>e-<span class="hljs-number">07</span> <span class="hljs-number">2</span>.<span class="hljs-number">66</span>e+<span class="hljs-number">01</span> weight
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">1</span>.<span class="hljs-number">27</span>e-<span class="hljs-number">04</span> <span class="hljs-number">2</span>.<span class="hljs-number">37</span>e+<span class="hljs-number">02</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wo Linear
<span class="hljs-attribute">1</span>.<span class="hljs-number">01</span>e-<span class="hljs-number">06</span> <span class="hljs-number">6</span>.<span class="hljs-number">44</span>e+<span class="hljs-number">00</span> weight
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span> <span class="hljs-number">9</span>.<span class="hljs-number">74</span>e+<span class="hljs-number">03</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense T5DenseGatedGeluDense
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.dropout Dropout
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span> inf output`}}),Se=new ka({props:{code:"debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100)",highlighted:'debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=<span class="hljs-number">100</span>)'}}),Ne=new ka({props:{code:"debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3])",highlighted:'debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>])'}}),Ge=new ka({props:{code:"debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3)",highlighted:'debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>], abort_after_batch_num=<span class="hljs-number">3</span>)'}}),{c(){O=s("meta"),Ve=i(),P=s("h1"),T=s("a"),rt=s("span"),b(ne.$$.fragment),_s=i(),nt=s("span"),ws=o("Utilities for Trainer"),Ta=i(),Y=s("p"),$s=o("This page lists all the utility functions used by "),Me=s("a"),ys=o("Trainer"),Es=o("."),xa=i(),Re=s("p"),Ds=o("Most of those are only useful if you are studying the code of the Trainer in the library."),Oa=i(),L=s("h2"),W=s("a"),ot=s("span"),b(oe.$$.fragment),js=i(),lt=s("span"),Ps=o("Utilities"),Aa=i(),I=s("div"),b(le.$$.fragment),ks=i(),it=s("p"),Ts=o("Evaluation output (always contains labels), to be used to compute metrics."),Ca=i(),U=s("div"),b(ie.$$.fragment),xs=i(),ct=s("p"),Os=o("An enumeration."),La=i(),z=s("div"),b(ce.$$.fragment),As=i(),D=s("p"),Cs=o("Helper function for reproducible behavior to set the seed in "),pt=s("code"),Ls=o("random"),Is=o(", "),ht=s("code"),Us=o("numpy"),zs=o(", "),dt=s("code"),Ss=o("torch"),Hs=o(" and/or "),ut=s("code"),Ns=o("tf"),Gs=o(" (if installed)."),Ia=i(),S=s("div"),b(pe.$$.fragment),Vs=i(),ft=s("p"),Ms=o("Decorator to make all processes in distributed training wait for each local_master to do something."),Ua=i(),H=s("h2"),J=s("a"),mt=s("span"),b(he.$$.fragment),Rs=i(),gt=s("span"),Fs=o("Callbacks internals"),za=i(),N=s("div"),b(de.$$.fragment),qs=i(),bt=s("p"),Bs=o("Internal class that just calls the list of callbacks in order."),Sa=i(),G=s("h2"),Q=s("a"),vt=s("span"),b(ue.$$.fragment),Ks=i(),_t=s("span"),Ys=o("Distributed Evaluation"),Ha=i(),u=s("div"),b(fe.$$.fragment),Ws=i(),wt=s("p"),Js=o("A class responsible for properly gathering tensors (or nested list/tuple of tensors) on the CPU by chunks."),Qs=i(),$t=s("p"),Xs=o(`If our dataset has 16 samples with a batch size of 2 on 3 processes and we gather then transfer on CPU at every
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each frame reporting`),re.forEach(t),Mn=c(h),Ce=r(h,"OL",{});var us=n(Ce);Le=r(us,"LI",{});var fs=n(Le);Rn=l(fs,"the fully qualified module name plus the class name whose "),la=r(fs,"CODE",{});var pi=n(la);Fn=l(pi,"forward"),pi.forEach(t),qn=l(fs," was run"),fs.forEach(t),Bn=c(us),ia=r(us,"LI",{});var hi=n(ia);Kn=l(hi,"the absolute min and max value of all elements for each module weights, and the inputs and output"),hi.forEach(t),us.forEach(t),Yn=c(h),Ie=r(h,"P",{});var ms=n(Ie);Wn=l(ms,"For example, here is the header and the last few frames in detection report for "),ca=r(ms,"CODE",{});var di=n(ca);Jn=l(di,"google/mt5-small"),di.forEach(t),Qn=l(ms,` run in fp16
mixed precision :`),ms.forEach(t),Xn=c(h),v(Ue.$$.fragment,h),Zn=c(h),B=r(h,"P",{});var tt=n(B);eo=l(tt,"You can see here, that "),pa=r(tt,"CODE",{});var ui=n(pa);to=l(ui,"T5DenseGatedGeluDense.forward"),ui.forEach(t),ao=l(tt,` resulted in output activations, whose absolute max value was
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renormalizes the weights, after it zeroed some of the elements, which pushes the absolute max value to more than
64K, and we get an overlow.`),tt.forEach(t),no=c(h),da=r(h,"P",{});var mi=n(da);oo=l(mi,`As you can see it\u2019s the previous frames that we need to look into when the numbers start going into very large for
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given batch, and only do that for batches 1 and 3. Then you instantiate this class as :`),bs.forEach(t),jo=c(h),v(Ne.$$.fragment,h),Po=c(h),_a=r(h,"P",{});var yi=n(_a);ko=l(yi,"And now full batches 1 and 3 will be traced using the same format as explained above. Batches are 0-indexed."),yi.forEach(t),To=c(h),wa=r(h,"P",{});var Ei=n(wa);xo=l(Ei,`This is helpful if you know that the program starts misbehaving after a certain batch number, so you can
fast-forward right to that area.`),Ei.forEach(t),Oo=c(h),$a=r(h,"P",{});var Di=n($a);Ao=l(Di,"Early stopping:"),Di.forEach(t),Co=c(h),ya=r(h,"P",{});var ji=n(ya);Lo=l(ji,"You can also specify the batch number after which to stop the training, with :"),ji.forEach(t),Io=c(h),v(Ge.$$.fragment,h),Uo=c(h),Ea=r(h,"P",{});var Pi=n(Ea);zo=l(Pi,"This feature is mainly useful in the tracing mode, but you can use it for any mode."),Pi.forEach(t),So=c(h),Je=r(h,"P",{});var Xo=n(Je);Da=r(Xo,"STRONG",{});var ki=n(Da);Ho=l(ki,"Performance"),ki.forEach(t),No=l(Xo,":"),Xo.forEach(t),Go=c(h),K=r(h,"P",{});var at=n(K);Vo=l(at,"As this module measures absolute "),ja=r(at,"CODE",{});var Ti=n(ja);Mo=l(Ti,"min"),Ti.forEach(t),Ro=l(at,"/`"),Pa=r(at,"CODE",{});var xi=n(Pa);Fo=l(xi,"max"),xi.forEach(t),qo=l(at,` of each weight of the model on every forward it\u2019ll slow the training
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<p>A <a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.modeling_utils.SquadHeadOutput"
>transformers.modeling_utils.SquadHeadOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code><class 'transformers.configuration_utils.PretrainedConfig'></code>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned if both <code>start_positions</code> and <code>end_positions</code> are provided) \u2014 Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.</li>
<li><strong>start_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Log probabilities for the top config.start_n_top start token possibilities (beam-search).</li>
<li><strong>start_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Indices for the top config.start_n_top start token possibilities (beam-search).</li>
<li><strong>end_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Log probabilities for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities
(beam-search).</li>
<li><strong>end_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Indices for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities (beam-search).</li>
<li><strong>cls_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) \u2014 Log probabilities for the <code>is_impossible</code> label of the answers.</li>
</ul>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.modeling_utils.SquadHeadOutput"
>transformers.modeling_utils.SquadHeadOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ot=new T({props:{name:"class transformers.modeling_utils.SequenceSummary",anchor:"transformers.modeling_utils.SequenceSummary",parameters:[{name:"config",val:": PretrainedConfig"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2197",parametersDescription:[{anchor:"transformers.modeling_utils.SequenceSummary.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) —
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for the default values it uses):</p>
<ul>
<li>
<p><strong>summary_type</strong> (<code>str</code>) — The method to use to make this summary. Accepted values are:</p>
<ul>
<li><code>"last"</code> — Take the last token hidden state (like XLNet)</li>
<li><code>"first"</code> — Take the first token hidden state (like Bert)</li>
<li><code>"mean"</code> — Take the mean of all tokens hidden states</li>
<li><code>"cls_index"</code> — Supply a Tensor of classification token position (GPT/GPT-2)</li>
<li><code>"attn"</code> — Not implemented now, use multi-head attention</li>
</ul>
</li>
<li>
<p><strong>summary_use_proj</strong> (<code>bool</code>) — Add a projection after the vector extraction.</p>
</li>
<li>
<p><strong>summary_proj_to_labels</strong> (<code>bool</code>) — If <code>True</code>, the projection outputs to <code>config.num_labels</code> classes
(otherwise to <code>config.hidden_size</code>).</p>
</li>
<li>
<p><strong>summary_activation</strong> (<code>Optional[str]</code>) — Set to <code>"tanh"</code> to add a tanh activation to the output,
another string or <code>None</code> will add no activation.</p>
</li>
<li>
<p><strong>summary_first_dropout</strong> (<code>float</code>) — Optional dropout probability before the projection and activation.</p>
</li>
<li>
<p><strong>summary_last_dropout</strong> (<code>float</code>)— Optional dropout probability after the projection and activation.</p>
</li>
</ul>`,name:"config"}]}}),st=new T({props:{name:"forward",anchor:"transformers.modeling_utils.SequenceSummary.forward",parameters:[{name:"hidden_states",val:": FloatTensor"},{name:"cls_index",val:": typing.Optional[torch.LongTensor] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2252",parametersDescription:[{anchor:"transformers.modeling_utils.SequenceSummary.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>[batch_size, seq_len, hidden_size]</code>) —
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<p>The summary of the sequence hidden states.</p>
`,returnType:`
<p><code>torch.FloatTensor</code></p>
`}}),nt=new so({}),rt=new T({props:{name:"transformers.apply_chunking_to_forward",anchor:"transformers.apply_chunking_to_forward",parameters:[{name:"forward_fn",val:": typing.Callable[..., torch.Tensor]"},{name:"chunk_size",val:": int"},{name:"chunk_dim",val:": int"},{name:"*input_tensors",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2401",parametersDescription:[{anchor:"transformers.apply_chunking_to_forward.forward_fn",description:`<strong>forward_fn</strong> (<code>Callable[..., torch.Tensor]</code>) —
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The input tensors of <code>forward_fn</code> which will be chunked`,name:"input_tensors"}],returnDescription:`
<p>A tensor with the same shape as the <code>forward_fn</code> would have given if applied\`.</p>
`,returnType:`
<p><code>torch.Tensor</code></p>
`}}),at=new Ji({props:{code:`# rename the usual forward() fn to forward_chunk()
def forward_chunk(self, hidden_states):
hidden_states = self.decoder(hidden_states)
return hidden_states
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def forward(self, hidden_states):
return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)`,highlighted:`<span class="hljs-comment"># rename the usual forward() fn to forward_chunk()</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward_chunk</span>(<span class="hljs-params">self, hidden_states</span>):
hidden_states = self.decoder(hidden_states)
<span class="hljs-keyword">return</span> hidden_states
<span class="hljs-comment"># implement a chunked forward function</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, hidden_states</span>):
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`,returnType:`
<p><code>Tuple[Set[int], torch.LongTensor]</code></p>
`}}),dt=new T({props:{name:"transformers.prune_layer",anchor:"transformers.prune_layer",parameters:[{name:"layer",val:": typing.Union[torch.nn.modules.linear.Linear, transformers.modeling_utils.Conv1D]"},{name:"index",val:": LongTensor"},{name:"dim",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2377",parametersDescription:[{anchor:"transformers.prune_layer.layer",description:"<strong>layer</strong> (<code>Union[torch.nn.Linear, Conv1D]</code>) — The layer to prune.",name:"layer"},{anchor:"transformers.prune_layer.index",description:"<strong>index</strong> (<code>torch.LongTensor</code>) — The indices to keep in the layer.",name:"index"},{anchor:"transformers.prune_layer.dim",description:"<strong>dim</strong> (<code>int</code>, <em>optional</em>) — The dimension on which to keep the indices.",name:"dim"}],returnDescription:`
<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
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<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
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<p><a
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>Conv1D</a></p>
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config class of your model for the default values it uses):</p>
<ul>
<li>
<p><strong>summary_type</strong> (<code>str</code>) — The method to use to make this summary. Accepted values are:</p>
<ul>
<li><code>"last"</code> — Take the last token hidden state (like XLNet)</li>
<li><code>"first"</code> — Take the first token hidden state (like Bert)</li>
<li><code>"mean"</code> — Take the mean of all tokens hidden states</li>
<li><code>"cls_index"</code> — Supply a Tensor of classification token position (GPT/GPT-2)</li>
<li><code>"attn"</code> — Not implemented now, use multi-head attention</li>
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<li>
<p><strong>summary_use_proj</strong> (<code>bool</code>) — Add a projection after the vector extraction.</p>
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<li>
<p><strong>summary_proj_to_labels</strong> (<code>bool</code>) — If <code>True</code>, the projection outputs to <code>config.num_labels</code> classes
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<li>
<p><strong>summary_activation</strong> (<code>Optional[str]</code>) — Set to <code>"tanh"</code> to add a tanh activation to the output,
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<li>
<p><strong>summary_first_dropout</strong> (<code>float</code>) — Optional dropout probability before the projection and activation.</p>
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<p><strong>summary_last_dropout</strong> (<code>float</code>)— Optional dropout probability after the projection and activation.</p>
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The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name.`,name:"truncation_side"},{anchor:"transformers.PreTrainedTokenizerBase.model_input_names",description:`<strong>model_input_names</strong> (<code>List[string]</code>, <em>optional</em>) —
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A special token representing the beginning of a sentence. Will be associated to <code>self.bos_token</code> and
<code>self.bos_token_id</code>.`,name:"bos_token"},{anchor:"transformers.PreTrainedTokenizerBase.eos_token",description:`<strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the end of a sentence. Will be associated to <code>self.eos_token</code> and
<code>self.eos_token_id</code>.`,name:"eos_token"},{anchor:"transformers.PreTrainedTokenizerBase.unk_token",description:`<strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing an out-of-vocabulary token. Will be associated to <code>self.unk_token</code> and
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A special token separating two different sentences in the same input (used by BERT for instance). Will be
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A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
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A special token representing the class of the input (used by BERT for instance). Will be associated to
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A special token representing a masked token (used by masked-language modeling pretraining objectives, like
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A tuple or a list of additional special tokens. Add them here to ensure they won’t be split by the
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<code>self.additional_special_tokens_ids</code>.`,name:"additional_special_tokens"}]}}),rt=new z({props:{name:"__call__",anchor:"transformers.PreTrainedTokenizerBase.__call__",parameters:[{name:"text",val:": typing.Union[str, typing.List[str], typing.List[typing.List[str]]]"},{name:"text_pair",val:": typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"padding",val:": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False"},{name:"truncation",val:": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"stride",val:": int = 0"},{name:"is_split_into_words",val:": bool = False"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"},{name:"return_token_type_ids",val:": typing.Optional[bool] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_overflowing_tokens",val:": bool = False"},{name:"return_special_tokens_mask",val:": bool = False"},{name:"return_offsets_mapping",val:": bool = False"},{name:"return_length",val:": bool = False"},{name:"verbose",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2379",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.__call__.text",description:`<strong>text</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).`,name:"text"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.text_pair",description:`<strong>text_pair</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.stride",description:`<strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.`,name:"stride"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.is_split_into_words",description:`<strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.`,name:"is_split_into_words"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.return_token_type_ids",description:`<strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"return_token_type_ids"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.return_overflowing_tokens",description:`<strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.`,name:"return_overflowing_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.__call__.return_special_tokens_mask",description:`<strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
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<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
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**kwargs — passed to the <code>self.tokenize()</code> method`,name:"verbose"}],returnDescription:`
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> \u2014 List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> \u2014 List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>\u201Ctoken_type_ids\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> \u2014 List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>\u201Cattention_mask\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> \u2014 List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> \u2014 Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> \u2014 List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> \u2014 The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),st=new z({props:{name:"as_target_tokenizer",anchor:"transformers.PreTrainedTokenizerBase.as_target_tokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3404"}}),at=new z({props:{name:"batch_decode",anchor:"transformers.PreTrainedTokenizerBase.batch_decode",parameters:[{name:"sequences",val:": typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]"},{name:"skip_special_tokens",val:": bool = False"},{name:"clean_up_tokenization_spaces",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3250",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.batch_decode.sequences",description:`<strong>sequences</strong> (<code>Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
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<p>The list of decoded sentences.</p>
`,returnType:`
<p><code>List[str]</code></p>
`}}),it=new z({props:{name:"batch_encode_plus",anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus",parameters:[{name:"batch_text_or_text_pairs",val:": typing.Union[typing.List[str], typing.List[typing.Tuple[str, str]], typing.List[typing.List[str]], typing.List[typing.Tuple[typing.List[str], typing.List[str]]], typing.List[typing.List[int]], typing.List[typing.Tuple[typing.List[int], typing.List[int]]]]"},{name:"add_special_tokens",val:": bool = True"},{name:"padding",val:": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False"},{name:"truncation",val:": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"stride",val:": int = 0"},{name:"is_split_into_words",val:": bool = False"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"},{name:"return_token_type_ids",val:": typing.Optional[bool] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_overflowing_tokens",val:": bool = False"},{name:"return_special_tokens_mask",val:": bool = False"},{name:"return_offsets_mapping",val:": bool = False"},{name:"return_length",val:": bool = False"},{name:"verbose",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2600",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.batch_text_or_text_pairs",description:`<strong>batch_text_or_text_pairs</strong> (<code>List[str]</code>, <code>List[Tuple[str, str]]</code>, <code>List[List[str]]</code>, <code>List[Tuple[List[str], List[str]]]</code>, and for not-fast tokenizers, also <code>List[List[int]]</code>, <code>List[Tuple[List[int], List[int]]]</code>) —
Batch of sequences or pair of sequences to be encoded. This can be a list of
string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see
details in <code>encode_plus</code>).`,name:"batch_text_or_text_pairs"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.add_special_tokens",description:`<strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.`,name:"add_special_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.stride",description:`<strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.`,name:"stride"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.is_split_into_words",description:`<strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.`,name:"is_split_into_words"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.return_token_type_ids",description:`<strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"return_token_type_ids"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.return_overflowing_tokens",description:`<strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.`,name:"return_overflowing_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.return_special_tokens_mask",description:`<strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.`,name:"return_special_tokens_mask"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.return_offsets_mapping",description:`<strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.`,name:"return_offsets_mapping"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.return_length",description:`<strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.`,name:"return_length"},{anchor:"transformers.PreTrainedTokenizerBase.batch_encode_plus.verbose",description:`<strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method`,name:"verbose"}],returnDescription:`
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> \u2014 List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> \u2014 List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>\u201Ctoken_type_ids\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> \u2014 List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>\u201Cattention_mask\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> \u2014 List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> \u2014 Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> \u2014 List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> \u2014 The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),Se=new Pr({props:{warning:"{true}",$$slots:{default:[th]},$$scope:{ctx:W}}}),dt=new z({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2884",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens.token_ids_0",description:"<strong>token_ids_0</strong> (<code>List[int]</code>) — The first tokenized sequence.",name:"token_ids_0"},{anchor:"transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens.token_ids_1",description:"<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) — The second tokenized sequence.",name:"token_ids_1"}],returnDescription:`
<p>The model input with special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),ct=new z({props:{name:"clean_up_tokenization",anchor:"transformers.PreTrainedTokenizerBase.clean_up_tokenization",parameters:[{name:"out_string",val:": str"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3359",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.clean_up_tokenization.out_string",description:"<strong>out_string</strong> (<code>str</code>) — The text to clean up.",name:"out_string"}],returnDescription:`
<p>The cleaned-up string.</p>
`,returnType:`
<p><code>str</code></p>
`}}),lt=new z({props:{name:"convert_tokens_to_string",anchor:"transformers.PreTrainedTokenizerBase.convert_tokens_to_string",parameters:[{name:"tokens",val:": typing.List[str]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3237",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.convert_tokens_to_string.tokens",description:"<strong>tokens</strong> (<code>List[str]</code>) — The token to join in a string.",name:"tokens"}],returnDescription:`
<p>The joined tokens.</p>
`,returnType:`
<p><code>str</code></p>
`}}),mt=new z({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2864",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences.token_ids_0",description:"<strong>token_ids_0</strong> (<code>List[int]</code>) — The first tokenized sequence.",name:"token_ids_0"},{anchor:"transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences.token_ids_1",description:"<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) — The second tokenized sequence.",name:"token_ids_1"}],returnDescription:`
<p>The token type ids.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),ht=new z({props:{name:"decode",anchor:"transformers.PreTrainedTokenizerBase.decode",parameters:[{name:"token_ids",val:": typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]"},{name:"skip_special_tokens",val:": bool = False"},{name:"clean_up_tokenization_spaces",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3283",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.decode.token_ids",description:`<strong>token_ids</strong> (<code>Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
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Whether or not to clean up the tokenization spaces.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.PreTrainedTokenizerBase.decode.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific decode method.`,name:"kwargs"}],returnDescription:`
<p>The decoded sentence.</p>
`,returnType:`
<p><code>str</code></p>
`}}),ft=new z({props:{name:"encode",anchor:"transformers.PreTrainedTokenizerBase.encode",parameters:[{name:"text",val:": typing.Union[str, typing.List[str], typing.List[int]]"},{name:"text_pair",val:": typing.Union[str, typing.List[str], typing.List[int], NoneType] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"padding",val:": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False"},{name:"truncation",val:": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"stride",val:": int = 0"},{name:"return_tensors",val:": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2189",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.encode.text",description:`<strong>text</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>) —
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
<code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).`,name:"text"},{anchor:"transformers.PreTrainedTokenizerBase.encode.text_pair",description:`<strong>text_pair</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>, <em>optional</em>) —
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the <code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).`,name:"text_pair"},{anchor:"transformers.PreTrainedTokenizerBase.encode.add_special_tokens",description:`<strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.`,name:"add_special_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.encode.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.PreTrainedTokenizerBase.encode.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.PreTrainedTokenizerBase.encode.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.PreTrainedTokenizerBase.encode.stride",description:`<strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.`,name:"stride"},{anchor:"transformers.PreTrainedTokenizerBase.encode.is_split_into_words",description:`<strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.`,name:"is_split_into_words"},{anchor:"transformers.PreTrainedTokenizerBase.encode.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.PreTrainedTokenizerBase.encode.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"}],returnDescription:`
<p>The tokenized ids of the text.</p>
`,returnType:`
<p><code>List[int]</code>, <code>torch.Tensor</code>, <code>tf.Tensor</code> or <code>np.ndarray</code></p>
`}}),gt=new z({props:{name:"encode_plus",anchor:"transformers.PreTrainedTokenizerBase.encode_plus",parameters:[{name:"text",val:": typing.Union[str, typing.List[str], typing.List[int]]"},{name:"text_pair",val:": typing.Union[str, typing.List[str], typing.List[int], NoneType] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"padding",val:": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False"},{name:"truncation",val:": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"stride",val:": int = 0"},{name:"is_split_into_words",val:": bool = False"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"},{name:"return_token_type_ids",val:": typing.Optional[bool] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_overflowing_tokens",val:": bool = False"},{name:"return_special_tokens_mask",val:": bool = False"},{name:"return_offsets_mapping",val:": bool = False"},{name:"return_length",val:": bool = False"},{name:"verbose",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2504",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.text",description:`<strong>text</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code> (the latter only for not-fast tokenizers)) —
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
<code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).`,name:"text"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.text_pair",description:`<strong>text_pair</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>, <em>optional</em>) —
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the <code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).`,name:"text_pair"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.add_special_tokens",description:`<strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.`,name:"add_special_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.stride",description:`<strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.`,name:"stride"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.is_split_into_words",description:`<strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.`,name:"is_split_into_words"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.return_token_type_ids",description:`<strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"return_token_type_ids"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.return_overflowing_tokens",description:`<strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.`,name:"return_overflowing_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.return_special_tokens_mask",description:`<strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.`,name:"return_special_tokens_mask"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.return_offsets_mapping",description:`<strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.`,name:"return_offsets_mapping"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.return_length",description:`<strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.`,name:"return_length"},{anchor:"transformers.PreTrainedTokenizerBase.encode_plus.verbose",description:`<strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method`,name:"verbose"}],returnDescription:`
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> \u2014 List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> \u2014 List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>\u201Ctoken_type_ids\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> \u2014 List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>\u201Cattention_mask\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> \u2014 List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> \u2014 Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> \u2014 List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> \u2014 The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),We=new Pr({props:{warning:"{true}",$$slots:{default:[oh]},$$scope:{ctx:W}}}),kt=new z({props:{name:"from_pretrained",anchor:"transformers.PreTrainedTokenizerBase.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{name:"*init_inputs",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L1551",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>(<strong>Deprecated</strong>, not applicable to all derived classes) A path or url to a single saved vocabulary
file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g.,
<code>./my_model_directory/vocab.txt</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) —
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to force the (re-)download the vocabulary files and override the cached versions if they
exist.`,name:"force_download"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to delete incompletely received files. Attempt to resume the download if such a file
exists.`,name:"resume_download"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.use_auth_token",description:`<strong>use_auth_token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"use_auth_token"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to only rely on local files and not to attempt to download any files.`,name:"local_files_only"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.revision(str,",description:`<strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>"main"</code>) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision(str,"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) —
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.`,name:"subfolder"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.inputs",description:`<strong>inputs</strong> (additional positional arguments, <em>optional</em>) —
Will be passed along to the Tokenizer <code>__init__</code> method.`,name:"inputs"},{anchor:"transformers.PreTrainedTokenizerBase.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the Tokenizer <code>__init__</code> method. Can be used to set special tokens like <code>bos_token</code>,
<code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__</code> for more details.`,name:"kwargs"}]}}),je=new Pr({props:{$$slots:{default:[nh]},$$scope:{ctx:W}}}),vt=new _s({props:{code:`# We can't instantiate directly the base class *PreTrainedTokenizerBase* so let's show our examples on a derived class: BertTokenizer
# Download vocabulary from huggingface.co and cache.
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
# Download vocabulary from huggingface.co (user-uploaded) and cache.
tokenizer = BertTokenizer.from_pretrained("dbmdz/bert-base-german-cased")
# If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
tokenizer = BertTokenizer.from_pretrained("./test/saved_model/")
# If the tokenizer uses a single vocabulary file, you can point directly to this file
tokenizer = BertTokenizer.from_pretrained("./test/saved_model/my_vocab.txt")
# You can link tokens to special vocabulary when instantiating
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", unk_token="<unk>")
# You should be sure '<unk>' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
assert tokenizer.unk_token == "<unk>"`,highlighted:`<span class="hljs-comment"># We can't instantiate directly the base class *PreTrainedTokenizerBase* so let's show our examples on a derived class: BertTokenizer</span>
<span class="hljs-comment"># Download vocabulary from huggingface.co and cache.</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
<span class="hljs-comment"># Download vocabulary from huggingface.co (user-uploaded) and cache.</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"dbmdz/bert-base-german-cased"</span>)
<span class="hljs-comment"># If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-comment"># If the tokenizer uses a single vocabulary file, you can point directly to this file</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"./test/saved_model/my_vocab.txt"</span>)
<span class="hljs-comment"># You can link tokens to special vocabulary when instantiating</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, unk_token=<span class="hljs-string">"<unk>"</span>)
<span class="hljs-comment"># You should be sure '<unk>' is in the vocabulary when doing that.</span>
<span class="hljs-comment"># Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)</span>
<span class="hljs-keyword">assert</span> tokenizer.unk_token == <span class="hljs-string">"<unk>"</span>`}}),Tt=new z({props:{name:"get_special_tokens_mask",anchor:"transformers.PreTrainedTokenizerBase.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3328",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) —
List of ids of the first sequence.`,name:"token_ids_0"},{anchor:"transformers.PreTrainedTokenizerBase.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) —
List of ids of the second sequence.`,name:"token_ids_1"},{anchor:"transformers.PreTrainedTokenizerBase.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],returnDescription:`
<p>1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p>A list of integers in the range [0, 1]</p>
`}}),yt=new z({props:{name:"get_vocab",anchor:"transformers.PreTrainedTokenizerBase.get_vocab",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L1539",returnDescription:`
<p>The vocabulary.</p>
`,returnType:`
<p><code>Dict[str, int]</code></p>
`}}),wt=new z({props:{name:"pad",anchor:"transformers.PreTrainedTokenizerBase.pad",parameters:[{name:"encoded_inputs",val:": typing.Union[transformers.tokenization_utils_base.BatchEncoding, typing.List[transformers.tokenization_utils_base.BatchEncoding], typing.Dict[str, typing.List[int]], typing.Dict[str, typing.List[typing.List[int]]], typing.List[typing.Dict[str, typing.List[int]]]]"},{name:"padding",val:": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = True"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"},{name:"verbose",val:": bool = True"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2703",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.pad.encoded_inputs",description:`<strong>encoded_inputs</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a>, list of <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a>, <code>Dict[str, List[int]]</code>, <code>Dict[str, List[List[int]]</code> or <code>List[Dict[str, List[int]]]</code>) —
Tokenized inputs. Can represent one input (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a> or <code>Dict[str, List[int]]</code>) or a batch of
tokenized inputs (list of <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a>, <em>Dict[str, List[List[int]]]</em> or <em>List[Dict[str,
List[int]]]</em>) so you can use this method during preprocessing as well as in a PyTorch Dataloader
collate function.</p>
<p>Instead of <code>List[int]</code> you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see
the note above for the return type.`,name:"encoded_inputs"},{anchor:"transformers.PreTrainedTokenizerBase.pad.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Select a strategy to pad the returned sequences (according to the model’s padding side and padding
index) among:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.PreTrainedTokenizerBase.pad.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Maximum length of the returned list and optionally padding length (see above).`,name:"max_length"},{anchor:"transformers.PreTrainedTokenizerBase.pad.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability</p>
<blockquote>
<p>= 7.5 (Volta).</p>
</blockquote>`,name:"pad_to_multiple_of"},{anchor:"transformers.PreTrainedTokenizerBase.pad.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"},{anchor:"transformers.PreTrainedTokenizerBase.pad.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.PreTrainedTokenizerBase.pad.verbose",description:`<strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.`,name:"verbose"}]}}),Re=new Pr({props:{$$slots:{default:[rh]},$$scope:{ctx:W}}}),zt=new z({props:{name:"prepare_for_model",anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model",parameters:[{name:"ids",val:": typing.List[int]"},{name:"pair_ids",val:": typing.Optional[typing.List[int]] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"padding",val:": typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False"},{name:"truncation",val:": typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"stride",val:": int = 0"},{name:"pad_to_multiple_of",val:": typing.Optional[int] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.file_utils.TensorType, NoneType] = None"},{name:"return_token_type_ids",val:": typing.Optional[bool] = None"},{name:"return_attention_mask",val:": typing.Optional[bool] = None"},{name:"return_overflowing_tokens",val:": bool = False"},{name:"return_special_tokens_mask",val:": bool = False"},{name:"return_offsets_mapping",val:": bool = False"},{name:"return_length",val:": bool = False"},{name:"verbose",val:": bool = True"},{name:"prepend_batch_axis",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2904",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.ids",description:`<strong>ids</strong> (<code>List[int]</code>) —
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the <code>tokenize</code> and
<code>convert_tokens_to_ids</code> methods.`,name:"ids"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.pair_ids",description:`<strong>pair_ids</strong> (<code>List[int]</code>, <em>optional</em>) —
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the <code>tokenize</code>
and <code>convert_tokens_to_ids</code> methods.`,name:"pair_ids"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.add_special_tokens",description:`<strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.`,name:"add_special_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul>`,name:"truncation"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.stride",description:`<strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.`,name:"stride"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.is_split_into_words",description:`<strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.`,name:"is_split_into_words"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.pad_to_multiple_of",description:`<strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).`,name:"pad_to_multiple_of"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.return_token_type_ids",description:`<strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"return_token_type_ids"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.return_attention_mask",description:`<strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"return_attention_mask"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.return_overflowing_tokens",description:`<strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.`,name:"return_overflowing_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.return_special_tokens_mask",description:`<strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.`,name:"return_special_tokens_mask"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.return_offsets_mapping",description:`<strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.`,name:"return_offsets_mapping"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.return_length",description:`<strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.`,name:"return_length"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_for_model.verbose",description:`<strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method`,name:"verbose"}],returnDescription:`
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> \u2014 List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> \u2014 List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>\u201Ctoken_type_ids\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> \u2014 List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>\u201Cattention_mask\u201D</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> \u2014 List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> \u2014 Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> \u2014 List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> \u2014 The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),xt=new z({props:{name:"prepare_seq2seq_batch",anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch",parameters:[{name:"src_texts",val:": typing.List[str]"},{name:"tgt_texts",val:": typing.Optional[typing.List[str]] = None"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"max_target_length",val:": typing.Optional[int] = None"},{name:"padding",val:": str = 'longest'"},{name:"return_tensors",val:": str = None"},{name:"truncation",val:": bool = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3438",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.src_texts",description:`<strong>src_texts</strong> (<code>List[str]</code>) —
List of documents to summarize or source language texts.`,name:"src_texts"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.tgt_texts",description:`<strong>tgt_texts</strong> (<code>list</code>, <em>optional</em>) —
List of summaries or target language texts.`,name:"tgt_texts"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.max_length",description:`<strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length for encoder inputs (documents to summarize or source language texts) If
left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length is
required by one of the truncation/padding parameters. If the model has no specific maximum input length
(like XLNet) truncation/padding to a maximum length will be deactivated.`,name:"max_length"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.max_target_length",description:`<strong>max_target_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set
to <code>None</code>, this will use the max_length value.`,name:"max_target_length"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.padding",description:`<strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul>`,name:"padding"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.truncation",description:`<strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
**kwargs —
Additional keyword arguments passed along to <code>self.__call__</code>.</li>
</ul>`,name:"truncation"}],returnDescription:`
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li><strong>input_ids</strong> \u2014 List of token ids to be fed to the encoder.</li>
<li><strong>attention_mask</strong> \u2014 List of indices specifying which tokens should be attended to by the model.</li>
<li><strong>labels</strong> \u2014 List of token ids for tgt_texts.</li>
</ul>
<p>The full set of keys <code>[input_ids, attention_mask, labels]</code>, will only be returned if tgt_texts is passed.
Otherwise, input_ids, attention_mask will be the only keys.</p>
`,returnType:`
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
`}}),$t=new z({props:{name:"push_to_hub",anchor:"transformers.file_utils.PushToHubMixin.push_to_hub",parameters:[{name:"repo_path_or_name",val:": typing.Optional[str] = None"},{name:"repo_url",val:": typing.Optional[str] = None"},{name:"use_temp_dir",val:": bool = False"},{name:"commit_message",val:": typing.Optional[str] = None"},{name:"organization",val:": typing.Optional[str] = None"},{name:"private",val:": typing.Optional[bool] = None"},{name:"use_auth_token",val:": typing.Union[bool, str, NoneType] = None"},{name:"**model_card_kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842",parametersDescription:[{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name",description:`<strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your tokenizer in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.`,name:"repo_path_or_name"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.repo_url",description:`<strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.`,name:"repo_url"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir",description:`<strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.`,name:"use_temp_dir"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add tokenizer"</code>.`,name:"commit_message"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.organization",description:`<strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your tokenizer (you must be a member of this organization).`,name:"organization"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).`,name:"private"},{anchor:"transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token",description:`<strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.`,name:"use_auth_token"}],returnDescription:`
<p>The url of the commit of your tokenizer in the given repository.</p>
`,returnType:`
<p><code>str</code></p>
`}}),qt=new _s({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
# Push the tokenizer to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
tokenizer.push_to_hub("my-finetuned-bert")
# Push the tokenizer to your namespace with the name "my-finetuned-bert" with no local clone.
tokenizer.push_to_hub("my-finetuned-bert", use_temp_dir=True)
# Push the tokenizer to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
tokenizer.push_to_hub("my-finetuned-bert", organization="huggingface")
# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
tokenizer.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" with no local clone.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the tokenizer to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)`}}),Et=new z({props:{name:"register_for_auto_class",anchor:"transformers.PreTrainedTokenizerBase.register_for_auto_class",parameters:[{name:"auto_class",val:" = 'AutoTokenizer'"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3412",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.register_for_auto_class.auto_class",description:`<strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"AutoTokenizer"</code>) —
The auto class to register this new tokenizer with.`,name:"auto_class"}]}}),He=new Pr({props:{warning:"{true}",$$slots:{default:[sh]},$$scope:{ctx:W}}}),Lt=new z({props:{name:"save_pretrained",anchor:"transformers.PreTrainedTokenizerBase.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"legacy_format",val:": typing.Optional[bool] = None"},{name:"filename_prefix",val:": typing.Optional[str] = None"},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L1990",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.save_pretrained.save_directory",description:"<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — The path to a directory where the tokenizer will be saved.",name:"save_directory"},{anchor:"transformers.PreTrainedTokenizerBase.save_pretrained.legacy_format",description:`<strong>legacy_format</strong> (<code>bool</code>, <em>optional</em>) —
Only applicable for a fast tokenizer. If unset (default), will save the tokenizer in the unified JSON
format as well as in legacy format if it exists, i.e. with tokenizer specific vocabulary and a separate
added_tokens files.</p>
<p>If <code>False</code>, will only save the tokenizer in the unified JSON format. This format is incompatible with
“slow” tokenizers (not powered by the <em>tokenizers</em> library), so the tokenizer will not be able to be
loaded in the corresponding “slow” tokenizer.</p>
<p>If <code>True</code>, will save the tokenizer in legacy format. If the “slow” tokenizer doesn’t exits, a value
error is raised.
filename_prefix — (<code>str</code>, <em>optional</em>):
A prefix to add to the names of the files saved by the tokenizer.`,name:"legacy_format"},{anchor:"transformers.PreTrainedTokenizerBase.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>`,name:"push_to_hub"}],returnDescription:`
<p>The files saved.</p>
`,returnType:`
<p>A tuple of <code>str</code></p>
`}}),Nt=new z({props:{name:"save_vocabulary",anchor:"transformers.PreTrainedTokenizerBase.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2151",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.save_vocabulary.save_directory",description:`<strong>save_directory</strong> (<code>str</code>) —
The directory in which to save the vocabulary.`,name:"save_directory"},{anchor:"transformers.PreTrainedTokenizerBase.save_vocabulary.filename_prefix",description:`<strong>filename_prefix</strong> (<code>str</code>, <em>optional</em>) —
An optional prefix to add to the named of the saved files.`,name:"filename_prefix"}],returnDescription:`
<p>Paths to the files saved.</p>
`,returnType:`
<p><code>Tuple(str)</code></p>
`}}),Ft=new z({props:{name:"tokenize",anchor:"transformers.PreTrainedTokenizerBase.tokenize",parameters:[{name:"text",val:": str"},{name:"pair",val:": typing.Optional[str] = None"},{name:"add_special_tokens",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2169",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.tokenize.text",description:`<strong>text</strong> (<code>str</code>) —
The sequence to be encoded.`,name:"text"},{anchor:"transformers.PreTrainedTokenizerBase.tokenize.pair",description:`<strong>pair</strong> (<code>str</code>, <em>optional</em>) —
A second sequence to be encoded with the first.`,name:"pair"},{anchor:"transformers.PreTrainedTokenizerBase.tokenize.add_special_tokens",description:`<strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to add the special tokens associated with the corresponding model.`,name:"add_special_tokens"},{anchor:"transformers.PreTrainedTokenizerBase.tokenize.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific encode method. See details in
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__"><strong>call</strong>()</a>`,name:"kwargs"}],returnDescription:`
<p>The list of tokens.</p>
`,returnType:`
<p><code>List[str]</code></p>
`}}),Ct=new z({props:{name:"truncate_sequences",anchor:"transformers.PreTrainedTokenizerBase.truncate_sequences",parameters:[{name:"ids",val:": typing.List[int]"},{name:"pair_ids",val:": typing.Optional[typing.List[int]] = None"},{name:"num_tokens_to_remove",val:": int = 0"},{name:"truncation_strategy",val:": typing.Union[str, transformers.tokenization_utils_base.TruncationStrategy] = 'longest_first'"},{name:"stride",val:": int = 0"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3040",parametersDescription:[{anchor:"transformers.PreTrainedTokenizerBase.truncate_sequences.ids",description:`<strong>ids</strong> (<code>List[int]</code>) —
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the <code>tokenize</code> and
<code>convert_tokens_to_ids</code> methods.`,name:"ids"},{anchor:"transformers.PreTrainedTokenizerBase.truncate_sequences.pair_ids",description:`<strong>pair_ids</strong> (<code>List[int]</code>, <em>optional</em>) —
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the <code>tokenize</code>
and <code>convert_tokens_to_ids</code> methods.`,name:"pair_ids"},{anchor:"transformers.PreTrainedTokenizerBase.truncate_sequences.num_tokens_to_remove",description:`<strong>num_tokens_to_remove</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of tokens to remove using the truncation strategy.`,name:"num_tokens_to_remove"},{anchor:"transformers.PreTrainedTokenizerBase.truncate_sequences.truncation_strategy",description:`<strong>truncation_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
The strategy to follow for truncation. Can be:</p>
<ul>
<li><code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths greater
than the model maximum admissible input size).</li>
</ul>`,name:"truncation_strategy"},{anchor:"transformers.PreTrainedTokenizerBase.truncate_sequences.stride",description:`<strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a positive number, the overflowing tokens returned will contain some tokens from the main
sequence returned. The value of this argument defines the number of additional tokens.`,name:"stride"}],returnDescription:`
<p>The truncated <code>ids</code>, the truncated <code>pair_ids</code> and the list of
overflowing tokens. Note: The <em>longest_first</em> strategy returns empty list of overflowing tokens if a pair
of sequences (or a batch of pairs) is provided.</p>
`,returnType:`
<p><code>Tuple[List[int], List[int], List[int]]</code></p>
`}}),Ot=new gs({}),Wt=new z({props:{name:"class transformers.SpecialTokensMixin",anchor:"transformers.SpecialTokensMixin",parameters:[{name:"verbose",val:" = True"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L753",parametersDescription:[{anchor:"transformers.SpecialTokensMixin.bos_token",description:`<strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the beginning of a sentence.`,name:"bos_token"},{anchor:"transformers.SpecialTokensMixin.eos_token",description:`<strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the end of a sentence.`,name:"eos_token"},{anchor:"transformers.SpecialTokensMixin.unk_token",description:`<strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing an out-of-vocabulary token.`,name:"unk_token"},{anchor:"transformers.SpecialTokensMixin.sep_token",description:`<strong>sep_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token separating two different sentences in the same input (used by BERT for instance).`,name:"sep_token"},{anchor:"transformers.SpecialTokensMixin.pad_token",description:`<strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation.`,name:"pad_token"},{anchor:"transformers.SpecialTokensMixin.cls_token",description:`<strong>cls_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the class of the input (used by BERT for instance).`,name:"cls_token"},{anchor:"transformers.SpecialTokensMixin.mask_token",description:`<strong>mask_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT).`,name:"mask_token"},{anchor:"transformers.SpecialTokensMixin.additional_special_tokens",description:`<strong>additional_special_tokens</strong> (tuple or list of <code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A tuple or a list of additional special tokens.`,name:"additional_special_tokens"}]}}),jt=new z({props:{name:"add_special_tokens",anchor:"transformers.SpecialTokensMixin.add_special_tokens",parameters:[{name:"special_tokens_dict",val:": typing.Dict[str, typing.Union[str, tokenizers.AddedToken]]"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L833",parametersDescription:[{anchor:"transformers.SpecialTokensMixin.add_special_tokens.special_tokens_dict",description:`<strong>special_tokens_dict</strong> (dictionary <em>str</em> to <em>str</em> or <code>tokenizers.AddedToken</code>) —
Keys should be in the list of predefined special attributes: [<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>,
<code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>, <code>additional_special_tokens</code>].</p>
<p>Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer
assign the index of the <code>unk_token</code> to them).`,name:"special_tokens_dict"}],returnDescription:`
<p>Number of tokens added to the vocabulary.</p>
`,returnType:`
<p><code>int</code></p>
`}}),Ht=new _s({props:{code:`# Let's see how to add a new classification token to GPT-2
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2Model.from_pretrained("gpt2")
special_tokens_dict = {"cls_token": "<CLS>"}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print("We have added", num_added_toks, "tokens")
# Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.
model.resize_token_embeddings(len(tokenizer))
assert tokenizer.cls_token == "<CLS>"`,highlighted:`<span class="hljs-comment"># Let's see how to add a new classification token to GPT-2</span>
tokenizer = GPT2Tokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
model = GPT2Model.from_pretrained(<span class="hljs-string">"gpt2"</span>)
special_tokens_dict = {<span class="hljs-string">"cls_token"</span>: <span class="hljs-string">"<CLS>"</span>}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
<span class="hljs-built_in">print</span>(<span class="hljs-string">"We have added"</span>, num_added_toks, <span class="hljs-string">"tokens"</span>)
<span class="hljs-comment"># Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.</span>
model.resize_token_embeddings(<span class="hljs-built_in">len</span>(tokenizer))
<span class="hljs-keyword">assert</span> tokenizer.cls_token == <span class="hljs-string">"<CLS>"</span>`}}),Gt=new z({props:{name:"add_tokens",anchor:"transformers.SpecialTokensMixin.add_tokens",parameters:[{name:"new_tokens",val:": typing.Union[str, tokenizers.AddedToken, typing.List[typing.Union[str, tokenizers.AddedToken]]]"},{name:"special_tokens",val:": bool = False"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L905",parametersDescription:[{anchor:"transformers.SpecialTokensMixin.add_tokens.new_tokens",description:`<strong>new_tokens</strong> (<code>str</code>, <code>tokenizers.AddedToken</code> or a list of <em>str</em> or <code>tokenizers.AddedToken</code>) —
Tokens are only added if they are not already in the vocabulary. <code>tokenizers.AddedToken</code> wraps a string
token to let you personalize its behavior: whether this token should only match against a single word,
whether this token should strip all potential whitespaces on the left side, whether this token should
strip all potential whitespaces on the right side, etc.`,name:"new_tokens"},{anchor:"transformers.SpecialTokensMixin.add_tokens.special_tokens",description:`<strong>special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Can be used to specify if the token is a special token. This mostly change the normalization behavior
(special tokens like CLS or [MASK] are usually not lower-cased for instance).</p>
<p>See details for <code>tokenizers.AddedToken</code> in HuggingFace tokenizers library.`,name:"special_tokens"}],returnDescription:`
<p>Number of tokens added to the vocabulary.</p>
`,returnType:`
<p><code>int</code></p>
`}}),Yt=new _s({props:{code:`# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")
num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"])
print("We have added", num_added_toks, "tokens")
# Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.
model.resize_token_embeddings(len(tokenizer))`,highlighted:`<span class="hljs-comment"># Let's see how to increase the vocabulary of Bert model and tokenizer</span>
tokenizer = BertTokenizerFast.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
num_added_toks = tokenizer.add_tokens([<span class="hljs-string">"new_tok1"</span>, <span class="hljs-string">"my_new-tok2"</span>])
<span class="hljs-built_in">print</span>(<span class="hljs-string">"We have added"</span>, num_added_toks, <span class="hljs-string">"tokens"</span>)
<span class="hljs-comment"># Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.</span>
model.resize_token_embeddings(<span class="hljs-built_in">len</span>(tokenizer))`}}),Jt=new z({props:{name:"sanitize_special_tokens",anchor:"transformers.SpecialTokensMixin.sanitize_special_tokens",parameters:[],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L821",returnDescription:`
<p>The number of tokens added in the vocabulary during the operation.</p>
`,returnType:`
<p><code>int</code></p>
`}}),Kt=new gs({}),Qt=new z({props:{name:"class transformers.tokenization_utils_base.TruncationStrategy",anchor:"transformers.tokenization_utils_base.TruncationStrategy",parameters:[{name:"value",val:""},{name:"names",val:" = None"},{name:"module",val:" = None"},{name:"qualname",val:" = None"},{name:"type",val:" = None"},{name:"start",val:" = 1"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L129"}}),Zt=new z({props:{name:"class transformers.CharSpan",anchor:"transformers.CharSpan",parameters:[{name:"start",val:": int"},{name:"end",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L141",parametersDescription:[{anchor:"transformers.CharSpan.start",description:"<strong>start</strong> (<code>int</code>) — Index of the first character in the original string.",name:"start"},{anchor:"transformers.CharSpan.end",description:"<strong>end</strong> (<code>int</code>) — Index of the character following the last character in the original string.",name:"end"}]}}),eo=new z({props:{name:"class transformers.TokenSpan",anchor:"transformers.TokenSpan",parameters:[{name:"start",val:": int"},{name:"end",val:": int"}],source:"https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L154",parametersDescription:[{anchor:"transformers.TokenSpan.start",description:"<strong>start</strong> (<code>int</code>) — Index of the first token in the span.",name:"start"},{anchor:"transformers.TokenSpan.end",description:"<strong>end</strong> (<code>int</code>) — Index of the token following the last token in the span.",name:"end"}]}}),{c(){h=r("meta"),P=d(),v=r("h1"),y=r("a"),E=r("span"),u(w.$$.fragment),x=d(),B=r("span"),ge=o("Utilities for Tokenizers"),j=d(),D=r("p"),ro=o(`This page lists all the utility functions used by the tokenizers, mainly the class
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adding special tokens.`),di=d(),bn=r("p"),ci=o("This implementation does not add special tokens and this method should be overridden in a subclass."),li=d(),Ce=r("div"),u(ct.$$.fragment),pi=d(),vn=r("p"),mi=o("Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms."),hi=d(),Oe=r("div"),u(lt.$$.fragment),ui=d(),pt=r("p"),fi=o("Converts a sequence of tokens in a single string. The most simple way to do it is "),Tn=r("code"),_i=o('" ".join(tokens)'),gi=o(` but we
often want to remove sub-word tokenization artifacts at the same time.`),ki=d(),re=r("div"),u(mt.$$.fragment),bi=d(),uo=r("p"),vi=o("Create the token type IDs corresponding to the sequences passed. "),fo=r("a"),Ti=o(`What are token type
IDs?`),yi=d(),yn=r("p"),wi=o("Should be overridden in a subclass if the model has a special way of building those."),zi=d(),se=r("div"),u(ht.$$.fragment),xi=d(),wn=r("p"),$i=o(`Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.`),Pi=d(),ut=r("p"),qi=o("Similar to doing "),zn=r("code"),Ei=o("self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))"),Bi=o("."),Li=d(),ae=r("div"),u(ft.$$.fragment),Di=d(),xn=r("p"),Ii=o("Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary."),Ni=d(),_t=r("p"),Ai=o("Same as doing "),$n=r("code"),Fi=o("self.convert_tokens_to_ids(self.tokenize(text))"),Si=o("."),Ci=d(),ie=r("div"),u(gt.$$.fragment),Oi=d(),Pn=r("p"),Wi=o("Tokenize and prepare for the model a sequence or a pair of sequences."),ji=d(),u(We.$$.fragment),Mi=d(),C=r("div"),u(kt.$$.fragment),Ri=d(),bt=r("p"),Ui=o("Instantiate a "),_o=r("a"),Vi=o("PreTrainedTokenizerBase"),Hi=o(` (or a derived class) from a predefined
tokenizer.`),Gi=d(),u(je.$$.fragment),Xi=d(),qn=r("p"),Yi=o("Examples:"),Ji=d(),u(vt.$$.fragment),Ki=d(),Me=r("div"),u(Tt.$$.fragment),Qi=d(),ve=r("p"),Zi=o(`Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `),En=r("code"),ed=o("prepare_for_model"),td=o(" or "),Bn=r("code"),od=o("encode_plus"),nd=o(" methods."),rd=d(),de=r("div"),u(yt.$$.fragment),sd=d(),Ln=r("p"),ad=o("Returns the vocabulary as a dictionary of token to index."),id=d(),ce=r("p"),Dn=r("code"),dd=o("tokenizer.get_vocab()[token]"),cd=o(" is equivalent to "),In=r("code"),ld=o("tokenizer.convert_tokens_to_ids(token)"),pd=o(" when "),Nn=r("code"),md=o("token"),hd=o(` is in the
vocab.`),ud=d(),X=r("div"),u(wt.$$.fragment),fd=d(),An=r("p"),_d=o(`Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch.`),gd=d(),Q=r("p"),kd=o("Padding side (left/right) padding token ids are defined at the tokenizer level (with "),Fn=r("code"),bd=o("self.padding_side"),vd=o(`,
`),Sn=r("code"),Td=o("self.pad_token_id"),yd=o(" and "),Cn=r("code"),wd=o("self.pad_token_type_id"),zd=o(")"),xd=d(),u(Re.$$.fragment),$d=d(),Ue=r("div"),u(zt.$$.fragment),Pd=d(),M=r("p"),qd=o(`Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens. Please Note, for `),On=r("em"),Ed=o("pair_ids"),Bd=o(`
different than `),Wn=r("code"),Ld=o("None"),Dd=o(" and "),jn=r("em"),Id=o("truncation_strategy = longest_first"),Nd=o(" or "),Mn=r("code"),Ad=o("True"),Fd=o(`, it is not possible to return
overflowing tokens. Such a combination of arguments will raise an error.`),Sd=d(),Ve=r("div"),u(xt.$$.fragment),Cd=d(),Rn=r("p"),Od=o("Prepare model inputs for translation. For best performance, translate one sentence at a time."),Wd=d(),Y=r("div"),u($t.$$.fragment),jd=d(),Pt=r("p"),Md=o(`Upload the tokenizer files to the \u{1F917} Model Hub while synchronizing a local clone of the repo in
`),Un=r("code"),Rd=o("repo_path_or_name"),Ud=o("."),Vd=d(),Vn=r("p"),Hd=o("Examples:"),Gd=d(),u(qt.$$.fragment),Xd=d(),le=r("div"),u(Et.$$.fragment),Yd=d(),Bt=r("p"),Jd=o(`Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the
library are already mapped with `),Hn=r("code"),Kd=o("AutoTokenizer"),Qd=o("."),Zd=d(),u(He.$$.fragment),ec=d(),J=r("div"),u(Lt.$$.fragment),tc=d(),Gn=r("p"),oc=o("Save the full tokenizer state."),nc=d(),Dt=r("p"),rc=o(`This method make sure the full tokenizer can then be re-loaded using the
`),Xn=r("code"),sc=o("from_pretrained"),ac=o(" class method.."),ic=d(),It=r("p"),dc=o(`Warning,None This won\u2019t save modifications you may have applied to the tokenizer after the instantiation (for
instance, modifying `),Yn=r("code"),cc=o("tokenizer.do_lower_case"),lc=o(" after creation)."),pc=d(),pe=r("div"),u(Nt.$$.fragment),mc=d(),Jn=r("p"),hc=o("Save only the vocabulary of the tokenizer (vocabulary + added tokens)."),uc=d(),At=r("p"),fc=o(`This method won\u2019t save the configuration and special token mappings of the tokenizer. Use
`),Kn=r("code"),_c=o("_save_pretrained()"),gc=o("to save the whole state of the tokenizer."),kc=d(),Ge=r("div"),u(Ft.$$.fragment),bc=d(),St=r("p"),vc=o("Converts a string in a sequence of tokens, replacing unknown tokens with the "),Qn=r("code"),Tc=o("unk_token"),yc=o("."),wc=d(),Xe=r("div"),u(Ct.$$.fragment),zc=d(),Zn=r("p"),xc=o("Truncates a sequence pair in-place following the strategy."),Lr=d(),Te=r("h2"),Ye=r("a"),er=r("span"),u(Ot.$$.fragment),$c=d(),tr=r("span"),Pc=o("SpecialTokensMixin"),Dr=d(),A=r("div"),u(Wt.$$.fragment),qc=d(),ye=r("p"),Ec=o("A mixin derived by "),go=r("a"),Bc=o("PreTrainedTokenizer"),Lc=o(" and "),ko=r("a"),Dc=o("PreTrainedTokenizerFast"),Ic=o(` to handle specific behaviors related to
special tokens. In particular, this class hold the attributes which can be used to directly access these special
tokens in a model-independent manner and allow to set and update the special tokens.`),Nc=d(),q=r("div"),u(jt.$$.fragment),Ac=d(),or=r("p"),Fc=o(`Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If
special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the
current vocabulary).`),Sc=d(),nr=r("p"),Cc=o(`Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding
matrix of the model so that its embedding matrix matches the tokenizer.`),Oc=d(),Mt=r("p"),Wc=o("In order to do that, please use the "),bo=r("a"),jc=o("resize_token_embeddings()"),Mc=o(" method."),Rc=d(),Rt=r("p"),Uc=o("Using "),rr=r("code"),Vc=o("add_special_tokens"),Hc=o(" will ensure your special tokens can be used in several ways:"),Gc=d(),Ut=r("ul"),sr=r("li"),Xc=o("Special tokens are carefully handled by the tokenizer (they are never split)."),Yc=d(),Vt=r("li"),Jc=o("You can easily refer to special tokens using tokenizer class attributes like "),ar=r("code"),Kc=o("tokenizer.cls_token"),Qc=o(`. This
makes it easy to develop model-agnostic training and fine-tuning scripts.`),Zc=d(),R=r("p"),el=o(`When possible, special tokens are already registered for provided pretrained models (for instance
`),vo=r("a"),tl=o("BertTokenizer"),ol=d(),ir=r("code"),nl=o("cls_token"),rl=o(" is already registered to be :obj"),dr=r("em"),sl=o("\u2019[CLS]\u2019"),al=o(` and XLM\u2019s one is also registered to be
`),cr=r("code"),il=o("'</s>'"),dl=o(")."),cl=d(),lr=r("p"),ll=o("Examples:"),pl=d(),u(Ht.$$.fragment),ml=d(),N=r("div"),u(Gt.$$.fragment),hl=d(),pr=r("p"),ul=o(`Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary.`),fl=d(),mr=r("p"),_l=o(`Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding
matrix of the model so that its embedding matrix matches the tokenizer.`),gl=d(),Xt=r("p"),kl=o("In order to do that, please use the "),To=r("a"),bl=o("resize_token_embeddings()"),vl=o(" method."),Tl=d(),hr=r("p"),yl=o("Examples:"),wl=d(),u(Yt.$$.fragment),zl=d(),me=r("div"),u(Jt.$$.fragment),xl=d(),we=r("p"),$l=o("Make sure that all the special tokens attributes of the tokenizer ("),ur=r("code"),Pl=o("tokenizer.mask_token"),ql=o(`,
`),fr=r("code"),El=o("tokenizer.cls_token"),Bl=o(", etc.) are in the vocabulary."),Ll=d(),_r=r("p"),Dl=o("Add the missing ones to the vocabulary if needed."),Ir=d(),ze=r("h2"),Je=r("a"),gr=r("span"),u(Kt.$$.fragment),Il=d(),kr=r("span"),Nl=o("Enums and namedtuples"),Nr=d(),xe=r("div"),u(Qt.$$.fragment),Al=d(),$e=r("p"),Fl=o("Possible values for the "),br=r("code"),Sl=o("truncation"),Cl=o(" argument in "),Ke=r("a"),Ol=o("PreTrainedTokenizerBase."),vr=r("strong"),Wl=o("call"),jl=o("()"),Ml=o(`. Useful for tab-completion in
an IDE.`),Ar=d(),Pe=r("div"),u(Zt.$$.fragment),Rl=d(),Tr=r("p"),Ul=o("Character span in the original string."),Fr=d(),qe=r("div"),u(eo.$$.fragment),Vl=d(),yr=r("p"),Hl=o("Token span in an encoded string (list of tokens)."),this.h()},l(i){const T=eh('[data-svelte="svelte-1phssyn"]',document.head);h=s(T,"META",{name:!0,content:!0}),T.forEach(t),P=c(i),v=s(i,"H1",{class:!0});var to=a(v);y=s(to,"A",{id:!0,class:!0,href:!0});var wr=a(y);E=s(wr,"SPAN",{});var zr=a(E);f(w.$$.fragment,zr),zr.forEach(t),wr.forEach(t),x=c(to),B=s(to,"SPAN",{});var xr=a(B);ge=n(xr,"Utilities for Tokenizers"),xr.forEach(t),to.forEach(t),j=c(i),D=s(i,"P",{});var U=a(D);ro=n(U,`This page lists all the utility functions used by the tokenizers, mainly the class
`),so=s(U,"A",{href:!0});var Xl=a(so);ks=n(Xl,"PreTrainedTokenizerBase"),Xl.forEach(t),bs=n(U,` that implements the common methods between
`),ao=s(U,"A",{href:!0});var Yl=a(ao);vs=n(Yl,"PreTrainedTokenizer"),Yl.forEach(t),Ts=n(U," and "),io=s(U,"A",{href:!0});var Jl=a(io);ys=n(Jl,"PreTrainedTokenizerFast"),Jl.forEach(t),ws=n(U,` and the mixin
`),co=s(U,"A",{href:!0});var Kl=a(co);zs=n(Kl,"SpecialTokensMixin"),Kl.forEach(t),xs=n(U,"."),U.forEach(t),qr=c(i),lo=s(i,"P",{});var Ql=a(lo);$s=n(Ql,"Most of those are only useful if you are studying the code of the tokenizers in the library."),Ql.forEach(t),Er=c(i),ke=s(i,"H2",{class:!0});var Cr=a(ke);De=s(Cr,"A",{id:!0,class:!0,href:!0});var Zl=a(De);So=s(Zl,"SPAN",{});var ep=a(So);f(ot.$$.fragment,ep),ep.forEach(t),Zl.forEach(t),Ps=c(Cr),Co=s(Cr,"SPAN",{});var tp=a(Co);qs=n(tp,"PreTrainedTokenizerBase"),tp.forEach(t),Cr.forEach(t),Br=c(i),p=s(i,"DIV",{class:!0});var m=a(p);f(nt.$$.fragment,m),Es=c(m),be=s(m,"P",{});var yo=a(be);Bs=n(yo,"Base class for "),po=s(yo,"A",{href:!0});var op=a(po);Ls=n(op,"PreTrainedTokenizer"),op.forEach(t),Ds=n(yo," and "),mo=s(yo,"A",{href:!0});var np=a(mo);Is=n(np,"PreTrainedTokenizerFast"),np.forEach(t),Ns=n(yo,"."),yo.forEach(t),As=c(m),Oo=s(m,"P",{});var rp=a(Oo);Fs=n(rp,"Handles shared (mostly boiler plate) methods for those two classes."),rp.forEach(t),Ss=c(m),Wo=s(m,"P",{});var sp=a(Wo);Cs=n(sp,"Class attributes (overridden by derived classes)"),sp.forEach(t),Os=c(m),I=s(m,"UL",{});var O=a(I);te=s(O,"LI",{});var oo=a(te);jo=s(oo,"STRONG",{});var ap=a(jo);Ws=n(ap,"vocab_files_names"),ap.forEach(t),js=n(oo," ("),Mo=s(oo,"CODE",{});var ip=a(Mo);Ms=n(ip,"Dict[str, str]"),ip.forEach(t),Rs=n(oo,") \u2014 A dictionary with, as keys, the "),Ro=s(oo,"CODE",{});var dp=a(Ro);Us=n(dp,"__init__"),dp.forEach(t),Vs=n(oo,` keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).`),oo.forEach(t),Hs=c(O),F=s(O,"LI",{});var Z=a(F);Uo=s(Z,"STRONG",{});var cp=a(Uo);Gs=n(cp,"pretrained_vocab_files_map"),cp.forEach(t),Xs=n(Z," ("),Vo=s(Z,"CODE",{});var lp=a(Vo);Ys=n(lp,"Dict[str, Dict[str, str]]"),lp.forEach(t),Js=n(Z,`) \u2014 A dictionary of dictionaries, with the
high-level keys being the `),Ho=s(Z,"CODE",{});var pp=a(Ho);Ks=n(pp,"__init__"),pp.forEach(t),Qs=n(Z,` keyword name of each vocabulary file required by the model, the
low-level being the `),Go=s(Z,"CODE",{});var mp=a(Go);Zs=n(mp,"short-cut-names"),mp.forEach(t),ea=n(Z," of the pretrained models with, as associated values, the "),Xo=s(Z,"CODE",{});var hp=a(Xo);ta=n(hp,"url"),hp.forEach(t),oa=n(Z,` to the
associated pretrained vocabulary file.`),Z.forEach(t),na=c(O),V=s(O,"LI",{});var Ee=a(V);Yo=s(Ee,"STRONG",{});var up=a(Yo);ra=n(up,"max_model_input_sizes"),up.forEach(t),sa=n(Ee," ("),Jo=s(Ee,"CODE",{});var fp=a(Jo);aa=n(fp,"Dict[str, Optional[int]]"),fp.forEach(t),ia=n(Ee,") \u2014 A dictionary with, as keys, the "),Ko=s(Ee,"CODE",{});var _p=a(Ko);da=n(_p,"short-cut-names"),_p.forEach(t),ca=n(Ee,`
of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model,
or `),Qo=s(Ee,"CODE",{});var gp=a(Qo);la=n(gp,"None"),gp.forEach(t),pa=n(Ee," if the model has no maximum input size."),Ee.forEach(t),ma=c(O),S=s(O,"LI",{});var ee=a(S);Zo=s(ee,"STRONG",{});var kp=a(Zo);ha=n(kp,"pretrained_init_configuration"),kp.forEach(t),ua=n(ee," ("),en=s(ee,"CODE",{});var bp=a(en);fa=n(bp,"Dict[str, Dict[str, Any]]"),bp.forEach(t),_a=n(ee,`) \u2014 A dictionary with, as keys, the
`),tn=s(ee,"CODE",{});var vp=a(tn);ga=n(vp,"short-cut-names"),vp.forEach(t),ka=n(ee,` of the pretrained models, and as associated values, a dictionary of specific arguments to
pass to the `),on=s(ee,"CODE",{});var Tp=a(on);ba=n(Tp,"__init__"),Tp.forEach(t),va=n(ee,` method of the tokenizer class for this pretrained model when loading the tokenizer
with the `),ho=s(ee,"A",{href:!0});var yp=a(ho);Ta=n(yp,"from_pretrained()"),yp.forEach(t),ya=n(ee," method."),ee.forEach(t),wa=c(O),Ie=s(O,"LI",{});var $r=a(Ie);nn=s($r,"STRONG",{});var wp=a(nn);za=n(wp,"model_input_names"),wp.forEach(t),xa=n($r," ("),rn=s($r,"CODE",{});var zp=a(rn);$a=n(zp,"List[str]"),zp.forEach(t),Pa=n($r,") \u2014 A list of inputs expected in the forward pass of the model."),$r.forEach(t),qa=c(O),H=s(O,"LI",{});var Be=a(H);sn=s(Be,"STRONG",{});var xp=a(sn);Ea=n(xp,"padding_side"),xp.forEach(t),Ba=n(Be," ("),an=s(Be,"CODE",{});var $p=a(an);La=n($p,"str"),$p.forEach(t),Da=n(Be,`) \u2014 The default value for the side on which the model should have padding applied.
Should be `),dn=s(Be,"CODE",{});var Pp=a(dn);Ia=n(Pp,"'right'"),Pp.forEach(t),Na=n(Be," or "),cn=s(Be,"CODE",{});var qp=a(cn);Aa=n(qp,"'left'"),qp.forEach(t),Fa=n(Be,"."),Be.forEach(t),Sa=c(O),G=s(O,"LI",{});var Le=a(G);ln=s(Le,"STRONG",{});var Ep=a(ln);Ca=n(Ep,"truncation_side"),Ep.forEach(t),Oa=n(Le," ("),pn=s(Le,"CODE",{});var Bp=a(pn);Wa=n(Bp,"str"),Bp.forEach(t),ja=n(Le,`) \u2014 The default value for the side on which the model should have truncation
applied. Should be `),mn=s(Le,"CODE",{});var Lp=a(mn);Ma=n(Lp,"'right'"),Lp.forEach(t),Ra=n(Le," or "),hn=s(Le,"CODE",{});var Dp=a(hn);Ua=n(Dp,"'left'"),Dp.forEach(t),Va=n(Le,"."),Le.forEach(t),O.forEach(t),Ha=c(m),Ne=s(m,"DIV",{class:!0});var Or=a(Ne);f(rt.$$.fragment,Or),Ga=c(Or),un=s(Or,"P",{});var Ip=a(un);Xa=n(Ip,`Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.`),Ip.forEach(t),Or.forEach(t),Ya=c(m),Ae=s(m,"DIV",{class:!0});var Wr=a(Ae);f(st.$$.fragment,Wr),Ja=c(Wr),fn=s(Wr,"P",{});var Np=a(fn);Ka=n(Np,`Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.`),Np.forEach(t),Wr.forEach(t),Qa=c(m),Fe=s(m,"DIV",{class:!0});var jr=a(Fe);f(at.$$.fragment,jr),Za=c(jr),_n=s(jr,"P",{});var Ap=a(_n);ei=n(Ap,"Convert a list of lists of token ids into a list of strings by calling decode."),Ap.forEach(t),jr.forEach(t),ti=c(m),oe=s(m,"DIV",{class:!0});var wo=a(oe);f(it.$$.fragment,wo),oi=c(wo),gn=s(wo,"P",{});var Fp=a(gn);ni=n(Fp,"Tokenize and prepare for the model a list of sequences or a list of pairs of sequences."),Fp.forEach(t),ri=c(wo),f(Se.$$.fragment,wo),wo.forEach(t),si=c(m),ne=s(m,"DIV",{class:!0});var zo=a(ne);f(dt.$$.fragment,zo),ai=c(zo),kn=s(zo,"P",{});var Sp=a(kn);ii=n(Sp,`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens.`),Sp.forEach(t),di=c(zo),bn=s(zo,"P",{});var Cp=a(bn);ci=n(Cp,"This implementation does not add special tokens and this method should be overridden in a subclass."),Cp.forEach(t),zo.forEach(t),li=c(m),Ce=s(m,"DIV",{class:!0});var Mr=a(Ce);f(ct.$$.fragment,Mr),pi=c(Mr),vn=s(Mr,"P",{});var Op=a(vn);mi=n(Op,"Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms."),Op.forEach(t),Mr.forEach(t),hi=c(m),Oe=s(m,"DIV",{class:!0});var Rr=a(Oe);f(lt.$$.fragment,Rr),ui=c(Rr),pt=s(Rr,"P",{});var Ur=a(pt);fi=n(Ur,"Converts a sequence of tokens in a single string. The most simple way to do it is "),Tn=s(Ur,"CODE",{});var Wp=a(Tn);_i=n(Wp,'" ".join(tokens)'),Wp.forEach(t),gi=n(Ur,` but we
often want to remove sub-word tokenization artifacts at the same time.`),Ur.forEach(t),Rr.forEach(t),ki=c(m),re=s(m,"DIV",{class:!0});var xo=a(re);f(mt.$$.fragment,xo),bi=c(xo),uo=s(xo,"P",{});var Gl=a(uo);vi=n(Gl,"Create the token type IDs corresponding to the sequences passed. "),fo=s(Gl,"A",{href:!0});var jp=a(fo);Ti=n(jp,`What are token type
IDs?`),jp.forEach(t),Gl.forEach(t),yi=c(xo),yn=s(xo,"P",{});var Mp=a(yn);wi=n(Mp,"Should be overridden in a subclass if the model has a special way of building those."),Mp.forEach(t),xo.forEach(t),zi=c(m),se=s(m,"DIV",{class:!0});var $o=a(se);f(ht.$$.fragment,$o),xi=c($o),wn=s($o,"P",{});var Rp=a(wn);$i=n(Rp,`Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.`),Rp.forEach(t),Pi=c($o),ut=s($o,"P",{});var Vr=a(ut);qi=n(Vr,"Similar to doing "),zn=s(Vr,"CODE",{});var Up=a(zn);Ei=n(Up,"self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))"),Up.forEach(t),Bi=n(Vr,"."),Vr.forEach(t),$o.forEach(t),Li=c(m),ae=s(m,"DIV",{class:!0});var Po=a(ae);f(ft.$$.fragment,Po),Di=c(Po),xn=s(Po,"P",{});var Vp=a(xn);Ii=n(Vp,"Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary."),Vp.forEach(t),Ni=c(Po),_t=s(Po,"P",{});var Hr=a(_t);Ai=n(Hr,"Same as doing "),$n=s(Hr,"CODE",{});var Hp=a($n);Fi=n(Hp,"self.convert_tokens_to_ids(self.tokenize(text))"),Hp.forEach(t),Si=n(Hr,"."),Hr.forEach(t),Po.forEach(t),Ci=c(m),ie=s(m,"DIV",{class:!0});var qo=a(ie);f(gt.$$.fragment,qo),Oi=c(qo),Pn=s(qo,"P",{});var Gp=a(Pn);Wi=n(Gp,"Tokenize and prepare for the model a sequence or a pair of sequences."),Gp.forEach(t),ji=c(qo),f(We.$$.fragment,qo),qo.forEach(t),Mi=c(m),C=s(m,"DIV",{class:!0});var he=a(C);f(kt.$$.fragment,he),Ri=c(he),bt=s(he,"P",{});var Gr=a(bt);Ui=n(Gr,"Instantiate a "),_o=s(Gr,"A",{href:!0});var Xp=a(_o);Vi=n(Xp,"PreTrainedTokenizerBase"),Xp.forEach(t),Hi=n(Gr,` (or a derived class) from a predefined
tokenizer.`),Gr.forEach(t),Gi=c(he),f(je.$$.fragment,he),Xi=c(he),qn=s(he,"P",{});var Yp=a(qn);Yi=n(Yp,"Examples:"),Yp.forEach(t),Ji=c(he),f(vt.$$.fragment,he),he.forEach(t),Ki=c(m),Me=s(m,"DIV",{class:!0});var Xr=a(Me);f(Tt.$$.fragment,Xr),Qi=c(Xr),ve=s(Xr,"P",{});var Eo=a(ve);Zi=n(Eo,`Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `),En=s(Eo,"CODE",{});var Jp=a(En);ed=n(Jp,"prepare_for_model"),Jp.forEach(t),td=n(Eo," or "),Bn=s(Eo,"CODE",{});var Kp=a(Bn);od=n(Kp,"encode_plus"),Kp.forEach(t),nd=n(Eo," methods."),Eo.forEach(t),Xr.forEach(t),rd=c(m),de=s(m,"DIV",{class:!0});var Bo=a(de);f(yt.$$.fragment,Bo),sd=c(Bo),Ln=s(Bo,"P",{});var Qp=a(Ln);ad=n(Qp,"Returns the vocabulary as a dictionary of token to index."),Qp.forEach(t),id=c(Bo),ce=s(Bo,"P",{});var no=a(ce);Dn=s(no,"CODE",{});var Zp=a(Dn);dd=n(Zp,"tokenizer.get_vocab()[token]"),Zp.forEach(t),cd=n(no," is equivalent to "),In=s(no,"CODE",{});var em=a(In);ld=n(em,"tokenizer.convert_tokens_to_ids(token)"),em.forEach(t),pd=n(no," when "),Nn=s(no,"CODE",{});var tm=a(Nn);md=n(tm,"token"),tm.forEach(t),hd=n(no,` is in the
vocab.`),no.forEach(t),Bo.forEach(t),ud=c(m),X=s(m,"DIV",{class:!0});var Qe=a(X);f(wt.$$.fragment,Qe),fd=c(Qe),An=s(Qe,"P",{});var om=a(An);_d=n(om,`Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch.`),om.forEach(t),gd=c(Qe),Q=s(Qe,"P",{});var Ze=a(Q);kd=n(Ze,"Padding side (left/right) padding token ids are defined at the tokenizer level (with "),Fn=s(Ze,"CODE",{});var nm=a(Fn);bd=n(nm,"self.padding_side"),nm.forEach(t),vd=n(Ze,`,
`),Sn=s(Ze,"CODE",{});var rm=a(Sn);Td=n(rm,"self.pad_token_id"),rm.forEach(t),yd=n(Ze," and "),Cn=s(Ze,"CODE",{});var sm=a(Cn);wd=n(sm,"self.pad_token_type_id"),sm.forEach(t),zd=n(Ze,")"),Ze.forEach(t),xd=c(Qe),f(Re.$$.fragment,Qe),Qe.forEach(t),$d=c(m),Ue=s(m,"DIV",{class:!0});var Yr=a(Ue);f(zt.$$.fragment,Yr),Pd=c(Yr),M=s(Yr,"P",{});var ue=a(M);qd=n(ue,`Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens. Please Note, for `),On=s(ue,"EM",{});var am=a(On);Ed=n(am,"pair_ids"),am.forEach(t),Bd=n(ue,`
different than `),Wn=s(ue,"CODE",{});var im=a(Wn);Ld=n(im,"None"),im.forEach(t),Dd=n(ue," and "),jn=s(ue,"EM",{});var dm=a(jn);Id=n(dm,"truncation_strategy = longest_first"),dm.forEach(t),Nd=n(ue," or "),Mn=s(ue,"CODE",{});var cm=a(Mn);Ad=n(cm,"True"),cm.forEach(t),Fd=n(ue,`, it is not possible to return
overflowing tokens. Such a combination of arguments will raise an error.`),ue.forEach(t),Yr.forEach(t),Sd=c(m),Ve=s(m,"DIV",{class:!0});var Jr=a(Ve);f(xt.$$.fragment,Jr),Cd=c(Jr),Rn=s(Jr,"P",{});var lm=a(Rn);Od=n(lm,"Prepare model inputs for translation. For best performance, translate one sentence at a time."),lm.forEach(t),Jr.forEach(t),Wd=c(m),Y=s(m,"DIV",{class:!0});var et=a(Y);f($t.$$.fragment,et),jd=c(et),Pt=s(et,"P",{});var Kr=a(Pt);Md=n(Kr,`Upload the tokenizer files to the \u{1F917} Model Hub while synchronizing a local clone of the repo in
`),Un=s(Kr,"CODE",{});var pm=a(Un);Rd=n(pm,"repo_path_or_name"),pm.forEach(t),Ud=n(Kr,"."),Kr.forEach(t),Vd=c(et),Vn=s(et,"P",{});var mm=a(Vn);Hd=n(mm,"Examples:"),mm.forEach(t),Gd=c(et),f(qt.$$.fragment,et),et.forEach(t),Xd=c(m),le=s(m,"DIV",{class:!0});var Lo=a(le);f(Et.$$.fragment,Lo),Yd=c(Lo),Bt=s(Lo,"P",{});var Qr=a(Bt);Jd=n(Qr,`Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the
library are already mapped with `),Hn=s(Qr,"CODE",{});var hm=a(Hn);Kd=n(hm,"AutoTokenizer"),hm.forEach(t),Qd=n(Qr,"."),Qr.forEach(t),Zd=c(Lo),f(He.$$.fragment,Lo),Lo.forEach(t),ec=c(m),J=s(m,"DIV",{class:!0});var tt=a(J);f(Lt.$$.fragment,tt),tc=c(tt),Gn=s(tt,"P",{});var um=a(Gn);oc=n(um,"Save the full tokenizer state."),um.forEach(t),nc=c(tt),Dt=s(tt,"P",{});var Zr=a(Dt);rc=n(Zr,`This method make sure the full tokenizer can then be re-loaded using the
`),Xn=s(Zr,"CODE",{});var fm=a(Xn);sc=n(fm,"from_pretrained"),fm.forEach(t),ac=n(Zr," class method.."),Zr.forEach(t),ic=c(tt),It=s(tt,"P",{});var es=a(It);dc=n(es,`Warning,None This won\u2019t save modifications you may have applied to the tokenizer after the instantiation (for
instance, modifying `),Yn=s(es,"CODE",{});var _m=a(Yn);cc=n(_m,"tokenizer.do_lower_case"),_m.forEach(t),lc=n(es," after creation)."),es.forEach(t),tt.forEach(t),pc=c(m),pe=s(m,"DIV",{class:!0});var Do=a(pe);f(Nt.$$.fragment,Do),mc=c(Do),Jn=s(Do,"P",{});var gm=a(Jn);hc=n(gm,"Save only the vocabulary of the tokenizer (vocabulary + added tokens)."),gm.forEach(t),uc=c(Do),At=s(Do,"P",{});var ts=a(At);fc=n(ts,`This method won\u2019t save the configuration and special token mappings of the tokenizer. Use
`),Kn=s(ts,"CODE",{});var km=a(Kn);_c=n(km,"_save_pretrained()"),km.forEach(t),gc=n(ts,"to save the whole state of the tokenizer."),ts.forEach(t),Do.forEach(t),kc=c(m),Ge=s(m,"DIV",{class:!0});var os=a(Ge);f(Ft.$$.fragment,os),bc=c(os),St=s(os,"P",{});var ns=a(St);vc=n(ns,"Converts a string in a sequence of tokens, replacing unknown tokens with the "),Qn=s(ns,"CODE",{});var bm=a(Qn);Tc=n(bm,"unk_token"),bm.forEach(t),yc=n(ns,"."),ns.forEach(t),os.forEach(t),wc=c(m),Xe=s(m,"DIV",{class:!0});var rs=a(Xe);f(Ct.$$.fragment,rs),zc=c(rs),Zn=s(rs,"P",{});var vm=a(Zn);xc=n(vm,"Truncates a sequence pair in-place following the strategy."),vm.forEach(t),rs.forEach(t),m.forEach(t),Lr=c(i),Te=s(i,"H2",{class:!0});var ss=a(Te);Ye=s(ss,"A",{id:!0,class:!0,href:!0});var Tm=a(Ye);er=s(Tm,"SPAN",{});var ym=a(er);f(Ot.$$.fragment,ym),ym.forEach(t),Tm.forEach(t),$c=c(ss),tr=s(ss,"SPAN",{});var wm=a(tr);Pc=n(wm,"SpecialTokensMixin"),wm.forEach(t),ss.forEach(t),Dr=c(i),A=s(i,"DIV",{class:!0});var fe=a(A);f(Wt.$$.fragment,fe),qc=c(fe),ye=s(fe,"P",{});var Io=a(ye);Ec=n(Io,"A mixin derived by "),go=s(Io,"A",{href:!0});var zm=a(go);Bc=n(zm,"PreTrainedTokenizer"),zm.forEach(t),Lc=n(Io," and "),ko=s(Io,"A",{href:!0});var xm=a(ko);Dc=n(xm,"PreTrainedTokenizerFast"),xm.forEach(t),Ic=n(Io,` to handle specific behaviors related to
special tokens. In particular, this class hold the attributes which can be used to directly access these special
tokens in a model-independent manner and allow to set and update the special tokens.`),Io.forEach(t),Nc=c(fe),q=s(fe,"DIV",{class:!0});var L=a(q);f(jt.$$.fragment,L),Ac=c(L),or=s(L,"P",{});var $m=a(or);Fc=n($m,`Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If
special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the
current vocabulary).`),$m.forEach(t),Sc=c(L),nr=s(L,"P",{});var Pm=a(nr);Cc=n(Pm,`Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding
matrix of the model so that its embedding matrix matches the tokenizer.`),Pm.forEach(t),Oc=c(L),Mt=s(L,"P",{});var as=a(Mt);Wc=n(as,"In order to do that, please use the "),bo=s(as,"A",{href:!0});var qm=a(bo);jc=n(qm,"resize_token_embeddings()"),qm.forEach(t),Mc=n(as," method."),as.forEach(t),Rc=c(L),Rt=s(L,"P",{});var is=a(Rt);Uc=n(is,"Using "),rr=s(is,"CODE",{});var Em=a(rr);Vc=n(Em,"add_special_tokens"),Em.forEach(t),Hc=n(is," will ensure your special tokens can be used in several ways:"),is.forEach(t),Gc=c(L),Ut=s(L,"UL",{});var ds=a(Ut);sr=s(ds,"LI",{});var Bm=a(sr);Xc=n(Bm,"Special tokens are carefully handled by the tokenizer (they are never split)."),Bm.forEach(t),Yc=c(ds),Vt=s(ds,"LI",{});var cs=a(Vt);Jc=n(cs,"You can easily refer to special tokens using tokenizer class attributes like "),ar=s(cs,"CODE",{});var Lm=a(ar);Kc=n(Lm,"tokenizer.cls_token"),Lm.forEach(t),Qc=n(cs,`. This
makes it easy to develop model-agnostic training and fine-tuning scripts.`),cs.forEach(t),ds.forEach(t),Zc=c(L),R=s(L,"P",{});var _e=a(R);el=n(_e,`When possible, special tokens are already registered for provided pretrained models (for instance
`),vo=s(_e,"A",{href:!0});var Dm=a(vo);tl=n(Dm,"BertTokenizer"),Dm.forEach(t),ol=c(_e),ir=s(_e,"CODE",{});var Im=a(ir);nl=n(Im,"cls_token"),Im.forEach(t),rl=n(_e," is already registered to be :obj"),dr=s(_e,"EM",{});var Nm=a(dr);sl=n(Nm,"\u2019[CLS]\u2019"),Nm.forEach(t),al=n(_e,` and XLM\u2019s one is also registered to be
`),cr=s(_e,"CODE",{});var Am=a(cr);il=n(Am,"'</s>'"),Am.forEach(t),dl=n(_e,")."),_e.forEach(t),cl=c(L),lr=s(L,"P",{});var Fm=a(lr);ll=n(Fm,"Examples:"),Fm.forEach(t),pl=c(L),f(Ht.$$.fragment,L),L.forEach(t),ml=c(fe),N=s(fe,"DIV",{class:!0});var K=a(N);f(Gt.$$.fragment,K),hl=c(K),pr=s(K,"P",{});var Sm=a(pr);ul=n(Sm,`Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary.`),Sm.forEach(t),fl=c(K),mr=s(K,"P",{});var Cm=a(mr);_l=n(Cm,`Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding
matrix of the model so that its embedding matrix matches the tokenizer.`),Cm.forEach(t),gl=c(K),Xt=s(K,"P",{});var ls=a(Xt);kl=n(ls,"In order to do that, please use the "),To=s(ls,"A",{href:!0});var Om=a(To);bl=n(Om,"resize_token_embeddings()"),Om.forEach(t),vl=n(ls," method."),ls.forEach(t),Tl=c(K),hr=s(K,"P",{});var Wm=a(hr);yl=n(Wm,"Examples:"),Wm.forEach(t),wl=c(K),f(Yt.$$.fragment,K),K.forEach(t),zl=c(fe),me=s(fe,"DIV",{class:!0});var No=a(me);f(Jt.$$.fragment,No),xl=c(No),we=s(No,"P",{});var Ao=a(we);$l=n(Ao,"Make sure that all the special tokens attributes of the tokenizer ("),ur=s(Ao,"CODE",{});var jm=a(ur);Pl=n(jm,"tokenizer.mask_token"),jm.forEach(t),ql=n(Ao,`,
`),fr=s(Ao,"CODE",{});var Mm=a(fr);El=n(Mm,"tokenizer.cls_token"),Mm.forEach(t),Bl=n(Ao,", etc.) are in the vocabulary."),Ao.forEach(t),Ll=c(No),_r=s(No,"P",{});var Rm=a(_r);Dl=n(Rm,"Add the missing ones to the vocabulary if needed."),Rm.forEach(t),No.forEach(t),fe.forEach(t),Ir=c(i),ze=s(i,"H2",{class:!0});var ps=a(ze);Je=s(ps,"A",{id:!0,class:!0,href:!0});var Um=a(Je);gr=s(Um,"SPAN",{});var Vm=a(gr);f(Kt.$$.fragment,Vm),Vm.forEach(t),Um.forEach(t),Il=c(ps),kr=s(ps,"SPAN",{});var Hm=a(kr);Nl=n(Hm,"Enums and namedtuples"),Hm.forEach(t),ps.forEach(t),Nr=c(i),xe=s(i,"DIV",{class:!0});var ms=a(xe);f(Qt.$$.fragment,ms),Al=c(ms),$e=s(ms,"P",{});var Fo=a($e);Fl=n(Fo,"Possible values for the "),br=s(Fo,"CODE",{});var Gm=a(br);Sl=n(Gm,"truncation"),Gm.forEach(t),Cl=n(Fo," argument in "),Ke=s(Fo,"A",{href:!0});var hs=a(Ke);Ol=n(hs,"PreTrainedTokenizerBase."),vr=s(hs,"STRONG",{});var Xm=a(vr);Wl=n(Xm,"call"),Xm.forEach(t),jl=n(hs,"()"),hs.forEach(t),Ml=n(Fo,`. Useful for tab-completion in
an IDE.`),Fo.forEach(t),ms.forEach(t),Ar=c(i),Pe=s(i,"DIV",{class:!0});var us=a(Pe);f(Zt.$$.fragment,us),Rl=c(us),Tr=s(us,"P",{});var Ym=a(Tr);Ul=n(Ym,"Character span in the original string."),Ym.forEach(t),us.forEach(t),Fr=c(i),qe=s(i,"DIV",{class:!0});var fs=a(qe);f(eo.$$.fragment,fs),Vl=c(fs),yr=s(fs,"P",{});var Jm=a(yr);Hl=n(Jm,"Token span in an encoded string (list of tokens)."),Jm.forEach(t),fs.forEach(t),this.h()},h(){l(h,"name","hf:doc:metadata"),l(h,"content",JSON.stringify(ih)),l(y,"id","utilities-for-tokenizers"),l(y,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),l(y,"href","#utilities-for-tokenizers"),l(v,"class","relative 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<h1 class="relative group"><a id="data-collator" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#data-collator"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Data Collator
</span></h1>
<p>Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
the same type as the elements of <code>train_dataset</code> or <code>eval_dataset</code>.</p>
<p>To be able to build batches, data collators may apply some processing (like padding). Some of them (like
<a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorForLanguageModeling">DataCollatorForLanguageModeling</a>) also apply some random data augmentation (like random masking)
on the formed batch.</p>
<p>Examples of use can be found in the <a href="../examples">example scripts</a> or <a href="../notebooks">example notebooks</a>.</p>
<h2 class="relative group"><a id="transformers.default_data_collator" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.default_data_collator"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Default data collator
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.default_data_collator"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.default_data_collator</span></h4><!-- HTML_TAG_END -->
<a id="transformers.default_data_collator" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.default_data_collator"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L48" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">features<span class="opacity-60">: typing.List[InputDataClass]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60"> = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:</p>
<ul><li><code>label</code>: handles a single value (int or float) per object</li>
<li><code>label_ids</code>: handles a list of values per object</li></ul>
<p>Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it’s useful.</p></div>
<h2 class="relative group"><a id="transformers.DefaultDataCollator" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DefaultDataCollator"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DefaultDataCollator
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DefaultDataCollator"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DefaultDataCollator</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DefaultDataCollator" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DefaultDataCollator"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L74" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DefaultDataCollator.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DefaultDataCollator.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code>) —
The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Very simple data collator that simply collates batches of dict-like objects and performs special handling for
potential keys named:</p>
<ul><li><code>label</code>: handles a single value (int or float) per object</li>
<li><code>label_ids</code>: handles a list of values per object</li></ul>
<p>Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
to the model. See glue and ner for example of how it’s useful.</p>
<p>This is an object (like other data collators) rather than a pure function like default_data_collator. This can be
helpful if you need to set a return_tensors value at initialization.</p></div>
<h2 class="relative group"><a id="transformers.DataCollatorWithPadding" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorWithPadding"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DataCollatorWithPadding
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorWithPadding"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DataCollatorWithPadding</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorWithPadding" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorWithPadding"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L212" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizerBase</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorWithPadding.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorWithPadding.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) —
The tokenizer used for encoding the data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorWithPadding.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorWithPadding.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Select a strategy to pad the returned sequences (according to the model’s padding side and padding index)
among:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorWithPadding.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorWithPadding.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Maximum length of the returned list and optionally padding length (see above).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorWithPadding.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorWithPadding.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorWithPadding.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorWithPadding.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code>) —
The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Data collator that will dynamically pad the inputs received.</p></div>
<h2 class="relative group"><a id="transformers.DataCollatorForTokenClassification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForTokenClassification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DataCollatorForTokenClassification
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForTokenClassification"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DataCollatorForTokenClassification</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForTokenClassification" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForTokenClassification"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L264" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizerBase</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_pad_token_id<span class="opacity-60">: int = -100</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForTokenClassification.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForTokenClassification.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) —
The tokenizer used for encoding the data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForTokenClassification.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForTokenClassification.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Select a strategy to pad the returned sequences (according to the model’s padding side and padding index)
among:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForTokenClassification.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForTokenClassification.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Maximum length of the returned list and optionally padding length (see above).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForTokenClassification.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForTokenClassification.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForTokenClassification.label_pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForTokenClassification.label_pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to -100) —
The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForTokenClassification.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForTokenClassification.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code>) —
The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Data collator that will dynamically pad the inputs received, as well as the labels.</p></div>
<h2 class="relative group"><a id="transformers.DataCollatorForSeq2Seq" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DataCollatorForSeq2Seq
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForSeq2Seq"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DataCollatorForSeq2Seq</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForSeq2Seq" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForSeq2Seq"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L514" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizerBase</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Optional[typing.Any] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_pad_token_id<span class="opacity-60">: int = -100</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForSeq2Seq.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) —
The tokenizer used for encoding the data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForSeq2Seq.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>) —
The model that is being trained. If set and has the <em>prepare_decoder_input_ids_from_labels</em>, use it to
prepare the <em>decoder_input_ids</em></p>
<p>This is useful when using <em>label_smoothing</em> to avoid calculating loss twice.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForSeq2Seq.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Select a strategy to pad the returned sequences (according to the model’s padding side and padding index)
among:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single sequence
is provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForSeq2Seq.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Maximum length of the returned list and optionally padding length (see above).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForSeq2Seq.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForSeq2Seq.label_pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq.label_pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to -100) —
The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForSeq2Seq.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForSeq2Seq.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code>) —
The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Data collator that will dynamically pad the inputs received, as well as the labels.</p></div>
<h2 class="relative group"><a id="transformers.DataCollatorForLanguageModeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForLanguageModeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DataCollatorForLanguageModeling
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForLanguageModeling"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DataCollatorForLanguageModeling</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForLanguageModeling" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForLanguageModeling"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L607" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizerBase</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mlm<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mlm_probability<span class="opacity-60">: float = 0.15</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tf_experimental_compile<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForLanguageModeling.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForLanguageModeling.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>) —
The tokenizer used for encoding the data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForLanguageModeling.mlm" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForLanguageModeling.mlm"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mlm</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to use masked language modeling. If set to <code>False</code>, the labels are the same as the inputs
with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for non-masked
tokens and the value to predict for the masked token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForLanguageModeling.mlm_probability" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForLanguageModeling.mlm_probability"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mlm_probability</strong> (<code>float</code>, <em>optional</em>, defaults to 0.15) —
The probability with which to (randomly) mask tokens in the input, when <code>mlm</code> is set to <code>True</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForLanguageModeling.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForLanguageModeling.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DataCollatorForLanguageModeling.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForLanguageModeling.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code>) —
The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For best performance, this data collator should be used with a dataset having items that are dictionaries or
BatchEncoding, with the <code>"special_tokens_mask"</code> key, as returned by a <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> or a
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> with the argument <code>return_special_tokens_mask=True</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForLanguageModeling.numpy_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>numpy_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForLanguageModeling.numpy_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForLanguageModeling.numpy_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L805" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens_mask<span class="opacity-60">: typing.Optional[typing.Any] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForLanguageModeling.tf_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tf_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForLanguageModeling.tf_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForLanguageModeling.tf_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L659" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">vocab_size<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask_token_id<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens_mask<span class="opacity-60">: typing.Optional[typing.Any] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForLanguageModeling.torch_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>torch_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForLanguageModeling.torch_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForLanguageModeling.torch_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L748" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens_mask<span class="opacity-60">: typing.Optional[typing.Any] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.</p></div></div>
<h2 class="relative group"><a id="transformers.DataCollatorForWholeWordMask" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForWholeWordMask"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DataCollatorForWholeWordMask
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForWholeWordMask"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DataCollatorForWholeWordMask</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForWholeWordMask" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForWholeWordMask"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L846" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizerBase</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mlm<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mlm_probability<span class="opacity-60">: float = 0.15</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tf_experimental_compile<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Data collator used for language modeling that masks entire words.</p>
<ul><li>collates batches of tensors, honoring their tokenizer’s pad_token</li>
<li>preprocesses batches for masked language modeling</li></ul>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>This collator relies on details of the implementation of subword tokenization by <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>, specifically
that subword tokens are prefixed with <em>##</em>. For tokenizers that do not adhere to this scheme, this collator will
produce an output that is roughly equivalent to <code>.DataCollatorForLanguageModeling</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForWholeWordMask.numpy_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>numpy_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForWholeWordMask.numpy_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForWholeWordMask.numpy_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L1072" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask_labels<span class="opacity-60">: typing.Any</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForWholeWordMask.tf_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tf_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForWholeWordMask.tf_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForWholeWordMask.tf_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L1031" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask_labels<span class="opacity-60">: typing.Any</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForWholeWordMask.torch_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>torch_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForWholeWordMask.torch_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForWholeWordMask.torch_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L992" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask_labels<span class="opacity-60">: typing.Any</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.</p></div></div>
<h2 class="relative group"><a id="transformers.DataCollatorForPermutationLanguageModeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataCollatorForPermutationLanguageModeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DataCollatorForPermutationLanguageModeling
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForPermutationLanguageModeling"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DataCollatorForPermutationLanguageModeling</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForPermutationLanguageModeling" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForPermutationLanguageModeling"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L1197" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizerBase</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">plm_probability<span class="opacity-60">: float = 0.16666666666666666</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_span_length<span class="opacity-60">: int = 5</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Data collator used for permutation language modeling.</p>
<ul><li>collates batches of tensors, honoring their tokenizer’s pad_token</li>
<li>preprocesses batches for permutation language modeling with procedures specific to XLNet</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForPermutationLanguageModeling.numpy_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>numpy_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForPermutationLanguageModeling.numpy_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForPermutationLanguageModeling.numpy_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L1436" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>The masked tokens to be predicted for a particular sequence are determined by the following algorithm:</p>
<ol start="0"><li>Start from the beginning of the sequence by setting <code>cur_len = 0</code> (number of tokens processed so far).</li>
<li>Sample a <code>span_length</code> from the interval <code>[1, max_span_length]</code> (length of span of tokens to be masked)</li>
<li>Reserve a context of length <code>context_length = span_length / plm_probability</code> to surround span to be
masked</li>
<li>Sample a starting point <code>start_index</code> from the interval <code>[cur_len, cur_len + context_length - span_length]</code> and mask tokens <code>start_index:start_index + span_length</code></li>
<li>Set <code>cur_len = cur_len + context_length</code>. If <code>cur_len < max_len</code> (i.e. there are tokens remaining in the
sequence to be processed), repeat from Step 1.</li></ol></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForPermutationLanguageModeling.tf_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tf_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForPermutationLanguageModeling.tf_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForPermutationLanguageModeling.tf_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L1328" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>The masked tokens to be predicted for a particular sequence are determined by the following algorithm:</p>
<ol start="0"><li>Start from the beginning of the sequence by setting <code>cur_len = 0</code> (number of tokens processed so far).</li>
<li>Sample a <code>span_length</code> from the interval <code>[1, max_span_length]</code> (length of span of tokens to be masked)</li>
<li>Reserve a context of length <code>context_length = span_length / plm_probability</code> to surround span to be
masked</li>
<li>Sample a starting point <code>start_index</code> from the interval <code>[cur_len, cur_len + context_length - span_length]</code> and mask tokens <code>start_index:start_index + span_length</code></li>
<li>Set <code>cur_len = cur_len + context_length</code>. If <code>cur_len < max_len</code> (i.e. there are tokens remaining in the
sequence to be processed), repeat from Step 1.</li></ol></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataCollatorForPermutationLanguageModeling.torch_mask_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>torch_mask_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataCollatorForPermutationLanguageModeling.torch_mask_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataCollatorForPermutationLanguageModeling.torch_mask_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/data_collator.py#L1231" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Any</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>The masked tokens to be predicted for a particular sequence are determined by the following algorithm:</p>
<ol start="0"><li>Start from the beginning of the sequence by setting <code>cur_len = 0</code> (number of tokens processed so far).</li>
<li>Sample a <code>span_length</code> from the interval <code>[1, max_span_length]</code> (length of span of tokens to be masked)</li>
<li>Reserve a context of length <code>context_length = span_length / plm_probability</code> to surround span to be
masked</li>
<li>Sample a starting point <code>start_index</code> from the interval <code>[cur_len, cur_len + context_length - span_length]</code> and mask tokens <code>start_index:start_index + span_length</code></li>
<li>Set <code>cur_len = cur_len + context_length</code>. If <code>cur_len < max_len</code> (i.e. there are tokens remaining in the
sequence to be processed), repeat from Step 1.</li></ol></div></div>
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<h1 class="relative group"><a id="callbacks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#callbacks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Callbacks
</span></h1>
<p>Callbacks are objects that can customize the behavior of the training loop in the PyTorch
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> (this feature is not yet implemented in TensorFlow) that can inspect the training loop
state (for progress reporting, logging on TensorBoard or other ML platforms…) and take decisions (like early
stopping).</p>
<p>Callbacks are “read only” pieces of code, apart from the <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerControl">TrainerControl</a> object they return, they
cannot change anything in the training loop. For customizations that require changes in the training loop, you should
subclass <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> and override the methods you need (see <a href="trainer">trainer</a> for examples).</p>
<p>By default a <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will use the following callbacks:</p>
<ul><li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.DefaultFlowCallback">DefaultFlowCallback</a> which handles the default behavior for logging, saving and evaluation.</li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.PrinterCallback">PrinterCallback</a> or <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.ProgressCallback">ProgressCallback</a> to display progress and print the
logs (the first one is used if you deactivate tqdm through the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>, otherwise
it’s the second one).</li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.integrations.TensorBoardCallback">TensorBoardCallback</a> if tensorboard is accessible (either through PyTorch >= 1.4
or tensorboardX).</li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.integrations.WandbCallback">WandbCallback</a> if <a href="https://www.wandb.com/" rel="nofollow">wandb</a> is installed.</li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.integrations.CometCallback">CometCallback</a> if <a href="https://www.comet.ml/site/" rel="nofollow">comet_ml</a> is installed.</li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.integrations.MLflowCallback">MLflowCallback</a> if <a href="https://www.mlflow.org/" rel="nofollow">mlflow</a> is installed.</li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.integrations.AzureMLCallback">AzureMLCallback</a> if <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">azureml-sdk</a> is
installed.</li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.integrations.CodeCarbonCallback">CodeCarbonCallback</a> if <a href="https://pypi.org/project/codecarbon/" rel="nofollow">codecarbon</a> is
installed.</li></ul>
<p>The main class that implements callbacks is <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>. It gets the
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> used to instantiate the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, can access that
Trainer’s internal state via <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerState">TrainerState</a>, and can take some actions on the training loop via
<a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerControl">TrainerControl</a>.</p>
<h2 class="relative group"><a id="transformers.integrations.CometCallback" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.integrations.CometCallback"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Available Callbacks
</span></h2>
<p>Here is the list of the available <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> in the library:</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.CometCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.integrations.</span><span class="font-semibold">CometCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.integrations.CometCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.CometCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L660" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.comet.ml/site/" rel="nofollow">Comet ML</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.CometCallback.setup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>setup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.integrations.CometCallback.setup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.CometCallback.setup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L671" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Setup the optional Comet.ml integration.</p>
<p>Environment:
COMET_MODE (<code>str</code>, <em>optional</em>):
Whether to create an online, offline experiment or disable Comet logging. Can be “OFFLINE”, “ONLINE”,
or “DISABLED”. Defaults to “ONLINE”.
COMET_PROJECT_NAME (<code>str</code>, <em>optional</em>):
Comet project name for experiments
COMET_OFFLINE_DIRECTORY (<code>str</code>, <em>optional</em>):
Folder to use for saving offline experiments when <code>COMET_MODE</code> is “OFFLINE”
COMET_LOG_ASSETS (<code>str</code>, <em>optional</em>):
Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be “TRUE”, or
“FALSE”. Defaults to “TRUE”.</p>
<p>For a number of configurable items in the environment, see
<a href="https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables" rel="nofollow">here</a>.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DefaultFlowCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DefaultFlowCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DefaultFlowCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DefaultFlowCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L406" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles the default flow of the training loop for logs, evaluation and checkpoints.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PrinterCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PrinterCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PrinterCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PrinterCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L494" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A bare <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that just prints the logs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ProgressCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ProgressCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ProgressCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ProgressCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L452" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that displays the progress of training or evaluation.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.EarlyStoppingCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">EarlyStoppingCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.EarlyStoppingCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.EarlyStoppingCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L505" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">early_stopping_patience<span class="opacity-60">: int = 1</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">early_stopping_threshold<span class="opacity-60">: typing.Optional[float] = 0.0</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.EarlyStoppingCallback.early_stopping_patience" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.EarlyStoppingCallback.early_stopping_patience"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>early_stopping_patience</strong> (<code>int</code>) —
Use with <code>metric_for_best_model</code> to stop training when the specified metric worsens for
<code>early_stopping_patience</code> evaluation calls.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.EarlyStoppingCallback.early_stopping_threshold(float," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.EarlyStoppingCallback.early_stopping_threshold(float,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>early_stopping_threshold(<code>float</code>,</strong> <em>optional</em>) —
Use with TrainingArguments <code>metric_for_best_model</code> and <code>early_stopping_patience</code> to denote how much the
specified metric must improve to satisfy early stopping conditions. `<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that handles early stopping.</p>
<p>This callback depends on <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> argument <em>load_best_model_at_end</em> functionality to set best_metric
in <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerState">TrainerState</a>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.TensorBoardCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.integrations.</span><span class="font-semibold">TensorBoardCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.integrations.TensorBoardCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.TensorBoardCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L446" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tb_writer<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.integrations.TensorBoardCallback.tb_writer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.integrations.TensorBoardCallback.tb_writer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tb_writer</strong> (<code>SummaryWriter</code>, <em>optional</em>) —
The writer to use. Will instantiate one if not set.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.WandbCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.integrations.</span><span class="font-semibold">WandbCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.integrations.WandbCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.WandbCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L534" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.wandb.com/" rel="nofollow">Weight and Biases</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.WandbCallback.setup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>setup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.integrations.WandbCallback.setup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.WandbCallback.setup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L551" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Setup the optional Weights & Biases (<em>wandb</em>) integration.</p>
<p>One can subclass and override this method to customize the setup if needed. Find more information
<a href="https://docs.wandb.ai/integrations/huggingface" rel="nofollow">here</a>. You can also override the following environment
variables:</p>
<p>Environment:
WANDB_LOG_MODEL (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether or not to log model as artifact at the end of training. Use along with
<em>TrainingArguments.load_best_model_at_end</em> to upload best model.
WANDB_WATCH (<code>str</code>, <em>optional</em> defaults to <code>"gradients"</code>):
Can be <code>"gradients"</code>, <code>"all"</code> or <code>"false"</code>. Set to <code>"false"</code> to disable gradient logging or <code>"all"</code> to
log gradients and parameters.
WANDB_PROJECT (<code>str</code>, <em>optional</em>, defaults to <code>"huggingface"</code>):
Set this to a custom string to store results in a different project.
WANDB_DISABLED (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether or not to disable wandb entirely. Set <em>WANDB_DISABLED=true</em> to disable.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.MLflowCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.integrations.</span><span class="font-semibold">MLflowCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.integrations.MLflowCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.MLflowCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L759" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://www.mlflow.org/" rel="nofollow">MLflow</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.MLflowCallback.setup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>setup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.integrations.MLflowCallback.setup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.MLflowCallback.setup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L776" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Setup the optional MLflow integration.</p>
<p>Environment:
HF_MLFLOW_LOG_ARTIFACTS (<code>str</code>, <em>optional</em>):
Whether to use MLflow .log_artifact() facility to log artifacts.</p>
<p>This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to <code>True</code> or <em>1</em>, will copy
whatever is in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>output_dir</code> to the local or remote artifact storage. Using it
without a remote storage will just copy the files to your artifact location.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.AzureMLCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.integrations.</span><span class="font-semibold">AzureMLCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.integrations.AzureMLCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.AzureMLCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L736" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">azureml_run<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that sends the logs to <a href="https://pypi.org/project/azureml-sdk/" rel="nofollow">AzureML</a>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.integrations.CodeCarbonCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.integrations.</span><span class="font-semibold">CodeCarbonCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.integrations.CodeCarbonCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.integrations.CodeCarbonCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/integrations.py#L919" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> that tracks the CO2 emission of training.</p></div>
<h2 class="relative group"><a id="transformers.TrainerCallback" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TrainerCallback
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TrainerCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L159" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>) —
The training arguments used to instantiate the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>state</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerState">TrainerState</a>) —
The current state of the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.control" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.control"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>control</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerControl">TrainerControl</a>) —
The object that is returned to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> and can be used to make some decisions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <code>torch.nn.Module</code>) —
The model being trained.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer used for encoding the data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) —
The optimizer used for the training steps.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.lr_scheduler" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.lr_scheduler"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>lr_scheduler</strong> (<code>torch.optim.lr_scheduler.LambdaLR</code>) —
The scheduler used for setting the learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.train_dataloader" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.train_dataloader"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>train_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) —
The current dataloader used for training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.eval_dataloader" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.eval_dataloader"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_dataloader</strong> (<code>torch.utils.data.DataLoader</code>, <em>optional</em>) —
The current dataloader used for training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metrics</strong> (<code>Dict[str, float]</code>) —
The metrics computed by the last evaluation phase.</p>
<p>Those are only accessible in the event <code>on_evaluate</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerCallback.logs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerCallback.logs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logs</strong> (<code>Dict[str, float]</code>) —
The values to log.</p>
<p>Those are only accessible in the event <code>on_log</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A class for objects that will inspect the state of the training loop at some events and take some decisions. At
each of those events the following arguments are available:</p>
<p>The <code>control</code> object is the only one that can be changed by the callback, in which case the event that changes it
should return the modified version.</p>
<p>The argument <code>args</code>, <code>state</code> and <code>control</code> are positionals for all events, all the others are grouped in <code>kwargs</code>.
You can unpack the ones you need in the signature of the event using them. As an example, see the code of the
simple <code>PrinterCallback</code>.</p>
<p>Example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">PrinterCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">on_log</span>(<span class="hljs-params">self, args, state, control, logs=<span class="hljs-literal">None</span>, **kwargs</span>):
_ = logs.pop(<span class="hljs-string">"total_flos"</span>, <span class="hljs-literal">None</span>)
<span class="hljs-keyword">if</span> state.is_local_process_zero:
<span class="hljs-built_in">print</span>(logs)<!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_epoch_begin"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_epoch_begin</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_epoch_begin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_epoch_begin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L227" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the beginning of an epoch.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_epoch_end"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_epoch_end</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_epoch_end" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_epoch_end"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L233" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the end of an epoch.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_evaluate"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_evaluate</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_evaluate" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_evaluate"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L259" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called after an evaluation phase.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_init_end"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_init_end</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_init_end" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_init_end"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L209" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the end of the initialization of the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_log"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_log</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_log" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_log"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L271" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called after logging the last logs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_prediction_step"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_prediction_step</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_prediction_step" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_prediction_step"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L277" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called after a prediction step.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_save"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_save</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_save" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_save"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L265" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called after a checkpoint save.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_step_begin"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_step_begin</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_step_begin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_step_begin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L239" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the beginning of a training step. If using gradient accumulation, one training step might take
several inputs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_step_end"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_step_end</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_step_end" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_step_end"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L252" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the end of a training step. If using gradient accumulation, one training step might take
several inputs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_substep_end"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_substep_end</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_substep_end" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_substep_end"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L246" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the end of an substep during gradient accumulation.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_train_begin"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_train_begin</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_train_begin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_train_begin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L215" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the beginning of training.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerCallback.on_train_end"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>on_train_end</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerCallback.on_train_end" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerCallback.on_train_end"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L221" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state<span class="opacity-60">: TrainerState</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">control<span class="opacity-60">: TrainerControl</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Event called at the end of training.</p></div></div>
<p>Here is an example of how to register a custom callback with the PyTorch <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCallback</span>(<span class="hljs-title class_ inherited__">TrainerCallback</span>):
<span class="hljs-string">"A callback that prints a message at the beginning of training"</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">on_train_begin</span>(<span class="hljs-params">self, args, state, control, **kwargs</span>):
<span class="hljs-built_in">print</span>(<span class="hljs-string">"Starting training"</span>)
trainer = Trainer(
model,
args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=[MyCallback], <span class="hljs-comment"># We can either pass the callback class this way or an instance of it (MyCallback())</span>
)<!-- HTML_TAG_END --></pre></div>
<p>Another way to register a callback is to call <code>trainer.add_callback()</code> as follows:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->trainer = Trainer(...)
trainer.add_callback(MyCallback)
<span class="hljs-comment"># Alternatively, we can pass an instance of the callback class</span>
trainer.add_callback(MyCallback())<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="transformers.TrainerState" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TrainerState
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerState"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TrainerState</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TrainerState" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerState"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L35" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">epoch<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">global_step<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_steps<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_train_epochs<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">total_flos<span class="opacity-60">: float = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">log_history<span class="opacity-60">: typing.List[typing.Dict[str, float]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">best_metric<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">best_model_checkpoint<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_local_process_zero<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_world_process_zero<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_hyper_param_search<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">trial_name<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">trial_params<span class="opacity-60">: typing.Dict[str, typing.Union[str, float, int, bool]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.epoch" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.epoch"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>epoch</strong> (<code>float</code>, <em>optional</em>) —
Only set during training, will represent the epoch the training is at (the decimal part being the
percentage of the current epoch completed).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.global_step" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.global_step"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>global_step</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
During training, represents the number of update steps completed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.max_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.max_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
The number of update steps to do during the current training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.total_flos" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.total_flos"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>total_flos</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The total number of floating operations done by the model since the beginning of training (stored as floats
to avoid overflow).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.log_history" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.log_history"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>log_history</strong> (<code>List[Dict[str, float]]</code>, <em>optional</em>) —
The list of logs done since the beginning of training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.best_metric" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.best_metric"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>best_metric</strong> (<code>float</code>, <em>optional</em>) —
When tracking the best model, the value of the best metric encountered so far.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.best_model_checkpoint" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.best_model_checkpoint"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>best_model_checkpoint</strong> (<code>str</code>, <em>optional</em>) —
When tracking the best model, the value of the name of the checkpoint for the best model encountered so
far.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.is_local_process_zero" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.is_local_process_zero"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_local_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on
several machines) main process.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.is_world_process_zero" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.is_world_process_zero"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_world_process_zero</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be <code>True</code> for one process).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerState.is_hyper_param_search" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerState.is_hyper_param_search"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_hyper_param_search</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether we are in the process of a hyper parameter search using Trainer.hyperparameter_search. This will
impact the way data will be logged in TensorBoard.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A class containing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> inner state that will be saved along the model and optimizer when checkpointing
and passed to the <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>In all this class, one step is to be understood as one update step. When using gradient accumulation, one update
step may require several forward and backward passes: if you use <code>gradient_accumulation_steps=n</code>, then one update
step requires going through <em>n</em> batches.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerState.load_from_json"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>load_from_json</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerState.load_from_json" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerState.load_from_json"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L101" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">json_path<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Create an instance from the content of <code>json_path</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerState.save_to_json"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_to_json</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainerState.save_to_json" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerState.save_to_json"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L95" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">json_path<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Save the content of this instance in JSON format inside <code>json_path</code>.</p></div></div>
<h2 class="relative group"><a id="transformers.TrainerControl" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerControl"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TrainerControl
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainerControl"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TrainerControl</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TrainerControl" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainerControl"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L110" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">should_training_stop<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">should_epoch_stop<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">should_save<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">should_evaluate<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">should_log<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerControl.should_training_stop" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerControl.should_training_stop"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>should_training_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the training should be interrupted.</p>
<p>If <code>True</code>, this variable will not be set back to <code>False</code>. The training will just stop.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerControl.should_epoch_stop" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerControl.should_epoch_stop"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>should_epoch_stop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the current epoch should be interrupted.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next epoch.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerControl.should_save" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerControl.should_save"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>should_save</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should be saved at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerControl.should_evaluate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerControl.should_evaluate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>should_evaluate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should be evaluated at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainerControl.should_log" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainerControl.should_log"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>should_log</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the logs should be reported at this step.</p>
<p>If <code>True</code>, this variable will be set back to <code>False</code> at the beginning of the next step.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A class that handles the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> control flow. This class is used by the <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a> to activate some
switches in the training loop.</p></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en | hf_public_repos/doc-build-dev/transformers/pr_16143/en/main_classes/feature_extractor.html | <meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{"local":"feature-extractor","sections":[{"local":"transformers.FeatureExtractionMixin","title":"FeatureExtractionMixin"},{"local":"transformers.SequenceFeatureExtractor","title":"SequenceFeatureExtractor"},{"local":"transformers.BatchFeature","title":"BatchFeature"},{"local":"transformers.ImageFeatureExtractionMixin","title":"ImageFeatureExtractionMixin"}],"title":"Feature Extractor"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="feature-extractor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#feature-extractor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Feature Extractor
</span></h1>
<p>A feature extractor is in charge of preparing input features for a multi-modal model. This includes feature extraction
from sequences, <em>e.g.</em>, pre-processing audio files to Log-Mel Spectrogram features, feature extraction from images
<em>e.g.</em> cropping image image files, but also padding, normalization, and conversion to Numpy, PyTorch, and TensorFlow
tensors.</p>
<h2 class="relative group"><a id="transformers.FeatureExtractionMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FeatureExtractionMixin
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FeatureExtractionMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FeatureExtractionMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FeatureExtractionMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FeatureExtractionMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_utils.py#L205" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>This is a feature extraction mixin used to provide saving/loading functionality for sequential and image feature
extractors.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FeatureExtractionMixin.from_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FeatureExtractionMixin.from_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FeatureExtractionMixin.from_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_utils.py#L229" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.cache_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.cache_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) —
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.force_download" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.force_download"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.resume_download" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.resume_download"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.proxies" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.proxies"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.</code> The proxies are used on each request.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.revision(str," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.revision(str,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>"main"</code>) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.return_unused_kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.return_unused_kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.from_pretrained.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.from_pretrained.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) —
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Instantiate a type of <a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin">FeatureExtractionMixin</a> from a feature extractor, <em>e.g.</em> a
derived class of <a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor">SequenceFeatureExtractor</a>.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Passing <code>use_auth_token=True</code> is required when you want to use a private model.</p></div>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># We can't instantiate directly the base class *FeatureExtractionMixin* nor *SequenceFeatureExtractor* so let's show the examples on a</span>
<span class="hljs-comment"># derived class: *Wav2Vec2FeatureExtractor*</span>
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>
) <span class="hljs-comment"># Download feature_extraction_config from huggingface.co and cache.</span>
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
<span class="hljs-string">"./test/saved_model/"</span>
) <span class="hljs-comment"># E.g. feature_extractor (or model) was saved using *save_pretrained('./test/saved_model/')*</span>
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(<span class="hljs-string">"./test/saved_model/preprocessor_config.json"</span>)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>, return_attention_mask=<span class="hljs-literal">False</span>, foo=<span class="hljs-literal">False</span>
)
<span class="hljs-keyword">assert</span> feature_extractor.return_attention_mask <span class="hljs-keyword">is</span> <span class="hljs-literal">False</span>
feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained(
<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>, return_attention_mask=<span class="hljs-literal">False</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
)
<span class="hljs-keyword">assert</span> feature_extractor.return_attention_mask <span class="hljs-keyword">is</span> <span class="hljs-literal">False</span>
<span class="hljs-keyword">assert</span> unused_kwargs == {<span class="hljs-string">"foo"</span>: <span class="hljs-literal">False</span>}<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FeatureExtractionMixin.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FeatureExtractionMixin.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FeatureExtractionMixin.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_utils.py#L313" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory where the feature extractor JSON file will be saved (will be created if it does not exist).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionMixin.save_pretrained.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionMixin.save_pretrained.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your feature extractor to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save a feature_extractor object to the directory <code>save_directory</code>, so that it can be re-loaded using the
<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a> class method.</p></div></div>
<h2 class="relative group"><a id="transformers.SequenceFeatureExtractor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>SequenceFeatureExtractor
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SequenceFeatureExtractor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">SequenceFeatureExtractor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.SequenceFeatureExtractor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SequenceFeatureExtractor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_sequence_utils.py#L38" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature_size<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sampling_rate<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding_value<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.feature_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.feature_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>feature_size</strong> (<code>int</code>) —
The feature dimension of the extracted features.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.sampling_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.sampling_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sampling_rate</strong> (<code>int</code>) —
The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.padding_value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.padding_value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding_value</strong> (<code>float</code>) —
The value that is used to fill the padding values / vectors.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>This is a general feature extraction class for speech recognition.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SequenceFeatureExtractor.pad"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>pad</span></h4><!-- HTML_TAG_END -->
<a id="transformers.SequenceFeatureExtractor.pad" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SequenceFeatureExtractor.pad"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_sequence_utils.py#L61" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">processed_features<span class="opacity-60">: typing.Union[transformers.feature_extraction_utils.BatchFeature, typing.List[transformers.feature_extraction_utils.BatchFeature], typing.Dict[str, transformers.feature_extraction_utils.BatchFeature], typing.Dict[str, typing.List[transformers.feature_extraction_utils.BatchFeature]], typing.List[typing.Dict[str, transformers.feature_extraction_utils.BatchFeature]]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.pad.processed_features" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.pad.processed_features"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>processed_features</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a>, list of <a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a>, <code>Dict[str, List[float]]</code>, <code>Dict[str, List[List[float]]</code> or <code>List[Dict[str, List[float]]]</code>) —
Processed inputs. Can represent one input (<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a> or <code>Dict[str, List[float]]</code>) or a batch of
input values / vectors (list of <a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.BatchFeature">BatchFeature</a>, <em>Dict[str, List[List[float]]]</em> or <em>List[Dict[str,
List[float]]]</em>) so you can use this method during preprocessing as well as in a PyTorch Dataloader
collate function.</p>
<p>Instead of <code>List[float]</code> you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
see the note above for the return type.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.pad.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.pad.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Select a strategy to pad the returned sequences (according to the model’s padding side and padding
index) among:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.pad.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.pad.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Maximum length of the returned list and optionally padding length (see above).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.pad.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.pad.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>) —
Activates truncation to cut input sequences longer than <code>max_length</code> to <code>max_length</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.pad.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.pad.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability</p>
<blockquote>
<p>= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.</p>
</blockquote><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.pad.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.pad.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific feature_extractor’s default.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SequenceFeatureExtractor.pad.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SequenceFeatureExtractor.pad.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Pad input values / input vectors or a batch of input values / input vectors up to predefined length or to the
max sequence length in the batch.</p>
<p>Padding side (left/right) padding values are defined at the feature extractor level (with <code>self.padding_side</code>,
<code>self.padding_value</code>)</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If the <code>processed_features</code> passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
result will use the same type unless you provide a different tensor type with <code>return_tensors</code>. In the case of
PyTorch tensors, you will lose the specific device of your tensors however.</p></div></div></div>
<h2 class="relative group"><a id="transformers.BatchFeature" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchFeature"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BatchFeature
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchFeature"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">BatchFeature</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.BatchFeature" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchFeature"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_utils.py#L63" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60">: typing.Union[typing.Dict[str, typing.Any], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tensor_type<span class="opacity-60">: typing.Union[NoneType, str, transformers.file_utils.TensorType] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchFeature.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchFeature.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>dict</code>) —
Dictionary of lists/arrays/tensors returned by the <strong>call</strong>/pad methods (‘input_values’, ‘attention_mask’,
etc.).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchFeature.tensor_type" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchFeature.tensor_type"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tensor_type</strong> (<code>Union[None, str, TensorType]</code>, <em>optional</em>) —
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Holds the output of the <a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.SequenceFeatureExtractor.pad">pad()</a> and feature extractor specific <code>__call__</code> methods.</p>
<p>This class is derived from a python dictionary and can be used as a dictionary.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchFeature.convert_to_tensors"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>convert_to_tensors</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchFeature.convert_to_tensors" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchFeature.convert_to_tensors"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_utils.py#L117" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tensor_type<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchFeature.convert_to_tensors.tensor_type" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchFeature.convert_to_tensors.tensor_type"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tensor_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
The type of tensors to use. If <code>str</code>, should be one of the values of the enum
<a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>. If <code>None</code>, no modification is done.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Convert the inner content to tensors.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchFeature.to"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchFeature.to" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchFeature.to"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/feature_extraction_utils.py#L182" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: typing.Union[str, ForwardRef('torch.device')]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.BatchFeature"
>BatchFeature</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchFeature.to.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchFeature.to.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>str</code> or <code>torch.device</code>) — The device to put the tensors on.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchFeature.to.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.BatchFeature"
>BatchFeature</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The same instance after modification.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Send all values to device by calling <code>v.to(device)</code> (PyTorch only).</p></div></div>
<h2 class="relative group"><a id="transformers.ImageFeatureExtractionMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ImageFeatureExtractionMixin
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageFeatureExtractionMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ImageFeatureExtractionMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ImageFeatureExtractionMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageFeatureExtractionMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/image_utils.py#L76" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Mixin that contain utilities for preparing image features.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageFeatureExtractionMixin.center_crop"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>center_crop</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ImageFeatureExtractionMixin.center_crop" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageFeatureExtractionMixin.center_crop"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/image_utils.py#L249" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">image<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">size<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.center_crop.image" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.center_crop.image"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) —
The image to resize.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.center_crop.size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.center_crop.size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>size</strong> (<code>int</code> or <code>Tuple[int, int]</code>) —
The size to which crop the image.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Crops <code>image</code> to the given size using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageFeatureExtractionMixin.normalize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>normalize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ImageFeatureExtractionMixin.normalize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageFeatureExtractionMixin.normalize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/image_utils.py#L151" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">image<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mean<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">std<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.normalize.image" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.normalize.image"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) —
The image to normalize.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.normalize.mean" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.normalize.mean"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mean</strong> (<code>List[float]</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) —
The mean (per channel) to use for normalization.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.normalize.std" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.normalize.std"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>std</strong> (<code>List[float]</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) —
The standard deviation (per channel) to use for normalization.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Normalizes <code>image</code> with <code>mean</code> and <code>std</code>. Note that this will trigger a conversion of <code>image</code> to a NumPy array
if it’s a PIL Image.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageFeatureExtractionMixin.resize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>resize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ImageFeatureExtractionMixin.resize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageFeatureExtractionMixin.resize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/image_utils.py#L187" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">image<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">size<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">resample<span class="opacity-60"> = 2</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">default_to_square<span class="opacity-60"> = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_size<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.resize.image" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.resize.image"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) —
The image to resize.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.resize.size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.resize.size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>size</strong> (<code>int</code> or <code>Tuple[int, int]</code>) —
The size to use for resizing the image. If <code>size</code> is a sequence like (h, w), output size will be
matched to this.</p>
<p>If <code>size</code> is an int and <code>default_to_square</code> is <code>True</code>, then image will be resized to (size, size). If
<code>size</code> is an int and <code>default_to_square</code> is <code>False</code>, then smaller edge of the image will be matched to
this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.resize.resample" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.resize.resample"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>PIL.Image.BILINEAR</code>) —
The filter to user for resampling.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.resize.default_to_square" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.resize.default_to_square"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>default_to_square</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
How to convert <code>size</code> when it is a single int. If set to <code>True</code>, the <code>size</code> will be converted to a
square (<code>size</code>,<code>size</code>). If set to <code>False</code>, will replicate
<a href="https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize" rel="nofollow"><code>torchvision.transforms.Resize</code></a>
with support for resizing only the smallest edge and providing an optional <code>max_size</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.resize.max_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.resize.max_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) —
The maximum allowed for the longer edge of the resized image: if the longer edge of the image is
greater than <code>max_size</code> after being resized according to <code>size</code>, then the image is resized again so
that the longer edge is equal to <code>max_size</code>. As a result, <code>size</code> might be overruled, i.e the smaller
edge may be shorter than <code>size</code>. Only used if <code>default_to_square</code> is <code>False</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Resizes <code>image</code>. Note that this will trigger a conversion of <code>image</code> to a PIL Image.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageFeatureExtractionMixin.to_numpy_array"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_numpy_array</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ImageFeatureExtractionMixin.to_numpy_array" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageFeatureExtractionMixin.to_numpy_array"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/image_utils.py#L118" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">image<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">rescale<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">channel_first<span class="opacity-60"> = True</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.to_numpy_array.image" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.to_numpy_array.image"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>image</strong> (<code>PIL.Image.Image</code> or <code>np.ndarray</code> or <code>torch.Tensor</code>) —
The image to convert to a NumPy array.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.to_numpy_array.rescale" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.to_numpy_array.rescale"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>rescale</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will
default to <code>True</code> if the image is a PIL Image or an array/tensor of integers, <code>False</code> otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.to_numpy_array.channel_first" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.to_numpy_array.channel_first"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>channel_first</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to permute the dimensions of the image to put the channel dimension first.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Converts <code>image</code> to a numpy array. Optionally rescales it and puts the channel dimension as the first
dimension.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageFeatureExtractionMixin.to_pil_image"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_pil_image</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ImageFeatureExtractionMixin.to_pil_image" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageFeatureExtractionMixin.to_pil_image"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/image_utils.py#L88" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">image<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">rescale<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.to_pil_image.image" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.to_pil_image.image"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>image</strong> (<code>PIL.Image.Image</code> or <code>numpy.ndarray</code> or <code>torch.Tensor</code>) —
The image to convert to the PIL Image format.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageFeatureExtractionMixin.to_pil_image.rescale" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageFeatureExtractionMixin.to_pil_image.rescale"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>rescale</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will
default to <code>True</code> if the image type is a floating type, <code>False</code> otherwise.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Converts <code>image</code> to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
needed.</p></div></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en | hf_public_repos/doc-build-dev/transformers/pr_16143/en/main_classes/text_generation.html | <meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{"local":"generation","sections":[{"local":"transformers.generation_utils.GenerationMixin","title":"GenerationMixn"},{"local":"transformers.generation_tf_utils.TFGenerationMixin","title":"TFGenerationMixn"},{"local":"transformers.generation_flax_utils.FlaxGenerationMixin","title":"FlaxGenerationMixn"}],"title":"Generation"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="generation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#generation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Generation
</span></h1>
<p>The methods for auto-regressive text generation, namely <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate">generate()</a> (for the PyTorch models), <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin.generate">generate()</a> (for the TensorFlow models) and <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_flax_utils.FlaxGenerationMixin.generate">generate()</a> (for the Flax/JAX models), are implemented in <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin">GenerationMixin</a>, <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin">TFGenerationMixin</a> and <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_flax_utils.FlaxGenerationMixin">FlaxGenerationMixin</a> respectively.</p>
<p>The <code>GenerationMixin</code> classes are inherited by the corresponding base model classes, <em>e.g.</em> <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>, <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>, and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a> respectively, therefore exposing all
methods for auto-regressive text generation to every model class.</p>
<h2 class="relative group"><a id="transformers.generation_utils.GenerationMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>GenerationMixn
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">GenerationMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L379" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A class containing all functions for auto-regressive text generation, to be used as a mixin in <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>.</p>
<p>The class exposes <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate">generate()</a>, which can be used for:</p>
<ul><li><em>greedy decoding</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.greedy_search">greedy_search()</a> if <code>num_beams=1</code> and
<code>do_sample=False</code>.</li>
<li><em>multinomial sampling</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.sample">sample()</a> if <code>num_beams=1</code> and
<code>do_sample=True</code>.</li>
<li><em>beam-search decoding</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_search">beam_search()</a> if <code>num_beams>1</code> and
<code>do_sample=False</code>.</li>
<li><em>beam-search multinomial sampling</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_sample">beam_sample()</a> if
<code>num_beams>1</code> and <code>do_sample=True</code>.</li>
<li><em>diverse beam-search decoding</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.group_beam_search">group_beam_search()</a>, if
<code>num_beams>1</code> and <code>num_beam_groups>1</code>.</li>
<li><em>constrained beam-search decoding</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.constrained_beam_search">constrained_beam_search()</a>,
if <code>constraints!=None</code> or <code>force_words_ids!=None</code>.</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin.generate"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>generate</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin.generate" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin.generate"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L832" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Optional[torch.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_sample<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">early_stopping<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">temperature<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">typical_p<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repetition_penalty<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bad_words_ids<span class="opacity-60">: typing.Optional[typing.Iterable[int]] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">force_words_ids<span class="opacity-60">: typing.Union[typing.Iterable[int], typing.Iterable[typing.Iterable[int]], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">length_penalty<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">no_repeat_ngram_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_no_repeat_ngram_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_return_sequences<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_time<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_new_tokens<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_start_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_cache<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beam_groups<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">diversity_penalty<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prefix_allowed_tokens_fn<span class="opacity-60">: typing.Union[typing.Callable[[int, torch.Tensor], typing.List[int]], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_processor<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = []</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stopping_criteria<span class="opacity-60">: typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = []</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">constraints<span class="opacity-60">: typing.Optional[typing.List[transformers.generation_beam_constraints.Constraint]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">forced_bos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">forced_eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">remove_invalid_values<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">synced_gpus<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">exponential_decay_length_penalty<span class="opacity-60">: typing.Union[typing.Tuple[typing.Union[int, float]], NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> or <code>torch.LongTensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>torch.Tensor</code> of varying shape depending on the modality, <em>optional</em>) —
The sequence used as a prompt for the generation or as model inputs to the encoder. If <code>None</code> the
method initializes it with <code>bos_token_id</code> and a batch size of 1. For decoder-only models <code>inputs</code>
should of in the format of <code>input_ids</code>. For encoder-decoder models <em>inputs</em> can represent any of
<code>input_ids</code>, <code>input_values</code>, <code>input_features</code>, or <code>pixel_values</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to <code>model.config.max_length</code>) —
The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.max_new_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.max_new_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_new_tokens</strong> (<code>int</code>, <em>optional</em>, defaults to None) —
The maximum numbers of tokens to generate, ignore the current number of tokens. Use either
<code>max_new_tokens</code> or <code>max_length</code> but not both, they serve the same purpose.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.min_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.min_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) —
The minimum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.do_sample" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.do_sample"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_sample</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to use sampling ; use greedy decoding otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.early_stopping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.early_stopping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of beams for beam search. 1 means no beam search.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.temperature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.temperature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The value used to module the next token probabilities.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 50) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If set to float < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher
are kept for generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.repetition_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.repetition_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repetition_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.bos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.bos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>beginning-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.length_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.length_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the
model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer
sequences.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.no_repeat_ngram_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.no_repeat_ngram_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to int > 0, all ngrams of that size can only occur once.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.encoder_no_repeat_ngram_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.encoder_no_repeat_ngram_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to int > 0, all ngrams of that size that occur in the <code>encoder_input_ids</code> cannot occur in the
<code>decoder_input_ids</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.bad_words_ids(List[List[int]]," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.bad_words_ids(List[List[int]],"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bad_words_ids(<code>List[List[int]]</code>,</strong> <em>optional</em>) —
List of token ids that are not allowed to be generated. In order to get the token ids of the words that
should not appear in the generated text, use <code>tokenizer(bad_words, add_prefix_space=True, add_special_tokens=False).input_ids</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.force_words_ids(List[List[int]]" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.force_words_ids(List[List[int]]"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>force_words_ids(<code>List[List[int]]</code></strong> or <code>List[List[List[int]]]</code>, <em>optional</em>) —
List of token ids that must be generated. If given a <code>List[List[int]]</code>, this is treated as a simple
list of words that must be included, the opposite to <code>bad_words_ids</code>. If given <code>List[List[List[int]]]</code>,
this triggers a <a href="https://github.com/huggingface/transformers/issues/14081" rel="nofollow">disjunctive constraint</a>,
where one can allow different forms of each word.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.num_return_sequences(int," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.num_return_sequences(int,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_return_sequences(<code>int</code>,</strong> <em>optional</em>, defaults to 1) —
The number of independently computed returned sequences for each element in the batch.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.max_time(float," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.max_time(float,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_time(<code>float</code>,</strong> <em>optional</em>, defaults to None) —
The maximum amount of time you allow the computation to run for in seconds. generation will still
finish the current pass after allocated time has been passed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
Mask to avoid performing attention on padding token indices. Mask values are in <code>[0, 1]</code>, 1 for tokens
that are not masked, and 0 for masked tokens. If not provided, will default to a tensor the same shape
as <code>input_ids</code> that masks the pad token. <a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.decoder_start_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.decoder_start_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_start_token_id</strong> (<code>int</code>, <em>optional</em>) —
If an encoder-decoder model starts decoding with a different token than <em>bos</em>, the id of that token.
use_cache — (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.num_beam_groups" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.num_beam_groups"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beam_groups</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of
beams. <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.diversity_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.diversity_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>diversity_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
This value is subtracted from a beam’s score if it generates a token same as any beam from other group
at a particular time. Note that <code>diversity_penalty</code> is only effective if <code>group beam search</code> is
enabled.
prefix_allowed_tokens_fn — (<code>Callable[[int, torch.Tensor], List[int]]</code>, <em>optional</em>):
If provided, this function constraints the beam search to allowed tokens only at each step. If not
provided no constraint is applied. This function takes 2 arguments: the batch ID <code>batch_id</code> and
<code>input_ids</code>. It has to return a list with the allowed tokens for the next generation step conditioned
on the batch ID <code>batch_id</code> and the previously generated tokens <code>inputs_ids</code>. This argument is useful
for constrained generation conditioned on the prefix, as described in <a href="https://arxiv.org/abs/2010.00904" rel="nofollow">Autoregressive Entity
Retrieval</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.logits_processor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.logits_processor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
Custom logits processors that complement the default logits processors built from arguments and a
model’s config. If a logit processor is passed that is already created with the arguments or a model’s
config an error is thrown. This feature is intended for advanced users.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.stopping_criteria" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.stopping_criteria"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
Custom stopping criteria that complement the default stopping criteria built from arguments and a
model’s config. If a stopping criteria is passed that is already created with the arguments or a
model’s config an error is thrown. This feature is intended for advanced users.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.constraints" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.constraints"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>constraints</strong> (<code>List[Constraint]</code>, <em>optional</em>) —
Custom constraints that can be added to the generation to ensure that the output will contain the use
of certain tokens as defined by <code>Constraint</code> objects, in the most sensible way possible.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.forced_bos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.forced_bos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>forced_bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the first generated token after the <code>decoder_start_token_id</code>. Useful
for multilingual models like <a href="../model_doc/mbart">mBART</a> where the first generated token needs to be
the target language token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.forced_eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.forced_eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>forced_eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.remove_invalid_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.remove_invalid_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>remove_invalid_values</strong> (<code>bool</code>, <em>optional</em>) —
Whether to remove possible <em>nan</em> and <em>inf</em> outputs of the model to prevent the generation method to
crash. Note that using <code>remove_invalid_values</code> can slow down generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.synced_gpus" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.synced_gpus"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.generate.exponential_decay_length_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.generate.exponential_decay_length_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>exponential_decay_length_penalty</strong> (<code>tuple(int, float)</code>, <em>optional</em>) —
This Tuple adds an exponentially increasing length penalty, after a certain amount of tokens have been
generated. The tuple shall consist of: <code>(start_index, decay_factor)</code> where <code>start_index</code> indicates
where penalty starts and <code>decay_factor</code> represents the factor of exponential decay</p>
<p>model<em>kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If the model
is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs
should be prefixed with *decoder</em>*.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.generation_utils.GenerationMixin.generate.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> or <code>torch.LongTensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> (if
<code>return_dict_in_generate=True</code> or when <code>config.return_dict_in_generate=True</code>) or a <code>torch.FloatTensor</code>.</p>
<p>If the model is <em>not</em> an encoder-decoder model (<code>model.config.is_encoder_decoder=False</code>), the possible
<a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> types are:</p>
<ul>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchDecoderOnlyOutput"
>GreedySearchDecoderOnlyOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleDecoderOnlyOutput"
>SampleDecoderOnlyOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchDecoderOnlyOutput"
>BeamSearchDecoderOnlyOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleDecoderOnlyOutput"
>BeamSampleDecoderOnlyOutput</a></li>
</ul>
<p>If the model is an encoder-decoder model (<code>model.config.is_encoder_decoder=True</code>), the possible
<a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> types are:</p>
<ul>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchEncoderDecoderOutput"
>GreedySearchEncoderDecoderOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.SampleEncoderDecoderOutput"
>SampleEncoderDecoderOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSearchEncoderDecoderOutput"
>BeamSearchEncoderDecoderOutput</a>,</li>
<li><a
href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.BeamSampleEncoderDecoderOutput"
>BeamSampleEncoderDecoderOutput</a></li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Generates sequences of token ids for models with a language modeling head. The method supports the following
generation methods for text-decoder, text-to-text, speech-to-text, and vision-to-text models:</p>
<ul><li><em>greedy decoding</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.greedy_search">greedy_search()</a> if <code>num_beams=1</code> and
<code>do_sample=False</code>.</li>
<li><em>multinomial sampling</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.sample">sample()</a> if <code>num_beams=1</code> and
<code>do_sample=True</code>.</li>
<li><em>beam-search decoding</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_search">beam_search()</a> if <code>num_beams>1</code> and
<code>do_sample=False</code>.</li>
<li><em>beam-search multinomial sampling</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_sample">beam_sample()</a> if
<code>num_beams>1</code> and <code>do_sample=True</code>.</li>
<li><em>diverse beam-search decoding</em> by calling <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.group_beam_search">group_beam_search()</a>, if
<code>num_beams>1</code> and <code>num_beam_groups>1</code>.</li>
<li><em>constrained beam-search decoding</em> by calling
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.constrained_beam_search">constrained_beam_search()</a>, if <code>constraints!=None</code> or
<code>force_words_ids!=None</code>.</li></ul>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>Apart from <code>inputs</code>, all the arguments below will default to the value of the attribute of the same name as
defined in the model’s config (<code>config.json</code>) which in turn defaults to the
<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> of the model.</p></div>
<p>Most of these parameters are explained in more detail in <a href="https://huggingface.co/blog/how-to-generate" rel="nofollow">this blog
post</a>.</p>
<p>Examples:</p>
<p>Greedy Decoding:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"Today I believe we can finally"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># generate up to 30 tokens</span>
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids, do_sample=<span class="hljs-literal">False</span>, max_length=<span class="hljs-number">30</span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Today I believe we can finally get to the point where we can make a difference in the lives of the people of the United States of America.\n'</span>]<!-- HTML_TAG_END --></pre></div>
<p>Multinomial Sampling:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>prompt = <span class="hljs-string">"Today I believe we can finally"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># sample up to 30 tokens</span>
<span class="hljs-meta">>>> </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids, do_sample=<span class="hljs-literal">True</span>, max_length=<span class="hljs-number">30</span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Today I believe we can finally get rid of discrimination," said Rep. Mark Pocan (D-Wis.).\n\n"Just look at the'</span>]<!-- HTML_TAG_END --></pre></div>
<p>Beam-search decoding:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"Helsinki-NLP/opus-mt-en-de"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"Helsinki-NLP/opus-mt-en-de"</span>)
<span class="hljs-meta">>>> </span>sentence = <span class="hljs-string">"Paris is one of the densest populated areas in Europe."</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(sentence, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Paris ist eines der dichtesten besiedelten Gebiete Europas.'</span>]<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin.greedy_search"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>greedy_search</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin.greedy_search" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin.greedy_search"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L1489" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_processor<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stopping_criteria<span class="opacity-60">: typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">synced_gpus<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.logits_processor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.logits_processor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.stopping_criteria" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.stopping_criteria"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.greedy_search.synced_gpus" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.greedy_search.synced_gpus"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific keyword arguments will be forwarded to the <code>forward</code> function of the model.
If model is an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generates sequences of token ids for models with a language modeling head using <strong>greedy decoding</strong> and can be
used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForCausalLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> StoppingCriteriaList,
<span class="hljs-meta">... </span> MaxLengthCriteria,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># set pad_token_id to eos_token_id because GPT2 does not have a EOS token</span>
<span class="hljs-meta">>>> </span>model.config.pad_token_id = model.config.eos_token_id
<span class="hljs-meta">>>> </span>input_prompt = <span class="hljs-string">"It might be possible to"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(input_prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">10</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=<span class="hljs-number">20</span>)])
<span class="hljs-meta">>>> </span>outputs = model.greedy_search(
<span class="hljs-meta">... </span> input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">"It might be possible to get a better understanding of the nature of the problem, but it's not"</span>]<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin.sample"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>sample</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin.sample" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin.sample"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L1721" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_processor<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stopping_criteria<span class="opacity-60">: typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_warper<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">synced_gpus<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.logits_processor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.logits_processor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.stopping_criteria" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.stopping_criteria"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.logits_warper" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.logits_warper"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_warper</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.sample.synced_gpus" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.sample.synced_gpus"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generates sequences of token ids for models with a language modeling head using <strong>multinomial sampling</strong> and
can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForCausalLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> TopKLogitsWarper,
<span class="hljs-meta">... </span> TemperatureLogitsWarper,
<span class="hljs-meta">... </span> StoppingCriteriaList,
<span class="hljs-meta">... </span> MaxLengthCriteria,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"gpt2"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># set pad_token_id to eos_token_id because GPT2 does not have a EOS token</span>
<span class="hljs-meta">>>> </span>model.config.pad_token_id = model.config.eos_token_id
<span class="hljs-meta">>>> </span>input_prompt = <span class="hljs-string">"Today is a beautiful day, and"</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(input_prompt, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">15</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_warper = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> TopKLogitsWarper(<span class="hljs-number">50</span>),
<span class="hljs-meta">... </span> TemperatureLogitsWarper(<span class="hljs-number">0.7</span>),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=<span class="hljs-number">20</span>)])
<span class="hljs-meta">>>> </span>torch.manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">>>> </span>outputs = model.sample(
<span class="hljs-meta">... </span> input_ids,
<span class="hljs-meta">... </span> logits_processor=logits_processor,
<span class="hljs-meta">... </span> logits_warper=logits_warper,
<span class="hljs-meta">... </span> stopping_criteria=stopping_criteria,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Today is a beautiful day, and a wonderful day.\n\nI was lucky enough to meet the'</span>]<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin.beam_search"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>beam_search</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin.beam_search" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin.beam_search"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L1977" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_scorer<span class="opacity-60">: BeamScorer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_processor<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stopping_criteria<span class="opacity-60">: typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">synced_gpus<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.beam_scorer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.beam_scorer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beam_scorer</strong> (<code>BeamScorer</code>) —
An derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> should be read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.logits_processor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.logits_processor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.stopping_criteria" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.stopping_criteria"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_search.synced_gpus" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_search.synced_gpus"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generates sequences of token ids for models with a language modeling head using <strong>beam search decoding</strong> and
can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> BeamSearchScorer,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run beam search using 3 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = BeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> num_beams=num_beams,
<span class="hljs-meta">... </span> device=model.device,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt bist du?'</span>]<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin.beam_sample"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>beam_sample</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin.beam_sample" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin.beam_sample"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L2289" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_scorer<span class="opacity-60">: BeamScorer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_processor<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stopping_criteria<span class="opacity-60">: typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_warper<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">synced_gpus<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.beam_scorer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.beam_scorer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beam_scorer</strong> (<code>BeamScorer</code>) —
A derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> should be read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.logits_processor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.logits_processor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.stopping_criteria" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.stopping_criteria"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.logits_warper" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.logits_warper"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_warper</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.beam_sample.synced_gpus" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.beam_sample.synced_gpus"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generates sequences of token ids for models with a language modeling head using <strong>beam search multinomial
sampling</strong> and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> TopKLogitsWarper,
<span class="hljs-meta">... </span> TemperatureLogitsWarper,
<span class="hljs-meta">... </span> BeamSearchScorer,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run beam search using 3 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = BeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> max_length=model.config.max_length,
<span class="hljs-meta">... </span> num_beams=num_beams,
<span class="hljs-meta">... </span> device=model.device,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id)]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_warper = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> TopKLogitsWarper(<span class="hljs-number">50</span>),
<span class="hljs-meta">... </span> TemperatureLogitsWarper(<span class="hljs-number">0.7</span>),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.beam_sample(
<span class="hljs-meta">... </span> input_ids, beam_scorer, logits_processor=logits_processor, logits_warper=logits_warper, **model_kwargs
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt bist du?'</span>]<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin.group_beam_search"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>group_beam_search</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin.group_beam_search" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin.group_beam_search"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L2611" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_scorer<span class="opacity-60">: BeamScorer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_processor<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stopping_criteria<span class="opacity-60">: typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">synced_gpus<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.beam_scorer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.beam_scorer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beam_scorer</strong> (<code>BeamScorer</code>) —
An derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> should be read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.logits_processor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.logits_processor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.stopping_criteria" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.stopping_criteria"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.group_beam_search.synced_gpus" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.group_beam_search.synced_gpus"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)</p>
<p>model_kwargs —
Additional model specific kwargs that will be forwarded to the <code>forward</code> function of the model. If
model is an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generates sequences of token ids for models with a language modeling head using <strong>diverse beam search
decoding</strong> and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> HammingDiversityLogitsProcessor,
<span class="hljs-meta">... </span> BeamSearchScorer,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run diverse beam search using 6 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">6</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = BeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> max_length=model.config.max_length,
<span class="hljs-meta">... </span> num_beams=num_beams,
<span class="hljs-meta">... </span> device=model.device,
<span class="hljs-meta">... </span> num_beam_groups=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> HammingDiversityLogitsProcessor(<span class="hljs-number">5.5</span>, num_beams=<span class="hljs-number">6</span>, num_beam_groups=<span class="hljs-number">3</span>),
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.group_beam_search(
<span class="hljs-meta">... </span> input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt bist du?'</span>]<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GenerationMixin.constrained_beam_search"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>constrained_beam_search</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GenerationMixin.constrained_beam_search" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L2976" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">constrained_beam_scorer<span class="opacity-60">: ConstrainedBeamSearchScorer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits_processor<span class="opacity-60">: typing.Optional[transformers.generation_logits_process.LogitsProcessorList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stopping_criteria<span class="opacity-60">: typing.Optional[transformers.generation_stopping_criteria.StoppingCriteriaList] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">synced_gpus<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.constrained_beam_scorer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.constrained_beam_scorer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>constrained_beam_scorer</strong> (<code>ConstrainedBeamSearchScorer</code>) —
A derived instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> that defines how beam hypotheses are constructed, stored and
sorted during generation, while satisfying a list of positive constraints. For more information, the
documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.ConstrainedBeamSearchScorer">ConstrainedBeamSearchScorer</a> should be read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.logits_processor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.logits_processor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_processor</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a>
used to modify the prediction scores of the language modeling head applied at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.stopping_criteria" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.stopping_criteria"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stopping_criteria</strong> (<code>StoppingCriteriaList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteriaList">StoppingCriteriaList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a>
used to tell if the generation loop should stop.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.logits_warper" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.logits_warper"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits_warper</strong> (<code>LogitsProcessorList</code>, <em>optional</em>) —
An instance of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessorList">LogitsProcessorList</a>. List of instances of class derived from <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
<strong>DEPRECATED</strong>. Use <code>logits_processor</code> or <code>stopping_criteria</code> directly to cap the number of generated
tokens. The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GenerationMixin.constrained_beam_search.synced_gpus" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GenerationMixin.constrained_beam_search.synced_gpus"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>synced_gpus</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
model_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If model is
an encoder-decoder model the kwargs should include <code>encoder_outputs</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generates sequences of token ids for models with a language modeling head using <strong>constrained beam search
decoding</strong> and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span> AutoTokenizer,
<span class="hljs-meta">... </span> AutoModelForSeq2SeqLM,
<span class="hljs-meta">... </span> LogitsProcessorList,
<span class="hljs-meta">... </span> MinLengthLogitsProcessor,
<span class="hljs-meta">... </span> ConstrainedBeamSearchScorer,
<span class="hljs-meta">... </span> PhrasalConstraint,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-base"</span>)
<span class="hljs-meta">>>> </span>encoder_input_str = <span class="hljs-string">"translate English to German: How old are you?"</span>
<span class="hljs-meta">>>> </span>encoder_input_ids = tokenizer(encoder_input_str, return_tensors=<span class="hljs-string">"pt"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># lets run beam search using 3 beams</span>
<span class="hljs-meta">>>> </span>num_beams = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># define decoder start token ids</span>
<span class="hljs-meta">>>> </span>input_ids = torch.ones((num_beams, <span class="hljs-number">1</span>), device=model.device, dtype=torch.long)
<span class="hljs-meta">>>> </span>input_ids = input_ids * model.config.decoder_start_token_id
<span class="hljs-meta">>>> </span><span class="hljs-comment"># add encoder_outputs to model keyword arguments</span>
<span class="hljs-meta">>>> </span>model_kwargs = {
<span class="hljs-meta">... </span> <span class="hljs-string">"encoder_outputs"</span>: model.get_encoder()(
<span class="hljs-meta">... </span> encoder_input_ids.repeat_interleave(num_beams, dim=<span class="hljs-number">0</span>), return_dict=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span>constraint_str = <span class="hljs-string">"sind"</span>
<span class="hljs-meta">>>> </span>constraint_token_ids = tokenizer.encode(constraint_str)[:-<span class="hljs-number">1</span>] <span class="hljs-comment"># slice to remove eos token</span>
<span class="hljs-meta">>>> </span>constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate beam scorer</span>
<span class="hljs-meta">>>> </span>beam_scorer = ConstrainedBeamSearchScorer(
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">1</span>, num_beams=num_beams, device=model.device, constraints=constraints
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># instantiate logits processors</span>
<span class="hljs-meta">>>> </span>logits_processor = LogitsProcessorList(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> MinLengthLogitsProcessor(<span class="hljs-number">5</span>, eos_token_id=model.config.eos_token_id),
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>outputs = model.constrained_beam_search(
<span class="hljs-meta">... </span> input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)
[<span class="hljs-string">'Wie alt sind Sie?'</span>]<!-- HTML_TAG_END --></pre></div></div></div>
<h2 class="relative group"><a id="transformers.generation_tf_utils.TFGenerationMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFGenerationMixn
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_tf_utils.TFGenerationMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_tf_utils.</span><span class="font-semibold">TFGenerationMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_tf_utils.TFGenerationMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_tf_utils.TFGenerationMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_utils.py#L342" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A class containing all of the functions supporting generation, to be used as a mixin in <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_tf_utils.TFGenerationMixin.generate"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>generate</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_tf_utils.TFGenerationMixin.generate" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_tf_utils.TFGenerationMixin.generate"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_utils.py#L362" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_length<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_sample<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">early_stopping<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">temperature<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repetition_penalty<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bad_words_ids<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bos_token_id<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">length_penalty<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">no_repeat_ngram_size<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_return_sequences<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attention_mask<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_start_token_id<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_cache<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_scores<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_attentions<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_hidden_states<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict_in_generate<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">forced_bos_token_id<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">forced_eos_token_id<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-default"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> or <code>tf.Tensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, `(batch_size, sequence_length, —<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.feature_dim)`" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.feature_dim)`"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>feature_dim)`</strong> or <code>(batch_size, num_channels, height, width)</code>, <em>optional</em>) —
The sequence used as a prompt for the generation or as model inputs to the encoder. If <code>None</code> the
method initializes it with <code>bos_token_id</code> and a batch size of 1. For decoder-only models <code>inputs</code>
should of in the format of <code>input_ids</code>. For encoder-decoder models <em>inputs</em> can represent any of
<code>input_ids</code>, <code>input_values</code>, <code>input_features</code>, or <code>pixel_values</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.min_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.min_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) —
The minimum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.do_sample" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.do_sample"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_sample</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to use sampling ; use greedy decoding otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.early_stopping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.early_stopping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of beams for beam search. 1 means no beam search.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.temperature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.temperature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The value used to module the next token probabilities.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 50) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If set to float < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher
are kept for generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.repetition_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.repetition_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repetition_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.bos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.bos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>beginning-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.length_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.length_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty.</p>
<p>Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in
order to encourage the model to produce longer sequences.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.no_repeat_ngram_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.no_repeat_ngram_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to int > 0, all ngrams of that size can only occur once.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.bad_words_ids(List[int]," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.bad_words_ids(List[int],"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bad_words_ids(<code>List[int]</code>,</strong> <em>optional</em>) —
List of token ids that are not allowed to be generated. In order to get the tokens of the words that
should not appear in the generated text, use <code>tokenizer.encode(bad_word, add_prefix_space=True)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.num_return_sequences(int," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.num_return_sequences(int,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_return_sequences(<code>int</code>,</strong> <em>optional</em>, defaults to 1) —
The number of independently computed returned sequences for each element in the batch.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attention_mask</strong> (<code>tf.Tensor</code> of <code>dtype=tf.int32</code> and shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
Mask to avoid performing attention on padding token indices. Mask values are in <code>[0, 1]</code>, 1 for tokens
that are not masked, and 0 for masked tokens.</p>
<p>If not provided, will default to a tensor the same shape as <code>input_ids</code> that masks the pad token.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.decoder_start_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.decoder_start_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_start_token_id</strong> (<code>int</code>, <em>optional</em>) —
If an encoder-decoder model starts decoding with a different token than <em>bos</em>, the id of that token.
use_cache — (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>):
Whether or not the model should use the past last key/values attentions (if applicable to the model) to
speed up decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under
returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors
for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.output_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.output_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the prediction scores. See <code>scores</code> under returned tensors for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.return_dict_in_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.return_dict_in_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.forced_bos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.forced_bos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>forced_bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the first generated token after the <code>decoder_start_token_id</code>. Useful
for multilingual models like <a href="../model_doc/mbart">mBART</a> where the first generated token needs to be
the target language token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_tf_utils.TFGenerationMixin.generate.forced_eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_tf_utils.TFGenerationMixin.generate.forced_eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>forced_eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.
model_specific_kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.generation_tf_utils.TFGenerationMixin.generate.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> or <code>tf.Tensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START --><!-- HTML_TAG_END --></p></div></div>
<p>Generates sequences for models with a language modeling head. The method currently supports greedy decoding,
beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.</p>
<p>Adapted in part from <a href="https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529" rel="nofollow">Facebook’s XLM beam search
code</a>.</p>
<p>Apart from <code>input_ids</code> and <code>attention_mask</code>, all the arguments below will default to the value of the attribute
of the same name inside the <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> of the model. The default values indicated are the default
values of those config.</p>
<p>Most of these parameters are explained in more detail in <a href="https://huggingface.co/blog/how-to-generate" rel="nofollow">this blog
post</a>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"distilgpt2"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
outputs = model.generate(max_length=<span class="hljs-number">40</span>) <span class="hljs-comment"># do greedy decoding</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated: <span class="hljs-subst">{tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"openai-gpt"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"openai-gpt"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"The dog"</span>
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, num_beams=<span class="hljs-number">5</span>, num_return_sequences=<span class="hljs-number">3</span>, temperature=<span class="hljs-number">1.5</span>
) <span class="hljs-comment"># generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>): <span class="hljs-comment"># 3 output sequences were generated</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated <span class="hljs-subst">{i}</span>: <span class="hljs-subst">{tokenizer.decode(outputs[i], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"distilgpt2"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"The dog"</span>
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, max_length=<span class="hljs-number">40</span>, temperature=<span class="hljs-number">0.7</span>, num_return_sequences=<span class="hljs-number">3</span>, do_sample=<span class="hljs-literal">True</span>
) <span class="hljs-comment"># generate 3 candidates using sampling</span>
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">3</span>): <span class="hljs-comment"># 3 output sequences were generated</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated <span class="hljs-subst">{i}</span>: <span class="hljs-subst">{tokenizer.decode(outputs[i], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"ctrl"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"ctrl"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"Legal My neighbor is"</span> <span class="hljs-comment"># "Legal" is one of the control codes for ctrl</span>
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, max_length=<span class="hljs-number">50</span>, temperature=<span class="hljs-number">0.7</span>, repetition_penalty=<span class="hljs-number">1.2</span>
) <span class="hljs-comment"># generate sequences</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Generated: <span class="hljs-subst">{tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)}</span>"</span>)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>) <span class="hljs-comment"># Initialize tokenizer</span>
model = TFAutoModelWithLMHead.from_pretrained(
<span class="hljs-string">"gpt2"</span>
) <span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
input_context = <span class="hljs-string">"My cute dog"</span>
bad_words_ids = [
tokenizer.encode(bad_word, add_prefix_space=<span class="hljs-literal">True</span>) <span class="hljs-keyword">for</span> bad_word <span class="hljs-keyword">in</span> [<span class="hljs-string">"idiot"</span>, <span class="hljs-string">"stupid"</span>, <span class="hljs-string">"shut up"</span>]
]
input_ids = tokenizer.encode(input_context, return_tensors=<span class="hljs-string">"tf"</span>) <span class="hljs-comment"># encode input context</span>
outputs = model.generate(
input_ids=input_ids, max_length=<span class="hljs-number">100</span>, do_sample=<span class="hljs-literal">True</span>, bad_words_ids=bad_words_ids
) <span class="hljs-comment"># generate sequences without allowing bad_words to be generated</span><!-- HTML_TAG_END --></pre></div></div></div>
<h2 class="relative group"><a id="transformers.generation_flax_utils.FlaxGenerationMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxGenerationMixn
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_flax_utils.FlaxGenerationMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_flax_utils.</span><span class="font-semibold">FlaxGenerationMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_flax_utils.FlaxGenerationMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_flax_utils.FlaxGenerationMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_utils.py#L119" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A class containing all functions for auto-regressive text generation, to be used as a mixin in
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>.</p>
<p>The class exposes <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_flax_utils.FlaxGenerationMixin.generate">generate()</a>, which can be used for:</p>
<ul><li><em>greedy decoding</em> by calling <code>_greedy_search()</code>if
<code>num_beams=1</code> and <code>do_sample=False</code>.</li>
<li><em>multinomial sampling</em> by calling <code>_sample()</code>if <code>num_beams=1</code>
and <code>do_sample=True</code>.</li>
<li><em>beam-search decoding</em> by calling <code>_beam_search</code> if <code>num_beams>1</code>
and <code>do_sample=False</code>.</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_flax_utils.FlaxGenerationMixin.generate"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>generate</span></h4><!-- HTML_TAG_END -->
<a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_utils.py#L163" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_start_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_sample<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prng_key<span class="opacity-60">: typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">temperature<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">no_repeat_ngram_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">forced_bos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">forced_eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">length_penalty<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">early_stopping<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">trace<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">params<span class="opacity-60">: typing.Union[typing.Dict[str, jax._src.numpy.lax_numpy.ndarray], NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
The sequence used as a prompt for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.do_sample" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.do_sample"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_sample</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to use sampling ; use greedy decoding otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.temperature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.temperature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The value used to module the next token probabilities.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 50) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If set to float < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher
are kept for generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.bos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.bos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>beginning-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of beams for beam search. 1 means no beam search.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.decoder_start_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.decoder_start_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_start_token_id</strong> (<code>int</code>, <em>optional</em>) —
If an encoder-decoder model starts decoding with a different token than <em>bos</em>, the id of that token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.trace" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.trace"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>trace</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to trace generation. Setting <code>trace=False</code> should only be used for debugging and will lead to a
considerably slower runtime.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGenerationMixin.generate.params" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGenerationMixin.generate.params"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>params</strong> (<code>Dict[str, jnp.ndarray]</code>, <em>optional</em>) —
Optionally the model parameters can be passed. Can be useful for parallelized generation.
model<em>kwargs —
Additional model specific kwargs will be forwarded to the <code>forward</code> function of the model. If the model
is an encoder-decoder model, encoder specific kwargs should not be prefixed and decoder specific kwargs
should be prefixed with *decoder</em>*. Also accepts <code>encoder_outputs</code> to skip encoder part.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generates sequences of token ids for models with a language modeling head. The method supports the following
generation methods for text-decoder, text-to-text, speech-to-text, and vision-to-text models:</p>
<ul><li><em>greedy decoding</em> by calling <code>_greedy_search()</code>if
<code>num_beams=1</code> and <code>do_sample=False</code>.</li>
<li><em>multinomial sampling</em> by calling <code>_sample()</code>if <code>num_beams=1</code>
and <code>do_sample=True</code>.</li>
<li><em>beam-search decoding</em> by calling <code>_beam_search</code> if <code>num_beams>1</code>
and <code>do_sample=False</code>.</li></ul>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>Apart from <code>inputs</code>, all the arguments below will default to the value of the attribute of the same name as
defined in the model’s config (<code>config.json</code>) which in turn defaults to the
<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> of the model.</p></div>
<p>Most of these parameters are explained in more detail in <a href="https://huggingface.co/blog/how-to-generate" rel="nofollow">this blog
post</a>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxAutoModelForCausalLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)
<span class="hljs-meta">>>> </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)
<span class="hljs-meta">>>> </span>input_context = <span class="hljs-string">"The dog"</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># encode input context</span>
<span class="hljs-meta">>>> </span>input_ids = tokenizer(input_context, return_tensors=<span class="hljs-string">"np"</span>).input_ids
<span class="hljs-meta">>>> </span><span class="hljs-comment"># generate candidates using sampling</span>
<span class="hljs-meta">>>> </span>outputs = model.generate(input_ids=input_ids, max_length=<span class="hljs-number">20</span>, top_k=<span class="hljs-number">30</span>, do_sample=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(outputs, skip_special_tokens=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<h1 class="relative group"><a id="pipelines" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipelines"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipelines
</span></h1>
<p>The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of
the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity
Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Question Answering. See the
<a href="../task_summary">task summary</a> for examples of use.</p>
<p>There are two categories of pipeline abstractions to be aware about:</p>
<ul><li><p>The <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> which is the most powerful object encapsulating all other pipelines.</p></li>
<li><p>The other task-specific pipelines:</p>
<ul><li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AudioClassificationPipeline">AudioClassificationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline">AutomaticSpeechRecognitionPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ConversationalPipeline">ConversationalPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.FeatureExtractionPipeline">FeatureExtractionPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.FillMaskPipeline">FillMaskPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ImageClassificationPipeline">ImageClassificationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ImageSegmentationPipeline">ImageSegmentationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ObjectDetectionPipeline">ObjectDetectionPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.QuestionAnsweringPipeline">QuestionAnsweringPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.SummarizationPipeline">SummarizationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TableQuestionAnsweringPipeline">TableQuestionAnsweringPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TextClassificationPipeline">TextClassificationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TextGenerationPipeline">TextGenerationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Text2TextGenerationPipeline">Text2TextGenerationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TokenClassificationPipeline">TokenClassificationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TranslationPipeline">TranslationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ZeroShotClassificationPipeline">ZeroShotClassificationPipeline</a></li>
<li><a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ZeroShotImageClassificationPipeline">ZeroShotImageClassificationPipeline</a></li></ul></li></ul>
<h2 class="relative group"><a id="transformers.pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>The pipeline abstraction
</span></h2>
<p>The <em>pipeline</em> abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
pipeline but can provide additional quality of life.</p>
<p>Simple call on one item:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pipe = pipeline(<span class="hljs-string">"text-classification"</span>)
<span class="hljs-meta">>>> </span>pipe(<span class="hljs-string">"This restaurant is awesome"</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.9998743534088135</span>}]<!-- HTML_TAG_END --></pre></div>
<p>If you want to use a specific model from the <a href="https://huggingface.co" rel="nofollow">hub</a> you can ignore the task if the model on
the hub already defines it:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pipe = pipeline(model=<span class="hljs-string">"roberta-large-mnli"</span>)
<span class="hljs-meta">>>> </span>pipe(<span class="hljs-string">"This restaurant is awesome"</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.9998743534088135</span>}]<!-- HTML_TAG_END --></pre></div>
<p>To call a pipeline on many items, you can either call with a <em>list</em>.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pipe = pipeline(<span class="hljs-string">"text-classification"</span>)
<span class="hljs-meta">>>> </span>pipe([<span class="hljs-string">"This restaurant is awesome"</span>, <span class="hljs-string">"This restaurant is aweful"</span>])
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.9998743534088135</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'NEGATIVE'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.9996669292449951</span>}]<!-- HTML_TAG_END --></pre></div>
<p>To iterate of full datasets it is recommended to use a <code>dataset</code> directly. This means you don’t need to allocate
the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
GPU. If it doesn’t don’t hesitate to create an issue.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> datasets
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-keyword">from</span> transformers.pipelines.pt_utils <span class="hljs-keyword">import</span> KeyDataset
<span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm
pipe = pipeline(<span class="hljs-string">"automatic-speech-recognition"</span>, model=<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>, device=<span class="hljs-number">0</span>)
dataset = datasets.load_dataset(<span class="hljs-string">"superb"</span>, name=<span class="hljs-string">"asr"</span>, split=<span class="hljs-string">"test"</span>)
<span class="hljs-comment"># KeyDataset (only *pt*) will simply return the item in the dict returned by the dataset item</span>
<span class="hljs-comment"># as we're not interested in the *target* part of the dataset.</span>
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> tqdm(pipe(KeyDataset(dataset, <span class="hljs-string">"file"</span>))):
<span class="hljs-built_in">print</span>(out)
<span class="hljs-comment"># {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}</span>
<span class="hljs-comment"># {"text": ....}</span>
<span class="hljs-comment"># ....</span><!-- HTML_TAG_END --></pre></div>
<p>For ease of use, a generator is also possible:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
pipe = pipeline(<span class="hljs-string">"text-classification"</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">data</span>():
<span class="hljs-keyword">while</span> <span class="hljs-literal">True</span>:
<span class="hljs-comment"># This could come from a dataset, a database, a queue or HTTP request</span>
<span class="hljs-comment"># in a server</span>
<span class="hljs-comment"># Caveat: because this is iterative, you cannot use `num_workers > 1` variable</span>
<span class="hljs-comment"># to use multiple threads to preprocess data. You can still have 1 thread that</span>
<span class="hljs-comment"># does the preprocessing while the main runs the big inference</span>
<span class="hljs-keyword">yield</span> <span class="hljs-string">"This is a test"</span>
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> pipe(data()):
<span class="hljs-built_in">print</span>(out)
<span class="hljs-comment"># {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}</span>
<span class="hljs-comment"># {"text": ....}</span>
<span class="hljs-comment"># ....</span><!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.pipeline"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.pipeline</span></h4><!-- HTML_TAG_END -->
<a id="transformers.pipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.pipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines.py#L373" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Optional = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: typing.Union[str, transformers.configuration_utils.PretrainedConfig, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: typing.Union[str, transformers.tokenization_utils.PreTrainedTokenizer, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature_extractor<span class="opacity-60">: typing.Union[str, ForwardRef('SequenceFeatureExtractor'), NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">revision<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_fast<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[str, bool, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model_kwargs<span class="opacity-60">: typing.Dict[str, typing.Any] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pipeline_class<span class="opacity-60">: typing.Optional[typing.Any] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Pipeline"
>Pipeline</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>) —
The task defining which pipeline will be returned. Currently accepted tasks are:</p>
<ul>
<li><code>"audio-classification"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AudioClassificationPipeline">AudioClassificationPipeline</a>.</li>
<li><code>"automatic-speech-recognition"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline">AutomaticSpeechRecognitionPipeline</a>.</li>
<li><code>"conversational"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ConversationalPipeline">ConversationalPipeline</a>.</li>
<li><code>"feature-extraction"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.FeatureExtractionPipeline">FeatureExtractionPipeline</a>.</li>
<li><code>"fill-mask"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.FillMaskPipeline">FillMaskPipeline</a>:.</li>
<li><code>"image-classification"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ImageClassificationPipeline">ImageClassificationPipeline</a>.</li>
<li><code>"question-answering"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.QuestionAnsweringPipeline">QuestionAnsweringPipeline</a>.</li>
<li><code>"table-question-answering"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TableQuestionAnsweringPipeline">TableQuestionAnsweringPipeline</a>.</li>
<li><code>"text2text-generation"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Text2TextGenerationPipeline">Text2TextGenerationPipeline</a>.</li>
<li><code>"text-classification"</code> (alias <code>"sentiment-analysis"</code> available): will return a
<a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TextClassificationPipeline">TextClassificationPipeline</a>.</li>
<li><code>"text-generation"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TextGenerationPipeline">TextGenerationPipeline</a>:.</li>
<li><code>"token-classification"</code> (alias <code>"ner"</code> available): will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TokenClassificationPipeline">TokenClassificationPipeline</a>.</li>
<li><code>"translation"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TranslationPipeline">TranslationPipeline</a>.</li>
<li><code>"translation_xx_to_yy"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TranslationPipeline">TranslationPipeline</a>.</li>
<li><code>"summarization"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.SummarizationPipeline">SummarizationPipeline</a>.</li>
<li><code>"zero-shot-classification"</code>: will return a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ZeroShotClassificationPipeline">ZeroShotClassificationPipeline</a>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>, <em>optional</em>) —
The model that will be used by the pipeline to make predictions. This can be a model identifier or an
actual instance of a pretrained model inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> (for PyTorch) or
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> (for TensorFlow).</p>
<p>If not provided, the default for the <code>task</code> will be loaded.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) —
The configuration that will be used by the pipeline to instantiate the model. This can be a model
identifier or an actual pretrained model configuration inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>.</p>
<p>If not provided, the default configuration file for the requested model will be used. That means that if
<code>model</code> is given, its default configuration will be used. However, if <code>model</code> is not supplied, this
<code>task</code>’s default model’s config is used instead.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>, <em>optional</em>) —
The tokenizer that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained tokenizer inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.</p>
<p>If not provided, the default tokenizer for the given <code>model</code> will be loaded (if it is a string). If <code>model</code>
is not specified or not a string, then the default tokenizer for <code>config</code> is loaded (if it is a string).
However, if <code>config</code> is also not given or not a string, then the default tokenizer for the given <code>task</code>
will be loaded.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.feature_extractor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.feature_extractor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>feature_extractor</strong> (<code>str</code> or <code>PreTrainedFeatureExtractor</code>, <em>optional</em>) —
The feature extractor that will be used by the pipeline to encode data for the model. This can be a model
identifier or an actual pretrained feature extractor inheriting from <code>PreTrainedFeatureExtractor</code>.</p>
<p>Feature extractors are used for non-NLP models, such as Speech or Vision models as well as multi-modal
models. Multi-modal models will also require a tokenizer to be passed.</p>
<p>If not provided, the default feature extractor for the given <code>model</code> will be loaded (if it is a string). If
<code>model</code> is not specified or not a string, then the default feature extractor for <code>config</code> is loaded (if it
is a string). However, if <code>config</code> is also not given or not a string, then the default feature extractor
for the given <code>task</code> will be loaded.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.revision" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.revision"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) —
When passing a task name or a string model identifier: The specific model version to use. It can be a
branch name, a tag name, or a commit id, since we use a git-based system for storing models and other
artifacts on huggingface.co, so <code>revision</code> can be any identifier allowed by git.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.use_fast" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.use_fast"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to use a Fast tokenizer if possible (a <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipeline.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipeline.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>).
model_kwargs —
Additional dictionary of keyword arguments passed along to the model’s <code>from_pretrained(..., **model_kwargs)</code> function.
kwargs —
Additional keyword arguments passed along to the specific pipeline init (see the documentation for the
corresponding pipeline class for possible values).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.pipeline.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Pipeline"
>Pipeline</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A suitable pipeline for the task.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Utility factory method to build a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.</p>
<p>Pipelines are made of:</p>
<ul><li>A <a href="tokenizer">tokenizer</a> in charge of mapping raw textual input to token.</li>
<li>A <a href="model">model</a> to make predictions from the inputs.</li>
<li>Some (optional) post processing for enhancing model’s output.</li></ul>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline, AutoModelForTokenClassification, AutoTokenizer
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Sentiment analysis pipeline</span>
<span class="hljs-meta">>>> </span>pipeline(<span class="hljs-string">"sentiment-analysis"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Question answering pipeline, specifying the checkpoint identifier</span>
<span class="hljs-meta">>>> </span>pipeline(<span class="hljs-string">"question-answering"</span>, model=<span class="hljs-string">"distilbert-base-cased-distilled-squad"</span>, tokenizer=<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Named entity recognition pipeline, passing in a specific model and tokenizer</span>
<span class="hljs-meta">>>> </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"dbmdz/bert-large-cased-finetuned-conll03-english"</span>)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span>pipeline(<span class="hljs-string">"ner"</span>, model=model, tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div></div>
<h2 class="relative group"><a id="pipeline-batching" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline-batching"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipeline batching
</span></h2>
<p>All pipelines can use batching. This will work
whenever the pipeline uses its streaming ability (so when passing lists or <code>Dataset</code> or <code>generator</code>).</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-keyword">from</span> transformers.pipelines.pt_utils <span class="hljs-keyword">import</span> KeyDataset
<span class="hljs-keyword">import</span> datasets
dataset = datasets.load_dataset(<span class="hljs-string">"imdb"</span>, name=<span class="hljs-string">"plain_text"</span>, split=<span class="hljs-string">"unsupervised"</span>)
pipe = pipeline(<span class="hljs-string">"text-classification"</span>, device=<span class="hljs-number">0</span>)
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> pipe(KeyDataset(dataset, <span class="hljs-string">"text"</span>), batch_size=<span class="hljs-number">8</span>, truncation=<span class="hljs-string">"only_first"</span>):
<span class="hljs-built_in">print</span>(out)
<span class="hljs-comment"># [{'label': 'POSITIVE', 'score': 0.9998743534088135}]</span>
<span class="hljs-comment"># Exactly the same output as before, but the content are passed</span>
<span class="hljs-comment"># as batches to the model</span><!-- HTML_TAG_END --></pre></div>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>However, this is not automatically a win for performance. It can be either a 10x speedup or 5x slowdown depending
on hardware, data and the actual model being used.</p>
<p>Example where it’s mostly a speedup:</p></div>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-keyword">from</span> torch.utils.data <span class="hljs-keyword">import</span> Dataset
<span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm
pipe = pipeline(<span class="hljs-string">"text-classification"</span>, device=<span class="hljs-number">0</span>)
<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyDataset</span>(<span class="hljs-title class_ inherited__">Dataset</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__len__</span>(<span class="hljs-params">self</span>):
<span class="hljs-keyword">return</span> <span class="hljs-number">5000</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__getitem__</span>(<span class="hljs-params">self, i</span>):
<span class="hljs-keyword">return</span> <span class="hljs-string">"This is a test"</span>
dataset = MyDataset()
<span class="hljs-keyword">for</span> batch_size <span class="hljs-keyword">in</span> [<span class="hljs-number">1</span>, <span class="hljs-number">8</span>, <span class="hljs-number">64</span>, <span class="hljs-number">256</span>]:
<span class="hljs-built_in">print</span>(<span class="hljs-string">"-"</span> * <span class="hljs-number">30</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Streaming batch_size=<span class="hljs-subst">{batch_size}</span>"</span>)
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> tqdm(pipe(dataset, batch_size=batch_size), total=<span class="hljs-built_in">len</span>(dataset)):
<span class="hljs-keyword">pass</span><!-- HTML_TAG_END --></pre></div>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-section"># On GTX 970
------------------------------</span>
Streaming no batching
100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s]
<span class="hljs-code">------------------------------
Streaming batch_size=8
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s]
------------------------------</span>
Streaming batch<span class="hljs-emphasis">_size=64
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s]
------------------------------
Streaming batch_</span>size=256
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s]
(diminishing returns, saturated the GPU)<!-- HTML_TAG_END --></pre></div>
<p>Example where it’s most a slowdown:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">MyDataset</span>(<span class="hljs-title class_ inherited__">Dataset</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__len__</span>(<span class="hljs-params">self</span>):
<span class="hljs-keyword">return</span> <span class="hljs-number">5000</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">__getitem__</span>(<span class="hljs-params">self, i</span>):
<span class="hljs-keyword">if</span> i % <span class="hljs-number">64</span> == <span class="hljs-number">0</span>:
n = <span class="hljs-number">100</span>
<span class="hljs-keyword">else</span>:
n = <span class="hljs-number">1</span>
<span class="hljs-keyword">return</span> <span class="hljs-string">"This is a test"</span> * n<!-- HTML_TAG_END --></pre></div>
<p>This is a occasional very long sentence compared to the other. In that case, the <strong>whole</strong> batch will need to be 400
tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on
bigger batches, the program simply crashes.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-comment">------------------------------</span>
Streaming no batching
<span class="hljs-number">100</span>%|█████████████████████████████████████████████████████████████████████| <span class="hljs-number">1000</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">05</span><<span class="hljs-number">00</span>:<span class="hljs-number">00</span>, <span class="hljs-number">183.69</span><span class="hljs-keyword">it</span>/s]
<span class="hljs-comment">------------------------------</span>
Streaming batch_size=<span class="hljs-number">8</span>
<span class="hljs-number">100</span>%|█████████████████████████████████████████████████████████████████████| <span class="hljs-number">1000</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">03</span><<span class="hljs-number">00</span>:<span class="hljs-number">00</span>, <span class="hljs-number">265.74</span><span class="hljs-keyword">it</span>/s]
<span class="hljs-comment">------------------------------</span>
Streaming batch_size=<span class="hljs-number">64</span>
<span class="hljs-number">100</span>%|██████████████████████████████████████████████████████████████████████| <span class="hljs-number">1000</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">26</span><<span class="hljs-number">00</span>:<span class="hljs-number">00</span>, <span class="hljs-number">37.80</span><span class="hljs-keyword">it</span>/s]
<span class="hljs-comment">------------------------------</span>
Streaming batch_size=<span class="hljs-number">256</span>
<span class="hljs-number">0</span>%| | <span class="hljs-number">0</span>/<span class="hljs-number">1000</span> [<span class="hljs-number">00</span>:<span class="hljs-number">00</span><span class="hljs-meta"><?</span>, ?<span class="hljs-keyword">it</span>/s]
Traceback (most recent call <span class="hljs-keyword">last</span>):
File <span class="hljs-string">"/home/nicolas/src/transformers/test.py"</span>, <span class="hljs-built_in">line</span> <span class="hljs-number">42</span>, <span class="hljs-keyword">in</span> <module>
<span class="hljs-keyword">for</span> out <span class="hljs-keyword">in</span> tqdm(pipe(dataset, batch_size=<span class="hljs-number">256</span>), total=<span class="hljs-built_in">len</span>(dataset)):
....
q = q / math.<span class="hljs-built_in">sqrt</span>(dim_per_head) <span class="hljs-comment"># (bs, n_heads, q_length, dim_per_head)</span>
RuntimeError: CUDA out <span class="hljs-keyword">of</span> memory. Tried <span class="hljs-built_in">to</span> allocate <span class="hljs-number">376.00</span> MiB (GPU <span class="hljs-number">0</span>; <span class="hljs-number">3.95</span> GiB total capacity; <span class="hljs-number">1.72</span> GiB already allocated; <span class="hljs-number">354.88</span> MiB free; <span class="hljs-number">2.46</span> GiB reserved <span class="hljs-keyword">in</span> total <span class="hljs-keyword">by</span> PyTorch)<!-- HTML_TAG_END --></pre></div>
<p>There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of
thumb:</p>
<p>For users, a rule of thumb is:</p>
<ul><li><p><strong>Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the
only way to go.</strong></p></li>
<li><p>If you are latency constrained (live product doing inference), don’t batch</p></li>
<li><p>If you are using CPU, don’t batch.</p></li>
<li><p>If you are using throughput (you want to run your model on a bunch of static data), on GPU, then:</p>
<ul><li>If you have no clue about the size of the sequence_length (“natural” data), by default don’t batch, measure and
try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don’t
control the sequence_length.)</li>
<li>If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push
it until you get OOMs.</li>
<li>The larger the GPU the more likely batching is going to be more interesting</li></ul></li>
<li><p>As soon as you enable batching, make sure you can handle OOMs nicely.</p></li></ul>
<h2 class="relative group"><a id="pipeline-chunk-batching" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline-chunk-batching"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipeline chunk batching
</span></h2>
<p><code>zero-shot-classification</code> and <code>question-answering</code> are slightly specific in the sense, that a single input might yield
multiple forward pass of a model. Under normal circumstances, this would yield issues with <code>batch_size</code> argument.</p>
<p>In order to circumvent this issue, both of these pipelines are a bit specific, they are <code>ChunkPipeline</code> instead of
regular <code>Pipeline</code>. In short:</p>
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<pre><!-- HTML_TAG_START -->preprocessed = pipe.preprocess(inputs)
model_outputs = pipe.forward(preprocessed)
outputs = pipe.postprocess(model_outputs)<!-- HTML_TAG_END --></pre></div>
<p>Now becomes:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->all_model_outputs = []
<span class="hljs-keyword">for</span> preprocessed <span class="hljs-keyword">in</span> pipe.preprocess(inputs):
model_outputs = pipe.forward(preprocessed)
all_model_outputs.append(model_outputs)
outputs = pipe.postprocess(all_model_outputs)<!-- HTML_TAG_END --></pre></div>
<p>This should be very transparent to your code because the pipelines are used in
the same way.</p>
<p>This is a simplified view, since the pipeline can handle automatically the batch to ! Meaning you don’t have to care
about how many forward passes you inputs are actually going to trigger, you can optimize the <code>batch_size</code>
independently of the inputs. The caveats from the previous section still apply.</p>
<h2 class="relative group"><a id="pipeline-custom-code" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline-custom-code"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipeline custom code
</span></h2>
<p>If you want to override a specific pipeline.</p>
<p>Don’t hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
cases, so <code>transformers</code> could maybe support your use case.</p>
<p>If you want to try simply you can:</p>
<ul><li>Subclass your pipeline of choice</li></ul>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">MyPipeline</span>(<span class="hljs-title class_ inherited__">TextClassificationPipeline</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">postprocess</span>():
<span class="hljs-comment"># Your code goes here</span>
scores = scores * <span class="hljs-number">100</span>
<span class="hljs-comment"># And here</span>
my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
<span class="hljs-comment"># or if you use *pipeline* function, then:</span>
my_pipeline = pipeline(model=<span class="hljs-string">"xxxx"</span>, pipeline_class=MyPipeline)<!-- HTML_TAG_END --></pre></div>
<p>That should enable you to do all the custom code you want.</p>
<h2 class="relative group"><a id="implementing-a-pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#implementing-a-pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Implementing a pipeline
</span></h2>
<p><a href="../add_new_pipeline">Implementing a new pipeline</a></p>
<h2 class="relative group"><a id="the-task-specific-pipelines" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#the-task-specific-pipelines"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>The task specific pipelines
</span></h2>
<h3 class="relative group"><a id="transformers.AudioClassificationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AudioClassificationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AudioClassificationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">AudioClassificationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.AudioClassificationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AudioClassificationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/audio_classification.py#L67" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Audio classification pipeline using any <code>AutoModelForAudioClassification</code>. This pipeline predicts the class of a
raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio
formats.</p>
<p>This pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"audio-classification"</code>.</p>
<p>See the list of available models on
<a href="https://huggingface.co/models?filter=audio-classification" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AudioClassificationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.AudioClassificationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AudioClassificationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/audio_classification.py#L90" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Union[numpy.ndarray, bytes, str]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list of <code>dict</code> with the following keys</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.__call__.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.__call__.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>np.ndarray</code> or <code>bytes</code> or <code>str</code>) —
The inputs is either a raw waveform (<code>np.ndarray</code> of shape (n, ) of type <code>np.float32</code> or <code>np.float64</code>)
at the correct sampling rate (no further check will be done) or a <code>str</code> that is the filename of the
audio file, the file will be read at the correct sampling rate to get the waveform using <em>ffmpeg</em>. This
requires <em>ffmpeg</em> to be installed on the system. If <em>inputs</em> is <code>bytes</code> it is supposed to be the
content of an audio file and is interpreted by <em>ffmpeg</em> in the same way.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AudioClassificationPipeline.__call__.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AudioClassificationPipeline.__call__.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to None) —
The number of top labels that will be returned by the pipeline. If the provided number is <code>None</code> or
higher than the number of labels available in the model configuration, it will default to the number of
labels.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.AudioClassificationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list of <code>dict</code> with the following keys</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<ul>
<li><strong>label</strong> (<code>str</code>) — The label predicted.</li>
<li><strong>score</strong> (<code>float</code>) — The corresponding probability.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Classify the sequence(s) given as inputs. See the <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline">AutomaticSpeechRecognitionPipeline</a> documentation for more
information.</p></div></div>
<h3 class="relative group"><a id="transformers.AutomaticSpeechRecognitionPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AutomaticSpeechRecognitionPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutomaticSpeechRecognitionPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AutomaticSpeechRecognitionPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">AutomaticSpeechRecognitionPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.AutomaticSpeechRecognitionPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AutomaticSpeechRecognitionPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/automatic_speech_recognition.py#L69" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature_extractor<span class="opacity-60">: typing.Union[ForwardRef('SequenceFeatureExtractor'), str]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Pipeline that aims at extracting spoken text contained within some audio.</p>
<p>The input can be either a raw waveform or a audio file. In case of the audio file, ffmpeg should be installed for
to support multiple audio formats</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AutomaticSpeechRecognitionPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.AutomaticSpeechRecognitionPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AutomaticSpeechRecognitionPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/automatic_speech_recognition.py#L127" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Union[numpy.ndarray, bytes, str]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AutomaticSpeechRecognitionPipeline.__call__.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AutomaticSpeechRecognitionPipeline.__call__.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>np.ndarray</code> or <code>bytes</code> or <code>str</code> or <code>dict</code>) —
The inputs is either :<ul>
<li><code>str</code> that is the filename of the audio file, the file will be read at the correct sampling rate
to get the waveform using <em>ffmpeg</em>. This requires <em>ffmpeg</em> to be installed on the system.</li>
<li><code>bytes</code> it is supposed to be the content of an audio file and is interpreted by <em>ffmpeg</em> in the
same way.</li>
<li>(<code>np.ndarray</code> of shape (n, ) of type <code>np.float32</code> or <code>np.float64</code>)
Raw audio at the correct sampling rate (no further check will be done)</li>
<li><code>dict</code> form can be used to pass raw audio sampled at arbitrary <code>sampling_rate</code> and let this
pipeline do the resampling. The dict must be in the format <code>{"sampling_rate": int, "raw": np.array}</code> with optionally a <code>"stride": (left: int, right: int)</code> than can ask the pipeline to
treat the first <code>left</code> samples and last <code>right</code> samples to be ignored in decoding (but used at
inference to provide more context to the model). Only use <code>stride</code> with CTC models.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AutomaticSpeechRecognitionPipeline.__call__.return_timestamps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AutomaticSpeechRecognitionPipeline.__call__.return_timestamps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_timestamps</strong> (<em>optional</em>, <code>str</code>) —
Only available for pure CTC models. If set to <code>"char"</code>, the pipeline will return <code>timestamps</code> along the
text for every character in the text. For instance if you get <code>[{"text": "h", "timestamps": (0.5,0.6), {"text": "i", "timestamps": (0.7, .9)}]</code>, then it means the model predicts that the letter “h” was
pronounced after <code>0.5</code> and before <code>0.6</code> seconds. If set to <code>"word"</code>, the pipeline will return
<code>timestamps</code> along the text for every word in the text. For instance if you get <code>[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, .1.5)}]</code>, then it means the model
predicts that the word “hi” was pronounces before 0.5 and after 0.9 seconds.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.AutomaticSpeechRecognitionPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A dictionary with the following keys:</p>
<ul>
<li><strong>text</strong> (<code>str</code> ) — The recognized text.</li>
<li><strong>chunks</strong> (<em>optional(, <code>List[Dict]</code>)
When using <code>return_timestamps</code>, the <code>chunks</code> will become a list containing all the various text
chunks identified by the model, </em>e.g.* <code>[{"text": "hi ", "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, 1.5)}]</code>. The original full text can roughly be recovered by doing
<code>"".join(chunk["text"] for chunk in output["chunks"])</code>.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Classify the sequence(s) given as inputs. See the <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline">AutomaticSpeechRecognitionPipeline</a> documentation for more
information.</p></div></div>
<h3 class="relative group"><a id="transformers.Conversation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ConversationalPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Conversation"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Conversation</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Conversation" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Conversation"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L19" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">conversation_id<span class="opacity-60">: UUID = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_user_inputs<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">generated_responses<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conversation.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <em>optional</em>) —
The initial user input to start the conversation. If not provided, a user input needs to be provided
manually using the <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation.add_user_input">add_user_input()</a> method before the conversation can begin.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conversation.conversation_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation.conversation_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>conversation_id</strong> (<code>uuid.UUID</code>, <em>optional</em>) —
Unique identifier for the conversation. If not provided, a random UUID4 id will be assigned to the
conversation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conversation.past_user_inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation.past_user_inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_user_inputs</strong> (<code>List[str]</code>, <em>optional</em>) —
Eventual past history of the conversation of the user. You don’t need to pass it manually if you use the
pipeline interactively but if you want to recreate history you need to set both <code>past_user_inputs</code> and
<code>generated_responses</code> with equal length lists of strings<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conversation.generated_responses" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation.generated_responses"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>generated_responses</strong> (<code>List[str]</code>, <em>optional</em>) —
Eventual past history of the conversation of the model. You don’t need to pass it manually if you use the
pipeline interactively but if you want to recreate history you need to set both <code>past_user_inputs</code> and
<code>generated_responses</code> with equal length lists of strings<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Utility class containing a conversation and its history. This class is meant to be used as an input to the
<a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ConversationalPipeline">ConversationalPipeline</a>. The conversation contains a number of utility function to manage the addition of new
user input and generated model responses. A conversation needs to contain an unprocessed user input before being
passed to the <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ConversationalPipeline">ConversationalPipeline</a>. This user input is either created when the class is instantiated, or by
calling <code>conversational_pipeline.append_response("input")</code> after a conversation turn.</p>
<p>Usage:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->conversation = Conversation(<span class="hljs-string">"Going to the movies tonight - any suggestions?"</span>)
<span class="hljs-comment"># Steps usually performed by the model when generating a response:</span>
<span class="hljs-comment"># 1. Mark the user input as processed (moved to the history)</span>
conversation.mark_processed()
<span class="hljs-comment"># 2. Append a mode response</span>
conversation.append_response(<span class="hljs-string">"The Big lebowski."</span>)
conversation.add_user_input(<span class="hljs-string">"Is it good?"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Conversation.add_user_input"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>add_user_input</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Conversation.add_user_input" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Conversation.add_user_input"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L83" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conversation.add_user_input.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation.add_user_input.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>) — The user input for the next conversation round.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conversation.add_user_input.overwrite" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation.add_user_input.overwrite"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not existing and unprocessed user input should be overwritten when this function is called.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Add a user input to the conversation for the next round. This populates the internal <code>new_user_input</code> field.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Conversation.append_response"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>append_response</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Conversation.append_response" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Conversation.append_response"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L116" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">response<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conversation.append_response.response" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conversation.append_response.response"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>response</strong> (<code>str</code>) — The model generated response.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Append a response to the list of generated responses.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Conversation.iter_texts"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>iter_texts</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Conversation.iter_texts" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Conversation.iter_texts"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L125" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Iterates over all blobs of the conversation.</p>
<p>Returns: Iterator of (is_user, text_chunk) in chronological order of the conversation. <code>is_user</code> is a <code>bool</code>,
<code>text_chunks</code> is a <code>str</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Conversation.mark_processed"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>mark_processed</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Conversation.mark_processed" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Conversation.mark_processed"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L107" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Mark the conversation as processed (moves the content of <code>new_user_input</code> to <code>past_user_inputs</code>) and empties
the <code>new_user_input</code> field.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConversationalPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ConversationalPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ConversationalPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConversationalPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L164" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.min_length_for_response" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.min_length_for_response"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_length_for_response</strong> (<code>int</code>, <em>optional</em>, defaults to 32) —
The minimum length (in number of tokens) for a response.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.minimum_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.minimum_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>minimum_tokens</strong> (<code>int</code>, <em>optional</em>, defaults to 10) —
The minimum length of tokens to leave for a response.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Multi-turn conversational pipeline.</p>
<p>This conversational pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"conversational"</code>.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a multi-turn conversational task,
currently: <em>‘microsoft/DialoGPT-small’</em>, <em>‘microsoft/DialoGPT-medium’</em>, <em>‘microsoft/DialoGPT-large’</em>. See the
up-to-date list of available models on
<a href="https://huggingface.co/models?filter=conversational" rel="nofollow">huggingface.co/models</a>.</p>
<p>Usage:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->conversational_pipeline = pipeline(<span class="hljs-string">"conversational"</span>)
conversation_1 = Conversation(<span class="hljs-string">"Going to the movies tonight - any suggestions?"</span>)
conversation_2 = Conversation(<span class="hljs-string">"What's the last book you have read?"</span>)
conversational_pipeline([conversation_1, conversation_2])
conversation_1.add_user_input(<span class="hljs-string">"Is it an action movie?"</span>)
conversation_2.add_user_input(<span class="hljs-string">"What is the genre of this book?"</span>)
conversational_pipeline([conversation_1, conversation_2])<!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConversationalPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ConversationalPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConversationalPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/conversational.py#L219" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">conversations<span class="opacity-60">: typing.Union[transformers.pipelines.conversational.Conversation, typing.List[transformers.pipelines.conversational.Conversation]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_workers<span class="opacity-60"> = 0</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation"
>Conversation</a> or a list of <a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation"
>Conversation</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.__call__.conversations" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.__call__.conversations"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>conversations</strong> (a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation">Conversation</a> or a list of <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation">Conversation</a>) —
Conversations to generate responses for.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConversationalPipeline.__call__.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConversationalPipeline.__call__.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.ConversationalPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation"
>Conversation</a> or a list of <a
href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Conversation"
>Conversation</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Conversation(s) with updated generated responses for those
containing a new user input.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Generate responses for the conversation(s) given as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.FeatureExtractionPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FeatureExtractionPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FeatureExtractionPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FeatureExtractionPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FeatureExtractionPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FeatureExtractionPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/feature_extraction.py#L7" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature_extractor<span class="opacity-60">: typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">modelcard<span class="opacity-60">: typing.Optional[transformers.modelcard.ModelCard] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_parser<span class="opacity-60">: ArgumentHandler = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">binary_output<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
transformer, which can be used as features in downstream tasks.</p>
<p>This feature extraction pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the task identifier:
<code>"feature-extraction"</code>.</p>
<p>All models may be used for this pipeline. See a list of all models, including community-contributed models on
<a href="https://huggingface.co/models" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FeatureExtractionPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FeatureExtractionPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FeatureExtractionPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/feature_extraction.py#L69" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A nested list of <code>float</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FeatureExtractionPipeline.__call__.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FeatureExtractionPipeline.__call__.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>str</code> or <code>List[str]</code>) — One or several texts (or one list of texts) to get the features of.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.FeatureExtractionPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A nested list of <code>float</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The features computed by the model.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Extract the features of the input(s).</p></div></div>
<h3 class="relative group"><a id="transformers.FillMaskPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FillMaskPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FillMaskPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FillMaskPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FillMaskPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FillMaskPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/fill_mask.py#L33" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature_extractor<span class="opacity-60">: typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">modelcard<span class="opacity-60">: typing.Optional[transformers.modelcard.ModelCard] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_parser<span class="opacity-60">: ArgumentHandler = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">binary_output<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, defaults to 5) —
The number of predictions to return.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.targets" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.targets"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>targets</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) —
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Masked language modeling prediction pipeline using any <code>ModelWithLMHead</code>. See the <a href="../task_summary#masked-language-modeling">masked language modeling
examples</a> for more information.</p>
<p>This mask filling pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"fill-mask"</code>.</p>
<p>The models that this pipeline can use are models that have been trained with a masked language modeling objective,
which includes the bi-directional models in the library. See the up-to-date list of available models on
<a href="https://huggingface.co/models?filter=fill-mask" rel="nofollow">huggingface.co/models</a>.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>This pipeline only works for inputs with exactly one token masked. Experimental: We added support for multiple
masks. The returned values are raw model output, and correspond to disjoint probabilities where one might expect
joint probabilities (See <a href="https://github.com/huggingface/transformers/pull/10222" rel="nofollow">discussion</a>).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FillMaskPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FillMaskPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FillMaskPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/fill_mask.py#L204" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list or a list of list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.__call__.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.__call__.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
One or several texts (or one list of prompts) with masked tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.__call__.targets" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.__call__.targets"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>targets</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) —
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first
resulting token will be used (with a warning, and that might be slower).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FillMaskPipeline.__call__.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FillMaskPipeline.__call__.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>) —
When passed, overrides the number of predictions to return.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.FillMaskPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list or a list of list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as list of dictionaries with the following keys:</p>
<ul>
<li><strong>sequence</strong> (<code>str</code>) — The corresponding input with the mask token prediction.</li>
<li><strong>score</strong> (<code>float</code>) — The corresponding probability.</li>
<li><strong>token</strong> (<code>int</code>) — The predicted token id (to replace the masked one).</li>
<li><strong>token</strong> (<code>str</code>) — The predicted token (to replace the masked one).</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Fill the masked token in the text(s) given as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.ImageClassificationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ImageClassificationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageClassificationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ImageClassificationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ImageClassificationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageClassificationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_classification.py#L31" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Image classification pipeline using any <code>AutoModelForImageClassification</code>. This pipeline predicts the class of an
image.</p>
<p>This image classification pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"image-classification"</code>.</p>
<p>See the list of available models on
<a href="https://huggingface.co/models?filter=image-classification" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageClassificationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ImageClassificationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageClassificationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_classification.py#L58" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">images<span class="opacity-60">: typing.Union[str, typing.List[str], ForwardRef('Image.Image'), typing.List[ForwardRef('Image.Image')]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.__call__.images" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.__call__.images"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing a http link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul>
<p>The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
images.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageClassificationPipeline.__call__.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageClassificationPipeline.__call__.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 5) —
The number of top labels that will be returned by the pipeline. If the provided number is higher than
the number of labels available in the model configuration, it will default to the number of labels.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Assign labels to the image(s) passed as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.ImageSegmentationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ImageSegmentationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageSegmentationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ImageSegmentationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ImageSegmentationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageSegmentationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_segmentation.py#L34" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Image segmentation pipeline using any <code>AutoModelForXXXSegmentation</code>. This pipeline predicts masks of objects and
their classes.</p>
<p>This image segmentation pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"image-segmentation"</code>.</p>
<p>See the list of available models on
<a href="https://huggingface.co/models?filter=image-segmentation" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ImageSegmentationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ImageSegmentationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ImageSegmentationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/image_segmentation.py#L69" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.__call__.images" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.__call__.images"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing an HTTP(S) link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul>
<p>The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
same format: all as HTTP(S) links, all as local paths, or all as PIL images.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.__call__.threshold" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.__call__.threshold"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The probability necessary to make a prediction.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ImageSegmentationPipeline.__call__.mask_threshold" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ImageSegmentationPipeline.__call__.mask_threshold"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask_threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) —
Threshold to use when turning the predicted masks into binary values.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Perform segmentation (detect masks & classes) in the image(s) passed as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.TokenClassificationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>NerPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TokenClassificationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L86" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_parser<span class="opacity-60"> = <transformers.pipelines.token_classification.TokenClassificationArgumentHandler object at 0x7f45b52f37c0></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.ignore_labels" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.ignore_labels"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_labels</strong> (<code>List[str]</code>, defaults to <code>["O"]</code>) —
A list of labels to ignore.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.grouped_entities" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.grouped_entities"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>grouped_entities</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
DEPRECATED, use <code>aggregation_strategy</code> instead. Whether or not to group the tokens corresponding to the
same entity together in the predictions or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.aggregation_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.aggregation_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>aggregation_strategy</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"none"</code>) —
The strategy to fuse (or not) tokens based on the model prediction.</p>
<ul>
<li>“none” : Will simply not do any aggregation and simply return raw results from the model</li>
<li>“simple” : Will attempt to group entities following the default schema. (A, B-TAG), (B, I-TAG), (C,
I-TAG), (D, B-TAG2) (E, B-TAG2) will end up being [{“word”: ABC, “entity”: “TAG”}, {“word”: “D”,
“entity”: “TAG2”}, {“word”: “E”, “entity”: “TAG2”}] Notice that two consecutive B tags will end up as
different entities. On word based languages, we might end up splitting words undesirably : Imagine
Microsoft being tagged as [{“word”: “Micro”, “entity”: “ENTERPRISE”}, {“word”: “soft”, “entity”:
“NAME”}]. Look for FIRST, MAX, AVERAGE for ways to mitigate that and disambiguate words (on languages
that support that meaning, which is basically tokens separated by a space). These mitigations will
only work on real words, “New york” might still be tagged with two different entities.</li>
<li>“first” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Words will simply use the tag of the first token of the word when there
is ambiguity.</li>
<li>“average” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words,
cannot end up with different tags. scores will be averaged first across tokens, and then the maximum
label is applied.</li>
<li>“max” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Word entity will simply be the token with the maximum score.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Named Entity Recognition pipeline using any <code>ModelForTokenClassification</code>. See the <a href="../task_summary#named-entity-recognition">named entity recognition
examples</a> for more information.</p>
<p>This token recognition pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"ner"</code> (for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous).</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a token classification task. See the
up-to-date list of available models on
<a href="https://huggingface.co/models?filter=token-classification" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.aggregate_words"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>aggregate_words</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.aggregate_words" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.aggregate_words"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L366" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">entities<span class="opacity-60">: typing.List[dict]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">aggregation_strategy<span class="opacity-60">: AggregationStrategy</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Override tokens from a given word that disagree to force agreement on word boundaries.</p>
<p>Example: micro|soft| com|pany| B-ENT I-NAME I-ENT I-ENT will be rewritten with first strategy as microsoft|
company| B-ENT I-ENT</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.gather_pre_entities"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>gather_pre_entities</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.gather_pre_entities" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.gather_pre_entities"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L252" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sentence<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">offset_mapping<span class="opacity-60">: typing.Union[typing.List[typing.Tuple[int, int]], NoneType]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens_mask<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">aggregation_strategy<span class="opacity-60">: AggregationStrategy</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Fuse various numpy arrays into dicts with all the information needed for aggregation</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.group_entities"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>group_entities</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.group_entities" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.group_entities"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L428" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">entities<span class="opacity-60">: typing.List[dict]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.group_entities.entities" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.group_entities.entities"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Find and group together the adjacent tokens with the same entity predicted.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.group_sub_entities"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>group_sub_entities</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.group_sub_entities" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.group_sub_entities"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L393" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">entities<span class="opacity-60">: typing.List[dict]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.group_sub_entities.entities" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.group_sub_entities.entities"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Group together the adjacent tokens with the same entity predicted.</p></div></div>
<p>See <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.TokenClassificationPipeline">TokenClassificationPipeline</a> for all details.</p>
<h3 class="relative group"><a id="transformers.ObjectDetectionPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ObjectDetectionPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ObjectDetectionPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ObjectDetectionPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ObjectDetectionPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ObjectDetectionPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/object_detection.py#L25" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Object detection pipeline using any <code>AutoModelForObjectDetection</code>. This pipeline predicts bounding boxes of objects
and their classes.</p>
<p>This object detection pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"object-detection"</code>.</p>
<p>See the list of available models on <a href="https://huggingface.co/models?filter=object-detection" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ObjectDetectionPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ObjectDetectionPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ObjectDetectionPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/object_detection.py#L51" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.__call__.images" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.__call__.images"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing an HTTP(S) link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul>
<p>The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
same format: all as HTTP(S) links, all as local paths, or all as PIL images.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ObjectDetectionPipeline.__call__.threshold" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ObjectDetectionPipeline.__call__.threshold"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The probability necessary to make a prediction.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Detect objects (bounding boxes & classes) in the image(s) passed as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.QuestionAnsweringPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>QuestionAnsweringPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.QuestionAnsweringPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">QuestionAnsweringPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.QuestionAnsweringPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.QuestionAnsweringPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L102" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">modelcard<span class="opacity-60">: typing.Optional[transformers.modelcard.ModelCard] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Question Answering pipeline using any <code>ModelForQuestionAnswering</code>. See the <a href="../task_summary#question-answering">question answering
examples</a> for more information.</p>
<p>This question answering pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"question-answering"</code>.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a question answering task. See the
up-to-date list of available models on
<a href="https://huggingface.co/models?filter=question-answering" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.QuestionAnsweringPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.QuestionAnsweringPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.QuestionAnsweringPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L205" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A <code>dict</code> or a list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>SquadExample</code>or a list of <code>SquadExample</code> —
One or several <code>SquadExample</code>containing the question and context.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.X" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.X"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>X</strong> (<code>SquadExample</code>or a list of <code>SquadExample</code> <em>optional</em>) —
One or several <code>SquadExample</code>containing the question and context (will be treated the same way as if
passed as the first positional argument).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>SquadExample</code>or a list of <code>SquadExample</code> <em>optional</em>) —
One or several <code>SquadExample</code>containing the question and context (will be treated the same way as if
passed as the first positional argument).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.question" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.question"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>question</strong> (<code>str</code> or <code>List[str]</code>) —
One or several question(s) (must be used in conjunction with the <code>context</code> argument).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.context" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.context"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>context</strong> (<code>str</code> or <code>List[str]</code>) —
One or several context(s) associated with the question(s) (must be used in conjunction with the
<code>question</code> argument).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.topk" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.topk"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>topk</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The number of answers to return (will be chosen by order of likelihood). Note that we return less than
topk answers if there are not enough options available within the context.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.doc_stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.doc_stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>doc_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 128) —
If the context is too long to fit with the question for the model, it will be split in several chunks
with some overlap. This argument controls the size of that overlap.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.max_answer_len" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.max_answer_len"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_answer_len</strong> (<code>int</code>, <em>optional</em>, defaults to 15) —
The maximum length of predicted answers (e.g., only answers with a shorter length are considered).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.max_seq_len" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.max_seq_len"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_seq_len</strong> (<code>int</code>, <em>optional</em>, defaults to 384) —
The maximum length of the total sentence (context + question) after tokenization. The context will be
split in several chunks (using <code>doc_stride</code>) if needed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.max_question_len" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.max_question_len"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_question_len</strong> (<code>int</code>, <em>optional</em>, defaults to 64) —
The maximum length of the question after tokenization. It will be truncated if needed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.__call__.handle_impossible_answer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.__call__.handle_impossible_answer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>handle_impossible_answer</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not we accept impossible as an answer.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.QuestionAnsweringPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A <code>dict</code> or a list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>score</strong> (<code>float</code>) — The probability associated to the answer.</li>
<li><strong>start</strong> (<code>int</code>) — The character start index of the answer (in the tokenized version of the input).</li>
<li><strong>end</strong> (<code>int</code>) — The character end index of the answer (in the tokenized version of the input).</li>
<li><strong>answer</strong> (<code>str</code>) — The answer to the question.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Answer the question(s) given as inputs by using the context(s).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.QuestionAnsweringPipeline.create_sample"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>create_sample</span></h4><!-- HTML_TAG_END -->
<a id="transformers.QuestionAnsweringPipeline.create_sample" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.QuestionAnsweringPipeline.create_sample"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L143" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">question<span class="opacity-60">: typing.Union[str, typing.List[str]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">context<span class="opacity-60">: typing.Union[str, typing.List[str]]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.create_sample.question" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.create_sample.question"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>question</strong> (<code>str</code> or <code>List[str]</code>) — The question(s) asked.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.create_sample.context" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.create_sample.context"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>context</strong> (<code>str</code> or <code>List[str]</code>) — The context(s) in which we will look for the answer.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>QuestionAnsweringPipeline leverages the <code>SquadExample</code>internally. This helper method encapsulate all the
logic for converting question(s) and context(s) to <code>SquadExample</code></p>
<p>We currently support extractive question answering.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.QuestionAnsweringPipeline.decode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>decode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.QuestionAnsweringPipeline.decode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.QuestionAnsweringPipeline.decode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L481" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">topk<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_answer_len<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">undesired_tokens<span class="opacity-60">: ndarray</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.decode.start" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.decode.start"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start</strong> (<code>np.ndarray</code>) — Individual start probabilities for each token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.decode.end" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.decode.end"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end</strong> (<code>np.ndarray</code>) — Individual end probabilities for each token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.decode.topk" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.decode.topk"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>topk</strong> (<code>int</code>) — Indicates how many possible answer span(s) to extract from the model output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.decode.max_answer_len" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.decode.max_answer_len"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_answer_len</strong> (<code>int</code>) — Maximum size of the answer to extract from the model’s output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.decode.undesired_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.decode.undesired_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>undesired_tokens</strong> (<code>np.ndarray</code>) — Mask determining tokens that can be part of the answer<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Take the output of any <code>ModelForQuestionAnswering</code> and will generate probabilities for each span to be the
actual answer.</p>
<p>In addition, it filters out some unwanted/impossible cases like answer len being greater than max_answer_len or
answer end position being before the starting position. The method supports output the k-best answer through
the topk argument.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.QuestionAnsweringPipeline.span_to_answer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>span_to_answer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.QuestionAnsweringPipeline.span_to_answer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.QuestionAnsweringPipeline.span_to_answer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L530" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>Dictionary like `{‘answer’</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.span_to_answer.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.span_to_answer.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>) — The actual context to extract the answer from.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.span_to_answer.start" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.span_to_answer.start"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start</strong> (<code>int</code>) — The answer starting token index.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.QuestionAnsweringPipeline.span_to_answer.end" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.QuestionAnsweringPipeline.span_to_answer.end"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end</strong> (<code>int</code>) — The answer end token index.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.QuestionAnsweringPipeline.span_to_answer.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>Dictionary like `{‘answer’</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>str, ‘start’: int, ‘end’: int}`</p>
<!-- HTML_TAG_END --></p></div></div>
<p>When decoding from token probabilities, this method maps token indexes to actual word in the initial context.</p></div></div>
<h3 class="relative group"><a id="transformers.SummarizationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>SummarizationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SummarizationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">SummarizationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.SummarizationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SummarizationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L186" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Summarize news articles and other documents.</p>
<p>This summarizing pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"summarization"</code>.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is
currently, ’<em>bart-large-cnn</em>’, ’<em>t5-small</em>’, ’<em>t5-base</em>’, ’<em>t5-large</em>’, ’<em>t5-3b</em>’, ’<em>t5-11b</em>’. See the up-to-date
list of available models on <a href="https://huggingface.co/models?filter=summarization" rel="nofollow">huggingface.co/models</a>.</p>
<p>Usage:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># use bart in pytorch</span>
summarizer = pipeline(<span class="hljs-string">"summarization"</span>)
summarizer(<span class="hljs-string">"An apple a day, keeps the doctor away"</span>, min_length=<span class="hljs-number">5</span>, max_length=<span class="hljs-number">20</span>)
<span class="hljs-comment"># use t5 in tf</span>
summarizer = pipeline(<span class="hljs-string">"summarization"</span>, model=<span class="hljs-string">"t5-base"</span>, tokenizer=<span class="hljs-string">"t5-base"</span>, framework=<span class="hljs-string">"tf"</span>)
summarizer(<span class="hljs-string">"An apple a day, keeps the doctor away"</span>, min_length=<span class="hljs-number">5</span>, max_length=<span class="hljs-number">20</span>)<!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SummarizationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.SummarizationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SummarizationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L212" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list or a list of list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.__call__.documents" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.__call__.documents"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>documents</strong> (<em>str</em> or <code>List[str]</code>) —
One or several articles (or one list of articles) to summarize.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.__call__.return_text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.__call__.return_text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.__call__.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.__call__.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SummarizationPipeline.__call__.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SummarizationPipeline.__call__.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.SummarizationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list or a list of list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>summary_text</strong> (<code>str</code>, present when <code>return_text=True</code>) — The summary of the corresponding input.</li>
<li><strong>summary_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) — The token
ids of the summary.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Summarize the text(s) given as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.TableQuestionAnsweringPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TableQuestionAnsweringPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TableQuestionAnsweringPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TableQuestionAnsweringPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TableQuestionAnsweringPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TableQuestionAnsweringPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/table_question_answering.py#L83" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_parser<span class="opacity-60"> = <transformers.pipelines.table_question_answering.TableQuestionAnsweringArgumentHandler object at 0x7f45b52d1cd0></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Table Question Answering pipeline using a <code>ModelForTableQuestionAnswering</code>. This pipeline is only available in
PyTorch.</p>
<p>This tabular question answering pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task
identifier: <code>"table-question-answering"</code>.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a tabular question answering task.
See the up-to-date list of available models on
<a href="https://huggingface.co/models?filter=table-question-answering" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TableQuestionAnsweringPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TableQuestionAnsweringPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TableQuestionAnsweringPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/table_question_answering.py#L244" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A dictionary or a list of dictionaries containing results</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.__call__.table" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.__call__.table"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>table</strong> (<code>pd.DataFrame</code> or <code>Dict</code>) —
Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
See above for an example of dictionary.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.__call__.query" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.__call__.query"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>query</strong> (<code>str</code> or <code>List[str]</code>) —
Query or list of queries that will be sent to the model alongside the table.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.__call__.sequential" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.__call__.sequential"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequential</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
inference to be done sequentially to extract relations within sequences, given their conversational
nature.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.__call__.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.__call__.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TableQuestionAnsweringPipeline.__call__.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TableQuestionAnsweringPipeline.__call__.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <code>TapasTruncationStrategy</code>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'drop_rows_to_fit'</code>: Truncate to a maximum length specified with the argument <code>max_length</code>
or to the maximum acceptable input length for the model if that argument is not provided. This will
truncate row by row, removing rows from the table.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TableQuestionAnsweringPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A dictionary or a list of dictionaries containing results</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result is a dictionary with the following
keys:</p>
<ul>
<li><strong>answer</strong> (<code>str</code>) — The answer of the query given the table. If there is an aggregator, the answer will
be preceded by <code>AGGREGATOR ></code>.</li>
<li><strong>coordinates</strong> (<code>List[Tuple[int, int]]</code>) — Coordinates of the cells of the answers.</li>
<li><strong>cells</strong> (<code>List[str]</code>) — List of strings made up of the answer cell values.</li>
<li><strong>aggregator</strong> (<code>str</code>) — If the model has an aggregator, this returns the aggregator.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:</p>
<ul><li><code>pipeline(table, query)</code></li>
<li><code>pipeline(table, [query])</code></li>
<li><code>pipeline(table=table, query=query)</code></li>
<li><code>pipeline(table=table, query=[query])</code></li>
<li><code>pipeline({"table": table, "query": query})</code></li>
<li><code>pipeline({"table": table, "query": [query]})</code></li>
<li><code>pipeline([{"table": table, "query": query}, {"table": table, "query": query}])</code></li></ul>
<p>The <code>table</code> argument should be a dict or a DataFrame built from that dict, containing the whole table:</p>
<p>Example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->data = {
<span class="hljs-string">"actors"</span>: [<span class="hljs-string">"brad pitt"</span>, <span class="hljs-string">"leonardo di caprio"</span>, <span class="hljs-string">"george clooney"</span>],
<span class="hljs-string">"age"</span>: [<span class="hljs-string">"56"</span>, <span class="hljs-string">"45"</span>, <span class="hljs-string">"59"</span>],
<span class="hljs-string">"number of movies"</span>: [<span class="hljs-string">"87"</span>, <span class="hljs-string">"53"</span>, <span class="hljs-string">"69"</span>],
<span class="hljs-string">"date of birth"</span>: [<span class="hljs-string">"7 february 1967"</span>, <span class="hljs-string">"10 june 1996"</span>, <span class="hljs-string">"28 november 1967"</span>],
}<!-- HTML_TAG_END --></pre></div>
<p>This dictionary can be passed in as such, or can be converted to a pandas DataFrame:</p>
<p>Example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
table = pd.DataFrame.from_dict(data)<!-- HTML_TAG_END --></pre></div></div></div>
<h3 class="relative group"><a id="transformers.TextClassificationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TextClassificationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TextClassificationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TextClassificationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TextClassificationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TextClassificationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_classification.py#L47" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.return_all_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.return_all_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_all_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to return all prediction scores or just the one of the predicted class.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.function_to_apply" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.function_to_apply"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>function_to_apply</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"default"</code>) —
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:</p>
<ul>
<li><code>"default"</code>: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.</li>
<li><code>"sigmoid"</code>: Applies the sigmoid function on the output.</li>
<li><code>"softmax"</code>: Applies the softmax function on the output.</li>
<li><code>"none"</code>: Does not apply any function on the output.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Text classification pipeline using any <code>ModelForSequenceClassification</code>. See the <a href="../task_summary#sequence-classification">sequence classification
examples</a> for more information.</p>
<p>This text classification pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"sentiment-analysis"</code> (for classifying sequences according to positive or negative sentiments).</p>
<p>If multiple classification labels are available (<code>model.config.num_labels >= 2</code>), the pipeline will run a softmax
over the results. If there is a single label, the pipeline will run a sigmoid over the result.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a sequence classification task. See
the up-to-date list of available models on
<a href="https://huggingface.co/models?filter=text-classification" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TextClassificationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TextClassificationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TextClassificationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_classification.py#L92" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list or a list of list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.__call__.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.__call__.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
One or several texts (or one list of prompts) to classify.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.__call__.return_all_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.__call__.return_all_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_all_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to return scores for all labels.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextClassificationPipeline.__call__.function_to_apply" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextClassificationPipeline.__call__.function_to_apply"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>function_to_apply</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"default"</code>) —
The function to apply to the model outputs in order to retrieve the scores. Accepts four different
values:</p>
<p>If this argument is not specified, then it will apply the following functions according to the number
of labels:</p>
<ul>
<li>If the model has a single label, will apply the sigmoid function on the output.</li>
<li>If the model has several labels, will apply the softmax function on the output.</li>
</ul>
<p>Possible values are:</p>
<ul>
<li><code>"sigmoid"</code>: Applies the sigmoid function on the output.</li>
<li><code>"softmax"</code>: Applies the softmax function on the output.</li>
<li><code>"none"</code>: Does not apply any function on the output.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TextClassificationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list or a list of list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as list of dictionaries with the following keys:</p>
<ul>
<li><strong>label</strong> (<code>str</code>) — The label predicted.</li>
<li><strong>score</strong> (<code>float</code>) — The corresponding probability.</li>
</ul>
<p>If <code>self.return_all_scores=True</code>, one such dictionary is returned per label.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Classify the text(s) given as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.TextGenerationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TextGenerationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TextGenerationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TextGenerationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TextGenerationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TextGenerationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_generation.py#L20" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Language generation pipeline using any <code>ModelWithLMHead</code>. This pipeline predicts the words that will follow a
specified text prompt.</p>
<p>This language generation pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"text-generation"</code>.</p>
<p>The models that this pipeline can use are models that have been trained with an autoregressive language modeling
objective, which includes the uni-directional models in the library (e.g. gpt2). See the list of available models
on <a href="https://huggingface.co/models?filter=text-generation" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TextGenerationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TextGenerationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TextGenerationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text_generation.py#L136" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_inputs<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list or a list of list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.__call__.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.__call__.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
One or several prompts (or one list of prompts) to complete.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.__call__.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.__call__.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.__call__.return_text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.__call__.return_text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.__call__.return_full_text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.__call__.return_full_text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_full_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If set to <code>False</code> only added text is returned, otherwise the full text is returned Only meaningful if
<em>return_text</em> is set to True.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.__call__.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.__call__.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.__call__.prefix" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.__call__.prefix"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>prefix</strong> (<code>str</code>, <em>optional</em>) —
Prefix added to prompt.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TextGenerationPipeline.__call__.handle_long_generation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TextGenerationPipeline.__call__.handle_long_generation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>handle_long_generation</strong> (<code>str</code>, <em>optional</em>) —
By default, this pipelines does not handle long generation (ones that exceed in one form or the other
the model maximum length). There is no perfect way to adress this (more info
:<a href="https://github.com/huggingface/transformers/issues/14033#issuecomment-948385227" rel="nofollow">https://github.com/huggingface/transformers/issues/14033#issuecomment-948385227</a>). This provides common
strategies to work around that problem depending on your use case.</p>
<ul>
<li><code>None</code> : default strategy where nothing in particular happens</li>
<li><code>"hole"</code>: Truncates left of input, and leaves a gap wide enough to let generation happen (might
truncate a lot of the prompt and not suitable when generation exceed the model capacity)</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TextGenerationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list or a list of list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>generated_text</strong> (<code>str</code>, present when <code>return_text=True</code>) — The generated text.</li>
<li><strong>generated_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) — The token
ids of the generated text.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Complete the prompt(s) given as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.Text2TextGenerationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Text2TextGenerationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Text2TextGenerationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Text2TextGenerationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Text2TextGenerationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Text2TextGenerationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L26" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Pipeline for text to text generation using seq2seq models.</p>
<p>This Text2TextGenerationPipeline pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task
identifier: <code>"text2text-generation"</code>.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on
<a href="https://huggingface.co/models?filter=text2text-generation" rel="nofollow">huggingface.co/models</a>.</p>
<p>Usage:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->text2text_generator = pipeline(<span class="hljs-string">"text2text-generation"</span>)
text2text_generator(<span class="hljs-string">"question: What is 42 ? context: 42 is the answer to life, the universe and everything"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Text2TextGenerationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Text2TextGenerationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Text2TextGenerationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L109" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list or a list of list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.__call__.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.__call__.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
Input text for the encoder.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.__call__.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.__call__.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.__call__.return_text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.__call__.return_text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.__call__.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.__call__.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Text2TextGenerationPipeline.__call__.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Text2TextGenerationPipeline.__call__.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>TruncationStrategy</code>, <em>optional</em>, defaults to <code>TruncationStrategy.DO_NOT_TRUNCATE</code>) —
The truncation strategy for the tokenization within the pipeline. <code>TruncationStrategy.DO_NOT_TRUNCATE</code>
(default) will never truncate, but it is sometimes desirable to truncate the input to fit the model’s
max_length instead of throwing an error down the line.
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Text2TextGenerationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list or a list of list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>generated_text</strong> (<code>str</code>, present when <code>return_text=True</code>) — The generated text.</li>
<li><strong>generated_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) — The token
ids of the generated text.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Generate the output text(s) using text(s) given as inputs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Text2TextGenerationPipeline.check_inputs"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>check_inputs</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Text2TextGenerationPipeline.check_inputs" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Text2TextGenerationPipeline.check_inputs"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L82" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Checks whether there might be something wrong with given input with regard to the model.</p></div></div>
<h3 class="relative group"><a id="transformers.TokenClassificationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TokenClassificationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TokenClassificationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L86" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_parser<span class="opacity-60"> = <transformers.pipelines.token_classification.TokenClassificationArgumentHandler object at 0x7f45b52f37c0></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.ignore_labels" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.ignore_labels"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_labels</strong> (<code>List[str]</code>, defaults to <code>["O"]</code>) —
A list of labels to ignore.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.grouped_entities" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.grouped_entities"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>grouped_entities</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
DEPRECATED, use <code>aggregation_strategy</code> instead. Whether or not to group the tokens corresponding to the
same entity together in the predictions or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.aggregation_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.aggregation_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>aggregation_strategy</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"none"</code>) —
The strategy to fuse (or not) tokens based on the model prediction.</p>
<ul>
<li>“none” : Will simply not do any aggregation and simply return raw results from the model</li>
<li>“simple” : Will attempt to group entities following the default schema. (A, B-TAG), (B, I-TAG), (C,
I-TAG), (D, B-TAG2) (E, B-TAG2) will end up being [{“word”: ABC, “entity”: “TAG”}, {“word”: “D”,
“entity”: “TAG2”}, {“word”: “E”, “entity”: “TAG2”}] Notice that two consecutive B tags will end up as
different entities. On word based languages, we might end up splitting words undesirably : Imagine
Microsoft being tagged as [{“word”: “Micro”, “entity”: “ENTERPRISE”}, {“word”: “soft”, “entity”:
“NAME”}]. Look for FIRST, MAX, AVERAGE for ways to mitigate that and disambiguate words (on languages
that support that meaning, which is basically tokens separated by a space). These mitigations will
only work on real words, “New york” might still be tagged with two different entities.</li>
<li>“first” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Words will simply use the tag of the first token of the word when there
is ambiguity.</li>
<li>“average” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words,
cannot end up with different tags. scores will be averaged first across tokens, and then the maximum
label is applied.</li>
<li>“max” : (works only on word based models) Will use the <code>SIMPLE</code> strategy except that words, cannot
end up with different tags. Word entity will simply be the token with the maximum score.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Named Entity Recognition pipeline using any <code>ModelForTokenClassification</code>. See the <a href="../task_summary#named-entity-recognition">named entity recognition
examples</a> for more information.</p>
<p>This token recognition pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"ner"</code> (for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous).</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a token classification task. See the
up-to-date list of available models on
<a href="https://huggingface.co/models?filter=token-classification" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L160" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Union[str, typing.List[str]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list or a list of list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.__call__.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.__call__.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>str</code> or <code>List[str]</code>) —
One or several texts (or one list of texts) for token classification.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TokenClassificationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list or a list of list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as a list of dictionaries (one for each token in the
corresponding input, or each entity if this pipeline was instantiated with an aggregation_strategy) with
the following keys:</p>
<ul>
<li><strong>word</strong> (<code>str</code>) — The token/word classified.</li>
<li><strong>score</strong> (<code>float</code>) — The corresponding probability for <code>entity</code>.</li>
<li><strong>entity</strong> (<code>str</code>) — The entity predicted for that token/word (it is named <em>entity_group</em> when
<em>aggregation_strategy</em> is not <code>"none"</code>.</li>
<li><strong>index</strong> (<code>int</code>, only present when <code>aggregation_strategy="none"</code>) — The index of the corresponding
token in the sentence.</li>
<li><strong>start</strong> (<code>int</code>, <em>optional</em>) — The index of the start of the corresponding entity in the sentence. Only
exists if the offsets are available within the tokenizer</li>
<li><strong>end</strong> (<code>int</code>, <em>optional</em>) — The index of the end of the corresponding entity in the sentence. Only
exists if the offsets are available within the tokenizer</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Classify each token of the text(s) given as inputs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.aggregate_words"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>aggregate_words</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.aggregate_words" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.aggregate_words"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L366" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">entities<span class="opacity-60">: typing.List[dict]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">aggregation_strategy<span class="opacity-60">: AggregationStrategy</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Override tokens from a given word that disagree to force agreement on word boundaries.</p>
<p>Example: micro|soft| com|pany| B-ENT I-NAME I-ENT I-ENT will be rewritten with first strategy as microsoft|
company| B-ENT I-ENT</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.gather_pre_entities"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>gather_pre_entities</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.gather_pre_entities" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.gather_pre_entities"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L252" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sentence<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">offset_mapping<span class="opacity-60">: typing.Union[typing.List[typing.Tuple[int, int]], NoneType]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens_mask<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">aggregation_strategy<span class="opacity-60">: AggregationStrategy</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Fuse various numpy arrays into dicts with all the information needed for aggregation</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.group_entities"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>group_entities</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.group_entities" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.group_entities"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L428" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">entities<span class="opacity-60">: typing.List[dict]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.group_entities.entities" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.group_entities.entities"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Find and group together the adjacent tokens with the same entity predicted.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenClassificationPipeline.group_sub_entities"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>group_sub_entities</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TokenClassificationPipeline.group_sub_entities" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenClassificationPipeline.group_sub_entities"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/token_classification.py#L393" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">entities<span class="opacity-60">: typing.List[dict]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenClassificationPipeline.group_sub_entities.entities" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenClassificationPipeline.group_sub_entities.entities"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>entities</strong> (<code>dict</code>) — The entities predicted by the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Group together the adjacent tokens with the same entity predicted.</p></div></div>
<h3 class="relative group"><a id="transformers.TranslationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TranslationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TranslationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TranslationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TranslationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TranslationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L253" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Translates from one language to another.</p>
<p>This translation pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"translation_xx_to_yy"</code>.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on a translation task. See the
up-to-date list of available models on <a href="https://huggingface.co/models?filter=translation" rel="nofollow">huggingface.co/models</a>.</p>
<p>Usage:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->en_fr_translator = pipeline(<span class="hljs-string">"translation_en_to_fr"</span>)
en_fr_translator(<span class="hljs-string">"How old are you?"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TranslationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TranslationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TranslationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/text2text_generation.py#L305" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list or a list of list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.__call__.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.__call__.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>str</code> or <code>List[str]</code>) —
Texts to be translated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.__call__.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.__call__.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to include the tensors of predictions (as token indices) in the outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.__call__.return_text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.__call__.return_text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to include the decoded texts in the outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.__call__.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.__call__.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clean up the potential extra spaces in the text output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.__call__.src_lang" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.__call__.src_lang"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>src_lang</strong> (<code>str</code>, <em>optional</em>) —
The language of the input. Might be required for multilingual models. Will not have any effect for
single pair translation models<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TranslationPipeline.__call__.tgt_lang" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TranslationPipeline.__call__.tgt_lang"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tgt_lang</strong> (<code>str</code>, <em>optional</em>) —
The language of the desired output. Might be required for multilingual models. Will not have any effect
for single pair translation models
generate_kwargs —
Additional keyword arguments to pass along to the generate method of the model (see the generate method
corresponding to your framework <a href="./model#generative-models">here</a>).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TranslationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list or a list of list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>translation_text</strong> (<code>str</code>, present when <code>return_text=True</code>) — The translation.</li>
<li><strong>translation_token_ids</strong> (<code>torch.Tensor</code> or <code>tf.Tensor</code>, present when <code>return_tensors=True</code>) — The
token ids of the translation.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Translate the text(s) given as inputs.</p></div></div>
<h3 class="relative group"><a id="transformers.ZeroShotClassificationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeroShotClassificationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ZeroShotClassificationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ZeroShotClassificationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ZeroShotClassificationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ZeroShotClassificationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_classification.py#L47" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_parser<span class="opacity-60"> = <transformers.pipelines.zero_shot_classification.ZeroShotClassificationArgumentHandler object at 0x7f45b52fe1c0></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>NLI-based zero-shot classification pipeline using a <code>ModelForSequenceClassification</code> trained on NLI (natural
language inference) tasks.</p>
<p>Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis
pair and passed to the pretrained model. Then, the logit for <em>entailment</em> is taken as the logit for the candidate
label being valid. Any NLI model can be used, but the id of the <em>entailment</em> label must be included in the model
config’s :attr:<em>~transformers.PretrainedConfig.label2id</em>.</p>
<p>This NLI pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"zero-shot-classification"</code>.</p>
<p>The models that this pipeline can use are models that have been fine-tuned on an NLI task. See the up-to-date list
of available models on <a href="https://huggingface.co/models?search=nli" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ZeroShotClassificationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ZeroShotClassificationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ZeroShotClassificationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_classification.py#L139" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: typing.Union[str, typing.List[str]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A <code>dict</code> or a list of <code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.__call__.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.__call__.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>str</code> or <code>List[str]</code>) —
The sequence(s) to classify, will be truncated if the model input is too large.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.__call__.candidate_labels" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.__call__.candidate_labels"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>candidate_labels</strong> (<code>str</code> or <code>List[str]</code>) —
The set of possible class labels to classify each sequence into. Can be a single label, a string of
comma-separated labels, or a list of labels.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.__call__.hypothesis_template" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.__call__.hypothesis_template"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hypothesis_template</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"This example is {}."</code>) —
The template used to turn each label into an NLI-style hypothesis. This template must include a {} or
similar syntax for the candidate label to be inserted into the template. For example, the default
template is <code>"This example is {}."</code> With the candidate label <code>"sports"</code>, this would be fed into the
model like <code>"<cls> sequence to classify <sep> This example is sports . <sep>"</code>. The default template
works well in many cases, but it may be worthwhile to experiment with different templates depending on
the task setting.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotClassificationPipeline.__call__.multi_label" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotClassificationPipeline.__call__.multi_label"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>multi_label</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not multiple candidate labels can be true. If <code>False</code>, the scores are normalized such that
the sum of the label likelihoods for each sequence is 1. If <code>True</code>, the labels are considered
independent and probabilities are normalized for each candidate by doing a softmax of the entailment
score vs. the contradiction score.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.ZeroShotClassificationPipeline.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A <code>dict</code> or a list of <code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Each result comes as a dictionary with the following keys:</p>
<ul>
<li><strong>sequence</strong> (<code>str</code>) — The sequence for which this is the output.</li>
<li><strong>labels</strong> (<code>List[str]</code>) — The labels sorted by order of likelihood.</li>
<li><strong>scores</strong> (<code>List[float]</code>) — The probabilities for each of the labels.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Classify the sequence(s) given as inputs. See the <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.ZeroShotClassificationPipeline">ZeroShotClassificationPipeline</a> documentation for more
information.</p></div></div>
<h3 class="relative group"><a id="transformers.ZeroShotImageClassificationPipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeroShotImageClassificationPipeline
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ZeroShotImageClassificationPipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ZeroShotImageClassificationPipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ZeroShotImageClassificationPipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ZeroShotImageClassificationPipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_image_classification.py#L29" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Zero shot image classification pipeline using <code>CLIPModel</code>. This pipeline predicts the class of an image when you
provide an image and a set of <code>candidate_labels</code>.</p>
<p>This image classification pipeline can currently be loaded from <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.pipeline">pipeline()</a> using the following task identifier:
<code>"zero-shot-image-classification"</code>.</p>
<p>See the list of available models on
<a href="https://huggingface.co/models?filter=zero-shot-image-classification" rel="nofollow">huggingface.co/models</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ZeroShotImageClassificationPipeline.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ZeroShotImageClassificationPipeline.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ZeroShotImageClassificationPipeline.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_image_classification.py#L48" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">images<span class="opacity-60">: typing.Union[str, typing.List[str], ForwardRef('Image'), typing.List[ForwardRef('Image')]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.__call__.images" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.__call__.images"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>images</strong> (<code>str</code>, <code>List[str]</code>, <code>PIL.Image</code> or <code>List[PIL.Image]</code>) —
The pipeline handles three types of images:</p>
<ul>
<li>A string containing a http link pointing to an image</li>
<li>A string containing a local path to an image</li>
<li>An image loaded in PIL directly</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.__call__.candidate_labels" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.__call__.candidate_labels"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>candidate_labels</strong> (<code>List[str]</code>) —
The candidate labels for this image<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ZeroShotImageClassificationPipeline.__call__.hypothesis_template" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ZeroShotImageClassificationPipeline.__call__.hypothesis_template"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hypothesis_template</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"This is a photo of {}"</code>) —
The sentence used in cunjunction with <em>candidate_labels</em> to attempt the image classification by
replacing the placeholder with the candidate_labels. Then likelihood is estimated by using
logits_per_image<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Assign labels to the image(s) passed as inputs.</p></div></div>
<h2 class="relative group"><a id="transformers.Pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Parent class: <code>Pipeline</code></span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Pipeline</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Pipeline" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L711" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature_extractor<span class="opacity-60">: typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">modelcard<span class="opacity-60">: typing.Optional[transformers.modelcard.ModelCard] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_parser<span class="opacity-60">: ArgumentHandler = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">binary_output<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>) —
The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> for PyTorch and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> for TensorFlow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>) —
The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.modelcard" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.modelcard"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>modelcard</strong> (<code>str</code> or <code>ModelCard</code> <em>optional</em>) —
Model card attributed to the model for this pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>) —
The framework to use, either <code>"pt"</code> for PyTorch or <code>"tf"</code> for TensorFlow. The specified framework must be
installed.</p>
<p>If no framework is specified, will default to the one currently installed. If no framework is specified and
both frameworks are installed, will default to the framework of the <code>model</code>, or to PyTorch if no model is
provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>, defaults to <code>""</code>) —
A task-identifier for the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the number of
workers to be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
When the pipeline will use <em>DataLoader</em> (when passing a dataset, on GPU for a Pytorch model), the size of
the batch to use, for inference this is not always beneficial, please read <a href="https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching" rel="nofollow">Batching with
pipelines</a> .<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.args_parser" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.args_parser"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args_parser</strong> (<a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.pipelines.ArgumentHandler">ArgumentHandler</a>, <em>optional</em>) —
Reference to the object in charge of parsing supplied pipeline parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
the associated CUDA device id.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.binary_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.binary_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>binary_output</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across
different pipelines.</p>
<p>Base class implementing pipelined operations. Pipeline workflow is defined as a sequence of the following
operations:</p>
<p>Input -> Tokenization -> Model Inference -> Post-Processing (task dependent) -> Output</p>
<p>Pipeline supports running on CPU or GPU through the device argument (see below).</p>
<p>Some pipeline, like for instance <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.FeatureExtractionPipeline">FeatureExtractionPipeline</a> (<code>'feature-extraction'</code>) output large tensor object
as nested-lists. In order to avoid dumping such large structure as textual data we provide the <code>binary_output</code>
constructor argument. If set to <code>True</code>, the output will be stored in the pickle format.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.check_model_type"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>check_model_type</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.check_model_type" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.check_model_type"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L864" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">supported_models<span class="opacity-60">: typing.Union[typing.List[str], dict]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.check_model_type.supported_models" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.check_model_type.supported_models"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>supported_models</strong> (<code>List[str]</code> or <code>dict</code>) —
The list of models supported by the pipeline, or a dictionary with model class values.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Check if the model class is in supported by the pipeline.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.device_placement"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>device_placement</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.device_placement" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.device_placement"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L806" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Context Manager allowing tensor allocation on the user-specified device in framework agnostic way.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># Explicitly ask for tensor allocation on CUDA device :0</span>
pipe = pipeline(..., device=<span class="hljs-number">0</span>)
<span class="hljs-keyword">with</span> pipe.device_placement():
<span class="hljs-comment"># Every framework specific tensor allocation will be done on the request device</span>
output = pipe(...)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.ensure_tensor_on_device"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>ensure_tensor_on_device</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.ensure_tensor_on_device" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.ensure_tensor_on_device"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L832" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**inputs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Dict[str, torch.Tensor]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.ensure_tensor_on_device.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.ensure_tensor_on_device.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (keyword arguments that should be <code>torch.Tensor</code>, the rest is ignored) —
The tensors to place on <code>self.device</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.ensure_tensor_on_device.Recursive" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.ensure_tensor_on_device.Recursive"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>Recursive</strong> on lists <strong>only</strong>. —<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Pipeline.ensure_tensor_on_device.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Dict[str, torch.Tensor]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The same as <code>inputs</code> but on the proper device.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Ensure PyTorch tensors are on the specified device.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.postprocess"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>postprocess</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.postprocess" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.postprocess"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L920" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model_outputs<span class="opacity-60">: ModelOutput</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**postprocess_parameters<span class="opacity-60">: typing.Dict</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Postprocess will receive the raw outputs of the <code>_forward</code> method, generally tensors, and reformat them into
something more friendly. Generally it will output a list or a dict or results (containing just strings and
numbers).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.predict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>predict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.predict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.predict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L800" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">X<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Scikit / Keras interface to transformers’ pipelines. This method will forward to <strong>call</strong>().</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.preprocess"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>preprocess</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.preprocess" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.preprocess"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L899" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_<span class="opacity-60">: typing.Any</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**preprocess_parameters<span class="opacity-60">: typing.Dict</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Preprocess will take the <code>input_</code> of a specific pipeline and return a dictionnary of everything necessary for
<code>_forward</code> to run properly. It should contain at least one tensor, but might have arbitrary other items.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L770" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Pipeline.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Pipeline.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code>) —
A path to the directory where to saved. It will be created if it doesn’t exist.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save the pipeline’s model and tokenizer.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Pipeline.transform"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transform</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Pipeline.transform" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Pipeline.transform"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L794" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">X<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Scikit / Keras interface to transformers’ pipelines. This method will forward to <strong>call</strong>().</p></div></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en | hf_public_repos/doc-build-dev/transformers/pr_16143/en/main_classes/model.html | <meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{"local":"models","sections":[{"local":"transformers.PreTrainedModel","sections":[{"local":"model-instantiation-dtype","title":"Model Instantiation dtype"}],"title":"PreTrainedModel"},{"local":"transformers.modeling_utils.ModuleUtilsMixin","title":"ModuleUtilsMixin"},{"local":"transformers.TFPreTrainedModel","title":"TFPreTrainedModel"},{"local":"transformers.modeling_tf_utils.TFModelUtilsMixin","title":"TFModelUtilsMixin"},{"local":"transformers.FlaxPreTrainedModel","title":"FlaxPreTrainedModel"},{"local":"transformers.file_utils.PushToHubMixin","title":"Pushing to the Hub"}],"title":"Models"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="models" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#models"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Models
</span></h1>
<p>The base classes <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>, <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>, and
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a> implement the common methods for loading/saving a model either from a local
file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace’s AWS
S3 repository).</p>
<p><a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> and <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> also implement a few methods which
are common among all the models to:</p>
<ul><li>resize the input token embeddings when new tokens are added to the vocabulary</li>
<li>prune the attention heads of the model.</li></ul>
<p>The other methods that are common to each model are defined in <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.modeling_utils.ModuleUtilsMixin">ModuleUtilsMixin</a>
(for the PyTorch models) and <code>TFModuleUtilsMixin</code> (for the TensorFlow models) or
for text generation, <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin">GenerationMixin</a> (for the PyTorch models),
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_tf_utils.TFGenerationMixin">TFGenerationMixin</a> (for the TensorFlow models) and
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_flax_utils.FlaxGenerationMixin">FlaxGenerationMixin</a> (for the Flax/JAX models).</p>
<h2 class="relative group"><a id="transformers.PreTrainedModel" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>PreTrainedModel
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PreTrainedModel</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L423" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*inputs<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Base class for all models.</p>
<p><a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models as well as a few methods common to all models to:</p>
<ul><li>resize the input embeddings,</li>
<li>prune heads in the self-attention heads.</li></ul>
<p>Class attributes (overridden by derived classes):</p>
<ul><li><p><strong>config_class</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) — A subclass of <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> to use as configuration class
for this model architecture.</p></li>
<li><p><strong>load_tf_weights</strong> (<code>Callable</code>) — A python <em>method</em> for loading a TensorFlow checkpoint in a PyTorch model,
taking as arguments:</p>
<ul><li><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>) — An instance of the model on which to load the TensorFlow checkpoint.</li>
<li><strong>config</strong> (<code>PreTrainedConfig</code>) — An instance of the configuration associated to the model.</li>
<li><strong>path</strong> (<code>str</code>) — A path to the TensorFlow checkpoint.</li></ul></li>
<li><p><strong>base_model_prefix</strong> (<code>str</code>) — A string indicating the attribute associated to the base model in derived
classes of the same architecture adding modules on top of the base model.</p></li>
<li><p><strong>is_parallelizable</strong> (<code>bool</code>) — A flag indicating whether this model supports model parallelization.</p></li>
<li><p><strong>main_input_name</strong> (<code>str</code>) — The name of the principal input to the model (often <code>input_ids</code> for NLP
models, <code>pixel_values</code> for vision models and <code>input_values</code> for speech models).</p></li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your model in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add model"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your model (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your model in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the model checkpoint to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
model = AutoModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" with no local clone.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.from_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.from_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.from_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1091" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60">: typing.Union[str, os.PathLike, NoneType]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*model_args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.from_pretrained.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.from_pretrained.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
<li>A path or url to a model folder containing a <em>flax checkpoint file</em> in <em>.msgpack</em> format (e.g,
<code>./flax_model/</code> containing <code>flax_model.msgpack</code>). In this case, <code>from_flax</code> should be set to
<code>True</code>.</li>
<li><code>None</code> if you are both providing the configuration and state dictionary (resp. with keyword
arguments <code>config</code> and <code>state_dict</code>).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.from_pretrained.model_args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.from_pretrained.model_args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_args</strong> (sequence of positional arguments, <em>optional</em>) —
All remaining positional arguments will be passed to the underlying model’s <code>__init__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.from_pretrained.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.from_pretrained.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<code>Union[PretrainedConfig, str, os.PathLike]</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>an instance of a class derived from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>,</li>
<li>a string or path valid as input to <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Instantiate a pretrained pytorch model from a pre-trained model configuration.</p>
<p>The model is set in evaluation mode by default using <code>model.eval()</code> (Dropout modules are deactivated). To train
the model, you should first set it back in training mode with <code>model.train()</code>.</p>
<p>The warning <em>Weights from XXX not initialized from pretrained model</em> means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.</p>
<p>The warning <em>Weights from XXX not used in YYY</em> means that the layer XXX is not used by YYY, therefore those
weights are discarded.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Passing `use_auth_token=True“ is required when you want to use a private model.</p></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Activate the special <a href="https://huggingface.co/transformers/installation.html#offline-mode" rel="nofollow">“offline-mode”</a> to
use this method in a firewalled environment.</p></div>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, BertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Update configuration during loading.</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> model.config.output_attentions == <span class="hljs-literal">True</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>config = BertConfig.from_json_file(<span class="hljs-string">"./tf_model/my_tf_model_config.json"</span>)
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"./tf_model/my_tf_checkpoint.ckpt.index"</span>, from_tf=<span class="hljs-literal">True</span>, config=config)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a Flax checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">>>> </span>model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, from_flax=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.get_input_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_input_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.get_input_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.get_input_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L575" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>nn.Module</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedModel.get_input_embeddings.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>nn.Module</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A torch module mapping vocabulary to hidden states.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the model’s input embeddings.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.get_output_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_output_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.get_output_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.get_output_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L601" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>nn.Module</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedModel.get_output_embeddings.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>nn.Module</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A torch module mapping hidden states to vocabulary.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the model’s output embeddings.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.gradient_checkpointing_disable"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>gradient_checkpointing_disable</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.gradient_checkpointing_disable" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.gradient_checkpointing_disable"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L980" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Deactivates gradient checkpointing for the current model.</p>
<p>Note that in other frameworks this feature can be referred to as “activation checkpointing” or “checkpoint
activations”.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.gradient_checkpointing_enable"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>gradient_checkpointing_enable</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.gradient_checkpointing_enable" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.gradient_checkpointing_enable"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L969" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Activates gradient checkpointing for the current model.</p>
<p>Note that in other frameworks this feature can be referred to as “activation checkpointing” or “checkpoint
activations”.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.init_weights"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>init_weights</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.init_weights" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.init_weights"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L936" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>If needed prunes and maybe initializes weights.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.post_init"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>post_init</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.post_init" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.post_init"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L494" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A method executed at the end of each Transformer model initialization, to execute code that needs the model’s
modules properly initialized (such as weight initialization).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.prune_heads"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>prune_heads</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.prune_heads" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.prune_heads"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L952" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">heads_to_prune<span class="opacity-60">: typing.Dict[int, typing.List[int]]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.prune_heads.heads_to_prune" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.prune_heads.heads_to_prune"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>heads_to_prune</strong> (<code>Dict[int, List[int]]</code>) —
Dictionary with keys being selected layer indices (<code>int</code>) and associated values being the list of heads
to prune in said layer (list of <code>int</code>). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on
layer 1 and heads 2 and 3 on layer 2.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Prunes heads of the base model.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.register_for_auto_class"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>register_for_auto_class</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.register_for_auto_class" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.register_for_auto_class"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1817" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">auto_class<span class="opacity-60"> = 'AutoModel'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.register_for_auto_class.auto_class" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.register_for_auto_class.auto_class"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"AutoModel"</code>) —
The auto class to register this new model with.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This API is experimental and may have some slight breaking changes in the next releases.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.resize_token_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>resize_token_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.resize_token_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.resize_token_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L729" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">new_num_tokens<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.nn.Embedding</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.resize_token_embeddings.new_num_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.resize_token_embeddings.new_num_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>new_num_tokens</strong> (<code>int</code>, <em>optional</em>) —
The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
vectors at the end. Reducing the size will remove vectors from the end. If not provided or <code>None</code>, just
returns a pointer to the input tokens <code>torch.nn.Embedding</code> module of the model without doing anything.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedModel.resize_token_embeddings.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.nn.Embedding</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Pointer to the input tokens Embeddings Module of the model.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Resizes input token embeddings matrix of the model if <code>new_num_tokens != config.vocab_size</code>.</p>
<p>Takes care of tying weights embeddings afterwards if the model class has a <code>tie_weights()</code> method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1000" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_config<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">state_dict<span class="opacity-60">: typing.Optional[dict] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_function<span class="opacity-60">: typing.Callable = <function save at 0x7f461a422820></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory to which to save. Will be created if it doesn’t exist.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.save_pretrained.save_config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.save_pretrained.save_config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_config</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to save the config of the model. Useful when in distributed training like TPUs and need
to call this function on all processes. In this case, set <code>save_config=True</code> only on the main process
to avoid race conditions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.save_pretrained.state_dict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.save_pretrained.state_dict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>state_dict</strong> (nested dictionary of <code>torch.Tensor</code>) —
The state dictionary of the model to save. Will default to <code>self.state_dict()</code>, but can be used to only
save parts of the model or if special precautions need to be taken when recovering the state dictionary
of a model (like when using model parallelism).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.save_pretrained.save_function" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.save_pretrained.save_function"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_function</strong> (<code>Callable</code>) —
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
need to replace <code>torch.save</code> by another method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.save_pretrained.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.save_pretrained.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save a model and its configuration file to a directory, so that it can be re-loaded using the
<code>[from_pretrained()](/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel.from_pretrained)</code> class method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.set_input_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>set_input_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.set_input_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.set_input_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L588" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60">: Module</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedModel.set_input_embeddings.value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedModel.set_input_embeddings.value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>value</strong> (<code>nn.Module</code>) — A module mapping vocabulary to hidden states.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Set model’s input embeddings.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedModel.tie_weights"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tie_weights</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedModel.tie_weights" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedModel.tie_weights"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L616" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Tie the weights between the input embeddings and the output embeddings.</p>
<p>If the <code>torchscript</code> flag is set in the configuration, can’t handle parameter sharing so we are cloning the
weights instead.</p></div></div>
<a id="from_pretrained-torch-dtype"></a>
<h3 class="relative group"><a id="model-instantiation-dtype" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#model-instantiation-dtype"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Model Instantiation dtype
</span></h3>
<p>Under Pytorch a model normally gets instantiated with <code>torch.float32</code> format. This can be an issue if one tries to
load a model whose weights are in fp16, since it’d require twice as much memory. To overcome this limitation, you can
either explicitly pass the desired <code>dtype</code> using <code>torch_dtype</code> argument:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->model = T5ForConditionalGeneration.from_pretrained(<span class="hljs-string">"t5"</span>, torch_dtype=torch.float16)<!-- HTML_TAG_END --></pre></div>
<p>or, if you want the model to always load in the most optimal memory pattern, you can use the special value <code>"auto"</code>,
and then <code>dtype</code> will be automatically derived from the model’s weights:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->model = T5ForConditionalGeneration.from_pretrained(<span class="hljs-string">"t5"</span>, torch_dtype=<span class="hljs-string">"auto"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Models instantiated from scratch can also be told which <code>dtype</code> to use with:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->config = T5Config.from_pretrained(<span class="hljs-string">"t5"</span>)
model = AutoModel.from_config(config)<!-- HTML_TAG_END --></pre></div>
<p>Due to Pytorch design, this functionality is only available for floating dtypes.</p>
<h2 class="relative group"><a id="transformers.modeling_utils.ModuleUtilsMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ModuleUtilsMixin
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_utils.</span><span class="font-semibold">ModuleUtilsMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L151" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A few utilities for <code>torch.nn.Modules</code>, to be used as a mixin.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.add_memory_hooks"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>add_memory_hooks</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.add_memory_hooks" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.add_memory_hooks"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L182" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.</p>
<p>Increase in memory consumption is stored in a <code>mem_rss_diff</code> attribute for each module and can be reset to zero
with <code>model.reset_memory_hooks_state()</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>estimate_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L378" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_dict<span class="opacity-60">: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>dict</code>) — The model inputs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.ModuleUtilsMixin.estimate_tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The total number of tokens.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Helper function to estimate the total number of tokens from the model inputs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>floating_point_ops</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L396" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_dict<span class="opacity-60">: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">exclude_embeddings<span class="opacity-60">: bool = True</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>) —
The batch size for the forward pass.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.sequence_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.sequence_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequence_length</strong> (<code>int</code>) —
The number of tokens in each line of the batch.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.exclude_embeddings" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.exclude_embeddings"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>exclude_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to count embedding and softmax operations.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.ModuleUtilsMixin.floating_point_ops.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The number of floating-point operations.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a
batch with this transformer model. Default approximation neglects the quadratic dependency on the number of
tokens (valid if <code>12 * d_model << sequence_length</code>) as laid out in <a href="https://arxiv.org/pdf/2001.08361.pdf" rel="nofollow">this
paper</a> section 2.1. Should be overridden for transformers with parameter
re-use e.g. Albert or Universal Transformers, or if doing long-range modeling with very high sequence lengths.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_extended_attention_mask</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L271" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attention_mask<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_shape<span class="opacity-60">: typing.Tuple[int]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: <property object at 0x7f45b4efb950></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask.attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask.attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attention_mask</strong> (<code>torch.Tensor</code>) —
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask.input_shape" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.get_extended_attention_mask.input_shape"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_shape</strong> (<code>Tuple[int]</code>) —
The shape of the input to the model.
device — (<code>torch.device</code>):
The device of the input to the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Makes broadcastable attention and causal masks so that future and masked tokens are ignored.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.get_head_mask"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_head_mask</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.get_head_mask" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.get_head_mask"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L314" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">head_mask<span class="opacity-60">: typing.Optional[torch.Tensor]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_hidden_layers<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_attention_chunked<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.get_head_mask.head_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.get_head_mask.head_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>head_mask</strong> (<code>torch.Tensor</code> with shape <code>[num_heads]</code> or <code>[num_hidden_layers x num_heads]</code>, <em>optional</em>) —
The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.get_head_mask.num_hidden_layers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.get_head_mask.num_hidden_layers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_hidden_layers</strong> (<code>int</code>) —
The number of hidden layers in the model.
is_attention_chunked — (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether or not the attentions scores are computed by chunks or not.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Prepare the head mask if needed.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>invert_attention_mask</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L218" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attention_mask<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.Tensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask.encoder_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask.encoder_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attention_mask</strong> (<code>torch.Tensor</code>) — An attention mask.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.ModuleUtilsMixin.invert_attention_mask.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.Tensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The inverted attention mask.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Invert an attention mask (e.g., switches 0. and 1.).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.num_parameters"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>num_parameters</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.num_parameters" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.num_parameters"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L352" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">only_trainable<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">exclude_embeddings<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.num_parameters.only_trainable" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.num_parameters.only_trainable"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>only_trainable</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return only the number of trainable parameters<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.ModuleUtilsMixin.num_parameters.exclude_embeddings" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.ModuleUtilsMixin.num_parameters.exclude_embeddings"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>exclude_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return only the number of non-embeddings parameters<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.ModuleUtilsMixin.num_parameters.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The number of parameters.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get number of (optionally, trainable or non-embeddings) parameters in the module.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.ModuleUtilsMixin.reset_memory_hooks_state"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>reset_memory_hooks_state</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.ModuleUtilsMixin.reset_memory_hooks_state" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.ModuleUtilsMixin.reset_memory_hooks_state"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L194" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Reset the <code>mem_rss_diff</code> attribute of each module (see <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.modeling_utils.ModuleUtilsMixin.add_memory_hooks">add_memory_hooks()</a>).</p></div></div>
<h2 class="relative group"><a id="transformers.TFPreTrainedModel" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFPreTrainedModel
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFPreTrainedModel</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L686" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Base class for all TF models.</p>
<p><a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a> takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models as well as a few methods common to all models to:</p>
<ul><li>resize the input embeddings,</li>
<li>prune heads in the self-attention heads.</li></ul>
<p>Class attributes (overridden by derived classes):</p>
<ul><li><strong>config_class</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) — A subclass of <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> to use as configuration class
for this model architecture.</li>
<li><strong>base_model_prefix</strong> (<code>str</code>) — A string indicating the attribute associated to the base model in derived
classes of the same architecture adding modules on top of the base model.</li>
<li><strong>main_input_name</strong> (<code>str</code>) — The name of the principal input to the model (often <code>input_ids</code> for NLP
models, <code>pixel_values</code> for vision models and <code>input_values</code> for speech models).</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your model in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add model"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your model (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your model in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the model checkpoint to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel
model = TFAutoModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" with no local clone.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.compile"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>compile</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.compile" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.compile"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L877" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60"> = 'rmsprop'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60"> = 'passthrough'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metrics<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss_weights<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">weighted_metrics<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">run_eagerly<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">steps_per_execution<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>This is a thin wrapper that sets the model’s loss output head as the loss if the user does not specify a loss
function themselves.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.from_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.from_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.from_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1439" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*model_args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.from_pretrained.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.from_pretrained.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
<li><code>None</code> if you are both providing the configuration and state dictionary (resp. with keyword
arguments <code>config</code> and <code>state_dict</code>).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.from_pretrained.model_args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.from_pretrained.model_args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_args</strong> (sequence of positional arguments, <em>optional</em>) —
All remaining positional arguments will be passed to the underlying model’s <code>__init__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.from_pretrained.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.from_pretrained.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<code>Union[PretrainedConfig, str]</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>an instance of a class derived from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>,</li>
<li>a string valid as input to <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Instantiate a pretrained TF 2.0 model from a pre-trained model configuration.</p>
<p>The warning <em>Weights from XXX not initialized from pretrained model</em> means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.</p>
<p>The warning <em>Weights from XXX not used in YYY</em> means that the layer XXX is not used by YYY, therefore those
weights are discarded.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Passing <code>use_auth_token=True</code> is required when you want to use a private model.</p></div>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, TFBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Update configuration during loading.</span>
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> model.config.output_attentions == <span class="hljs-literal">True</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a Pytorch model file instead of a TensorFlow checkpoint (slower, for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>config = BertConfig.from_json_file(<span class="hljs-string">"./pt_model/my_pt_model_config.json"</span>)
<span class="hljs-meta">>>> </span>model = TFBertModel.from_pretrained(<span class="hljs-string">"./pt_model/my_pytorch_model.bin"</span>, from_pt=<span class="hljs-literal">True</span>, config=config)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.get_bias"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_bias</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.get_bias" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.get_bias"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1123" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Variable</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.get_bias.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Variable</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The weights representing the bias, None if not an LM model.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Dict of bias attached to an LM head. The key represents the name of the bias attribute.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.get_input_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_input_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.get_input_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.get_input_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L796" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Variable</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.get_input_embeddings.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Variable</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The embeddings layer mapping vocabulary to hidden states.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the model’s input embeddings layer.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.get_lm_head"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_lm_head</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.get_lm_head" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.get_lm_head"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1156" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.keras.layers.Layer</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.get_lm_head.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.keras.layers.Layer</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The LM head layer if the model has one, None if not.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>The LM Head layer. This method must be overwritten by all the models that have a lm head.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.get_output_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_output_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.get_output_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.get_output_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1063" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Variable</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.get_output_embeddings.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Variable</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The new weights mapping vocabulary to hidden states.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the model’s output embeddings</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.get_output_layer_with_bias"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_output_layer_with_bias</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.get_output_layer_with_bias" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.get_output_layer_with_bias"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1100" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.keras.layers.Layer</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.get_output_layer_with_bias.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.keras.layers.Layer</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The layer that handles the bias, None if not an LM model.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the layer that handles a bias attribute in case the model has an LM head with weights tied to the
embeddings</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.get_prefix_bias_name"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_prefix_bias_name</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.get_prefix_bias_name" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.get_prefix_bias_name"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1113" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.get_prefix_bias_name.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The _prefix name of the bias.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the concatenated _prefix name of the bias from the model name to the parent layer</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.load_repo_checkpoint"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>load_repo_checkpoint</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.load_repo_checkpoint" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.load_repo_checkpoint"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L823" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>dict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.load_repo_checkpoint.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.load_repo_checkpoint.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>) —
Can either be a repository name for your {object} in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.load_repo_checkpoint.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>dict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A dictionary of extra metadata from the checkpoint, most commonly an “epoch” count.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Loads a saved checkpoint (model weights and optimizer state) from a repo. Returns the current epoch count when
the checkpoint was made.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.prune_heads"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>prune_heads</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.prune_heads" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.prune_heads"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1365" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">heads_to_prune<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.prune_heads.heads_to_prune" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.prune_heads.heads_to_prune"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>heads_to_prune</strong> (<code>Dict[int, List[int]]</code>) —
Dictionary with keys being selected layer indices (<code>int</code>) and associated values being the list of heads
to prune in said layer (list of <code>int</code>). For instance {1: [0, 2], 2: [2, 3]} will prune heads 0 and 2 on
layer 1 and heads 2 and 3 on layer 2.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Prunes heads of the base model.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.resize_token_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>resize_token_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.resize_token_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.resize_token_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1165" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">new_num_tokens<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Variable</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.resize_token_embeddings.new_num_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.resize_token_embeddings.new_num_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>new_num_tokens</strong> (<code>int</code>, <em>optional</em>) —
The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
vectors at the end. Reducing the size will remove vectors from the end. If not provided or <code>None</code>, just
returns a pointer to the input tokens <code>tf.Variable</code> module of the model without doing anything.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFPreTrainedModel.resize_token_embeddings.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Variable</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Pointer to the input tokens Embeddings Module of the model.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Resizes input token embeddings matrix of the model if <code>new_num_tokens != config.vocab_size</code>.</p>
<p>Takes care of tying weights embeddings afterwards if the model class has a <code>tie_weights()</code> method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1377" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">saved_model<span class="opacity-60"> = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">version<span class="opacity-60"> = 1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60"> = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code>) —
Directory to which to save. Will be created if it doesn’t exist.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.save_pretrained.saved_model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.save_pretrained.saved_model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>saved_model</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If the model has to be saved in saved model format as well or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.save_pretrained.version" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.save_pretrained.version"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>version</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The version of the saved model. A saved model needs to be versioned in order to be properly loaded by
TensorFlow Serving as detailed in the official documentation
<a href="https://www.tensorflow.org/tfx/serving/serving_basic" rel="nofollow">https://www.tensorflow.org/tfx/serving/serving_basic</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.save_pretrained.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.save_pretrained.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save a model and its configuration file to a directory, so that it can be re-loaded using the
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> class method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.serving"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>serving</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.serving" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.serving"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L765" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.serving.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.serving.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>Dict[str, tf.Tensor]</code>) —
The input of the saved model as a dictionary of tensors.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Method used for serving the model.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.serving_output"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>serving_output</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.serving_output" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.serving_output"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L786" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.serving_output.output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.serving_output.output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output</strong> (<code>TFBaseModelOutput</code>) —
The output returned by the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Prepare the output of the saved model. Each model must implement this function.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.set_bias"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>set_bias</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.set_bias" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.set_bias"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1140" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.set_bias.value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.set_bias.value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>value</strong> (<code>Dict[tf.Variable]</code>) —
All the new bias attached to an LM head.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Set all the bias in the LM head.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.set_input_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>set_input_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.set_input_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.set_input_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1043" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.set_input_embeddings.value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.set_input_embeddings.value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>value</strong> (<code>tf.Variable</code>) —
The new weights mapping hidden states to vocabulary.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Set model’s input embeddings</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.set_output_embeddings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>set_output_embeddings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.set_output_embeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.set_output_embeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1083" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFPreTrainedModel.set_output_embeddings.value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFPreTrainedModel.set_output_embeddings.value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>value</strong> (<code>tf.Variable</code>) —
The new weights mapping hidden states to vocabulary.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Set model’s output embeddings</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.test_step"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>test_step</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.test_step" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.test_step"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L977" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A modification of Keras’s default test_step that cleans up the printed metrics when we use a dummy loss.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFPreTrainedModel.train_step"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>train_step</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFPreTrainedModel.train_step" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFPreTrainedModel.train_step"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L926" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A modification of Keras’s default <code>train_step</code> that cleans up the printed metrics when we use a dummy loss. If
a user specifies a loss at model compile time, this function behaves as the original Keras <code>train_step</code>. In
this case, it expects the same <code>data</code> as the original function (i.e. <code>(inputs, labels)</code>).</p>
<p>However, when the model is compiled without specifying the loss AND the expected label columns are passed as
part of the input dictionary, the loss is computed internally (inside the model class) and is used in the
backwards pass. In this case, <code>data</code> is a singleton tuple containing <code>(inputs,)</code>.</p>
<p>This is possible under the aforementioned circumstances because our overriden compile function can set an
additional loss function that reduces a <code>loss</code> output, and the model will output a <code>loss</code> component (notice the
name matching) containing the loss that was used to train the pre-trained model.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_tf_utils.TFModelUtilsMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.TFModelUtilsMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFModelUtilsMixin
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFModelUtilsMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFModelUtilsMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFModelUtilsMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFModelUtilsMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L83" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A few utilities for <code>tf.keras.Model</code>, to be used as a mixin.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>num_parameters</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L88" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">only_trainable<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters.only_trainable" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters.only_trainable"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>only_trainable</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return only the number of trainable parameters<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_tf_utils.TFModelUtilsMixin.num_parameters.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The number of parameters.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the number of (optionally, trainable) parameters in the model.</p></div></div>
<h2 class="relative group"><a id="transformers.FlaxPreTrainedModel" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxPreTrainedModel
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxPreTrainedModel"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxPreTrainedModel</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxPreTrainedModel" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxPreTrainedModel"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L72" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module<span class="opacity-60">: Module</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_shape<span class="opacity-60">: typing.Tuple = (1, 1)</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">seed<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dtype<span class="opacity-60">: dtype = <class 'jax._src.numpy.lax_numpy.float32'></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Base class for all models.</p>
<p><a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a> takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models.</p>
<p>Class attributes (overridden by derived classes):</p>
<ul><li><strong>config_class</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) — A subclass of <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> to use as configuration class
for this model architecture.</li>
<li><strong>base_model_prefix</strong> (<code>str</code>) — A string indicating the attribute associated to the base model in derived
classes of the same architecture adding modules on top of the base model.</li>
<li><strong>main_input_name</strong> (<code>str</code>) — The name of the principal input to the model (often <code>input_ids</code> for NLP
models, <code>pixel_values</code> for vision models and <code>input_values</code> for speech models).</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your model in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add model"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your model (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your model in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the model checkpoint to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxAutoModel
model = FlaxAutoModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the model to your namespace with the name "my-finetuned-bert" with no local clone.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
model.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxPreTrainedModel.from_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxPreTrainedModel.from_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxPreTrainedModel.from_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L296" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dtype<span class="opacity-60">: dtype = <class 'jax._src.numpy.lax_numpy.float32'></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*model_args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.from_pretrained.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.from_pretrained.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>pt index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In this case,
<code>from_pt</code> should be set to <code>True</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.from_pretrained.dtype" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.from_pretrained.dtype"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) —
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.from_pretrained.model_args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.from_pretrained.model_args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_args</strong> (sequence of positional arguments, <em>optional</em>) —
All remaining positional arguments will be passed to the underlying model’s <code>__init__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.from_pretrained.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.from_pretrained.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<code>Union[PretrainedConfig, str, os.PathLike]</code>, <em>optional</em>) —
Can be either:</p>
<ul>
<li>an instance of a class derived from <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>,</li>
<li>a string or path valid as input to <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Instantiate a pretrained flax model from a pre-trained model configuration.</p>
<p>The warning <em>Weights from XXX not initialized from pretrained model</em> means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.</p>
<p>The warning <em>Weights from XXX not used in YYY</em> means that the layer XXX is not used by YYY, therefore those
weights are discarded.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertConfig, FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).</span>
<span class="hljs-meta">>>> </span>config = BertConfig.from_json_file(<span class="hljs-string">"./pt_model/config.json"</span>)
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"./pt_model/pytorch_model.bin"</span>, from_pt=<span class="hljs-literal">True</span>, config=config)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxPreTrainedModel.register_for_auto_class"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>register_for_auto_class</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxPreTrainedModel.register_for_auto_class" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxPreTrainedModel.register_for_auto_class"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L716" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">auto_class<span class="opacity-60"> = 'FlaxAutoModel'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.register_for_auto_class.auto_class" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.register_for_auto_class.auto_class"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"FlaxAutoModel"</code>) —
The auto class to register this new model with.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This API is experimental and may have some slight breaking changes in the next releases.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxPreTrainedModel.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxPreTrainedModel.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxPreTrainedModel.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L659" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">params<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60"> = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory to which to save. Will be created if it doesn’t exist.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.save_pretrained.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.save_pretrained.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save a model and its configuration file to a directory, so that it can be re-loaded using the
<code>[from_pretrained()](/docs/transformers/pr_16143/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained)</code> class method</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxPreTrainedModel.to_bf16"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_bf16</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxPreTrainedModel.to_bf16" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxPreTrainedModel.to_bf16"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L191" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">params<span class="opacity-60">: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask<span class="opacity-60">: typing.Any = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.to_bf16.params" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.to_bf16.params"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>params</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> of model parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.to_bf16.mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.to_bf16.mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> with same structure as the <code>params</code> tree. The leaves should be booleans, <code>True</code> for params
you want to cast, and should be <code>False</code> for those you want to skip.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Cast the floating-point <code>params</code> to <code>jax.numpy.bfloat16</code>. This returns a new <code>params</code> tree and does not cast
the <code>params</code> in place.</p>
<p>This method can be used on TPU to explicitly convert the model parameters to bfloat16 precision to do full
half-precision training or to save weights in bfloat16 for inference in order to save memory and improve speed.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># load model</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># By default, the model parameters will be in fp32 precision, to cast these to bfloat16 precision</span>
<span class="hljs-meta">>>> </span>model.params = model.to_bf16(model.params)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># If you want don't want to cast certain parameters (for example layer norm bias and scale)</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># then pass the mask as follows</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> flax <span class="hljs-keyword">import</span> traverse_util
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span>flat_params = traverse_util.flatten_dict(model.params)
<span class="hljs-meta">>>> </span>mask = {
<span class="hljs-meta">... </span> path: (path[-<span class="hljs-number">2</span>] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"bias"</span>) <span class="hljs-keyword">and</span> path[-<span class="hljs-number">2</span>:] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"scale"</span>))
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> flat_params
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span>mask = traverse_util.unflatten_dict(mask)
<span class="hljs-meta">>>> </span>model.params = model.to_bf16(model.params, mask)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxPreTrainedModel.to_fp16"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_fp16</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxPreTrainedModel.to_fp16" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxPreTrainedModel.to_fp16"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L257" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">params<span class="opacity-60">: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask<span class="opacity-60">: typing.Any = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.to_fp16.params" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.to_fp16.params"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>params</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> of model parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.to_fp16.mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.to_fp16.mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> with same structure as the <code>params</code> tree. The leaves should be booleans, <code>True</code> for params
you want to cast, and should be <code>False</code> for those you want to skip<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Cast the floating-point <code>parmas</code> to <code>jax.numpy.float16</code>. This returns a new <code>params</code> tree and does not cast the
<code>params</code> in place.</p>
<p>This method can be used on GPU to explicitly convert the model parameters to float16 precision to do full
half-precision training or to save weights in float16 for inference in order to save memory and improve speed.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># load model</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># By default, the model params will be in fp32, to cast these to float16</span>
<span class="hljs-meta">>>> </span>model.params = model.to_fp16(model.params)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># If you want don't want to cast certain parameters (for example layer norm bias and scale)</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># then pass the mask as follows</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> flax <span class="hljs-keyword">import</span> traverse_util
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span>flat_params = traverse_util.flatten_dict(model.params)
<span class="hljs-meta">>>> </span>mask = {
<span class="hljs-meta">... </span> path: (path[-<span class="hljs-number">2</span>] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"bias"</span>) <span class="hljs-keyword">and</span> path[-<span class="hljs-number">2</span>:] != (<span class="hljs-string">"LayerNorm"</span>, <span class="hljs-string">"scale"</span>))
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> flat_params
<span class="hljs-meta">... </span>}
<span class="hljs-meta">>>> </span>mask = traverse_util.unflatten_dict(mask)
<span class="hljs-meta">>>> </span>model.params = model.to_fp16(model.params, mask)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxPreTrainedModel.to_fp32"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_fp32</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxPreTrainedModel.to_fp32" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxPreTrainedModel.to_fp32"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_utils.py#L230" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">params<span class="opacity-60">: typing.Union[typing.Dict, flax.core.frozen_dict.FrozenDict]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask<span class="opacity-60">: typing.Any = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.to_fp32.params" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.to_fp32.params"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>params</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> of model parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxPreTrainedModel.to_fp32.mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxPreTrainedModel.to_fp32.mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask</strong> (<code>Union[Dict, FrozenDict]</code>) —
A <code>PyTree</code> with same structure as the <code>params</code> tree. The leaves should be booleans, <code>True</code> for params
you want to cast, and should be <code>False</code> for those you want to skip<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Cast the floating-point <code>parmas</code> to <code>jax.numpy.float32</code>. This method can be used to explicitly convert the
model parameters to fp32 precision. This returns a new <code>params</code> tree and does not cast the <code>params</code> in place.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> FlaxBertModel
<span class="hljs-meta">>>> </span><span class="hljs-comment"># Download model and configuration from huggingface.co</span>
<span class="hljs-meta">>>> </span>model = FlaxBertModel.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># By default, the model params will be in fp32, to illustrate the use of this method,</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># we'll first cast to fp16 and back to fp32</span>
<span class="hljs-meta">>>> </span>model.params = model.to_f16(model.params)
<span class="hljs-meta">>>> </span><span class="hljs-comment"># now cast back to fp32</span>
<span class="hljs-meta">>>> </span>model.params = model.to_fp32(model.params)<!-- HTML_TAG_END --></pre></div></div></div>
<h2 class="relative group"><a id="transformers.file_utils.PushToHubMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pushing to the Hub
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.file_utils.</span><span class="font-semibold">PushToHubMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2837" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A Mixin containing the functionality to push a model or tokenizer to the hub.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your {object} in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add {object}"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your {object} (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your {object} in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the {object_files} to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> {object_class}
{<span class="hljs-built_in">object</span>} = {object_class}.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the {object} to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the {object} to your namespace with the name "my-finetuned-bert" with no local clone.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the {object} to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
{<span class="hljs-built_in">object</span>}.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<h1 class="relative group"><a id="configuration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#configuration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Configuration
</span></h1>
<p>The base class <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> implements the common methods for loading/saving a configuration
either from a local file or directory, or from a pretrained model configuration provided by the library (downloaded
from HuggingFace’s AWS S3 repository).</p>
<p>Each derived config class implements model specific attributes. Common attributes present in all config classes are:
<code>hidden_size</code>, <code>num_attention_heads</code>, and <code>num_hidden_layers</code>. Text models further implement:
<code>vocab_size</code>.</p>
<h2 class="relative group"><a id="transformers.PretrainedConfig" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>PretrainedConfig
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PretrainedConfig</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L53" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>name_or_path</strong> (<code>str</code>, <em>optional</em>, defaults to <code>""</code>) —
Store the string that was passed to <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">PreTrainedModel.from_pretrained()</a> or
<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">TFPreTrainedModel.from_pretrained()</a> as <code>pretrained_model_name_or_path</code> if the configuration was created
with such a method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.output_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.output_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should return all hidden-states.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.output_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.output_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should returns all attentions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.return_dict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.return_dict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not the model should return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.is_encoder_decoder" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.is_encoder_decoder"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_encoder_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether the model is used as an encoder/decoder or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.is_decoder" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.is_decoder"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether the model is used as decoder or not (in which case it’s used as an encoder).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.cross_attention_hidden_size**" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.cross_attention_hidden_size**"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attention_hidden_size**</strong> (<code>bool</code>, <em>optional</em>) —
The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder
setting and the cross-attention hidden dimension differs from <code>self.config.hidden_size</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.add_cross_attention" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.add_cross_attention"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_cross_attention</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether cross-attention layers should be added to the model. Note, this option is only relevant for models
that can be used as decoder models within the <a href="/docs/transformers/pr_16143/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> class, which consists of all models
in <code>AUTO_MODELS_FOR_CAUSAL_LM</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.tie_encoder_decoder" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.tie_encoder_decoder"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tie_encoder_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder
and decoder model to have the exact same parameter names.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.prune_heads" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.prune_heads"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>prune_heads</strong> (<code>Dict[int, List[int]]</code>, <em>optional</em>, defaults to <code>{}</code>) —
Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
heads to prune in said layer.</p>
<p>For instance <code>{1: [0, 2], 2: [2, 3]}</code> will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.chunk_size_feed_forward" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.chunk_size_feed_forward"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>chunk_size_feed_forward</strong> (<code>int</code>, <em>optional</em>, defaults to <code>0</code>) —
The chunk size of all feed forward layers in the residual attention blocks. A chunk size of <code>0</code> means that
the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes <code>n</code> <
sequence_length embeddings at a time. For more information on feed forward chunking, see <a href="../glossary.html#feed-forward-chunking">How does Feed
Forward Chunking work?</a>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<p class="flex items-center font-semibold">Parameters for sequence generation <span class="flex-auto border-t-2 ml-3"></span></p>
<p><!-- HTML_TAG_START -->
<ul>
<li><strong>max_length</strong> (<code>int</code>, <em>optional</em>, defaults to 20) —
Maximum length that will be used by default in the <code>generate</code> method of the model.</li>
<li><strong>min_length</strong> (<code>int</code>, <em>optional</em>, defaults to 10) —
Minimum length that will be used by default in the <code>generate</code> method of the model.</li>
<li><strong>do_sample</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag that will be used by default in the <code>generate</code> method of the model. Whether or not to use sampling ;
use greedy decoding otherwise.</li>
<li><strong>early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Flag that will be used by default in the <code>generate</code> method of the model. Whether to stop the beam search
when at least <code>num_beams</code> sentences are finished per batch or not.</li>
<li><strong>num_beams</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of beams for beam search that will be used by default in the <code>generate</code> method of the model. 1 means
no beam search.</li>
<li><strong>num_beam_groups</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams
that will be used by default in the <code>generate</code> method of the model. 1 means no group beam search.</li>
<li><strong>diversity_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
Value to control diversity for group beam search. that will be used by default in the <code>generate</code> method of
the model. 0 means no diversity penalty. The higher the penalty, the more diverse are the outputs.</li>
<li><strong>temperature</strong> (<code>float</code>, <em>optional</em>, defaults to 1) —
The value used to module the next token probabilities that will be used by default in the <code>generate</code> method
of the model. Must be strictly positive.</li>
<li><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 50) —
Number of highest probability vocabulary tokens to keep for top-k-filtering that will be used by default in
the <code>generate</code> method of the model.</li>
<li><strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1) —
Value that will be used by default in the <code>generate</code> method of the model for <code>top_p</code>. If set to float < 1,
only the most probable tokens with probabilities that add up to <code>top_p</code> or higher are kept for generation.</li>
<li><strong>repetition_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1) —
Parameter for repetition penalty that will be used by default in the <code>generate</code> method of the model. 1.0
means no penalty.</li>
<li><strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1) —
Exponential penalty to the length that will be used by default in the <code>generate</code> method of the model.</li>
<li><strong>no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) — Value that will be used by default in the —
<code>generate</code> method of the model for <code>no_repeat_ngram_size</code>. If set to int > 0, all ngrams of that size can
only occur once.</li>
<li><strong>encoder_no_repeat_ngram_size</strong> (<code>int</code>, <em>optional</em>, defaults to 0) — Value that will be used by —
default in the <code>generate</code> method of the model for <code>encoder_no_repeat_ngram_size</code>. If set to int > 0, all
ngrams of that size that occur in the <code>encoder_input_ids</code> cannot occur in the <code>decoder_input_ids</code>.</li>
<li><strong>bad_words_ids</strong> (<code>List[int]</code>, <em>optional</em>) —
List of token ids that are not allowed to be generated that will be used by default in the <code>generate</code>
method of the model. In order to get the tokens of the words that should not appear in the generated text,
use <code>tokenizer.encode(bad_word, add_prefix_space=True)</code>.</li>
<li><strong>num_return_sequences</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of independently computed returned sequences for each element in the batch that will be used by
default in the <code>generate</code> method of the model.</li>
<li><strong>output_scores</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether the model should return the logits when used for generation.</li>
<li><strong>return_dict_in_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether the model should return a <a
href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput"
>ModelOutput</a> instead of a <code>torch.LongTensor</code>.</li>
<li><strong>forced_bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the first generated token after the <code>decoder_start_token_id</code>. Useful for
multilingual models like <a href="../model_doc/mbart">mBART</a> where the first generated token needs to be the target
language token.</li>
<li><strong>forced_eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.</li>
<li><strong>remove_invalid_values</strong> (<code>bool</code>, <em>optional</em>) —
Whether to remove possible <em>nan</em> and <em>inf</em> outputs of the model to prevent the generation method to crash.
Note that using <code>remove_invalid_values</code> can slow down generation.</li>
</ul>
<!-- HTML_TAG_END --></p><p class="flex items-center font-semibold">Parameters for fine-tuning tasks <span class="flex-auto border-t-2 ml-3"></span></p>
<p><!-- HTML_TAG_START -->
<ul>
<li><strong>architectures</strong> (<code>List[str]</code>, <em>optional</em>) —
Model architectures that can be used with the model pretrained weights.</li>
<li><strong>finetuning_task</strong> (<code>str</code>, <em>optional</em>) —
Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow
or PyTorch) checkpoint.</li>
<li><strong>id2label</strong> (<code>Dict[int, str]</code>, <em>optional</em>) —
A map from index (for instance prediction index, or target index) to label.</li>
<li><strong>label2id</strong> (<code>Dict[str, int]</code>, <em>optional</em>) — A map from label to index for the model.</li>
<li><strong>num_labels</strong> (<code>int</code>, <em>optional</em>) —
Number of labels to use in the last layer added to the model, typically for a classification task.</li>
<li><strong>task_specific_params</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) —
Additional keyword arguments to store for the current task.</li>
<li><strong>problem_type</strong> (<code>str</code>, <em>optional</em>) —
Problem type for <code>XxxForSequenceClassification</code> models. Can be one of <code>"regression"</code>,
<code>"single_label_classification"</code> or <code>"multi_label_classification"</code>.</li>
</ul>
<!-- HTML_TAG_END --></p><p class="flex items-center font-semibold">Parameters linked to the tokenizer <span class="flex-auto border-t-2 ml-3"></span></p>
<p><!-- HTML_TAG_START -->
<ul>
<li><strong>tokenizer_class</strong> (<code>str</code>, <em>optional</em>) —
The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the
model by default).</li>
<li><strong>prefix</strong> (<code>str</code>, <em>optional</em>) —
A specific prompt that should be added at the beginning of each text before calling the model.</li>
<li><strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) — The id of the <em>beginning-of-stream</em> token.</li>
<li><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) — The id of the <em>padding</em> token.</li>
<li><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) — The id of the <em>end-of-stream</em> token.</li>
<li><strong>decoder_start_token_id</strong> (<code>int</code>, <em>optional</em>) —
If an encoder-decoder model starts decoding with a different token than <em>bos</em>, the id of that token.</li>
<li><strong>sep_token_id</strong> (<code>int</code>, <em>optional</em>) — The id of the <em>separation</em> token.</li>
</ul>
<!-- HTML_TAG_END --></p><p class="flex items-center font-semibold">PyTorch specific parameters <span class="flex-auto border-t-2 ml-3"></span></p>
<p><!-- HTML_TAG_START -->
<ul>
<li>
<p><strong>torchscript</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should be used with Torchscript.</p>
</li>
<li>
<p><strong>tie_word_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether the model’s input and output word embeddings should be tied. Note that this is only relevant if the
model has a output word embedding layer.</p>
</li>
<li>
<p><strong>torch_dtype</strong> (<code>str</code>, <em>optional</em>) —
The <code>dtype</code> of the weights. This attribute can be used to initialize the model to a non-default <code>dtype</code>
(which is normally <code>float32</code>) and thus allow for optimal storage allocation. For example, if the saved
model is <code>float16</code>, ideally we want to load it back using the minimal amount of memory needed to load
<code>float16</code> weights. Since the config object is stored in plain text, this attribute contains just the
floating type string without the <code>torch.</code> prefix. For example, for <code>torch.float16</code> `<code>torch_dtype</code> is the
<code>"float16"</code> string.</p>
<p>This attribute is currently not being used during model loading time, but this may change in the future
versions. But we can already start preparing for the future by saving the dtype with save_pretrained.</p>
</li>
</ul>
<!-- HTML_TAG_END --></p><p class="flex items-center font-semibold">TensorFlow specific parameters <span class="flex-auto border-t-2 ml-3"></span></p>
<p><!-- HTML_TAG_START -->
<ul>
<li><strong>use_bfloat16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the model should use BFloat16 scalars (only used by some TensorFlow models).</li>
</ul>
<!-- HTML_TAG_END --></p>
</div></div>
<p>Base class for all configuration classes. Handles a few parameters common to all models’ configurations as well as
methods for loading/downloading/saving configurations.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
initialize a model does <strong>not</strong> load the model weights. It only affects the model’s configuration.</p></div>
<p>Class attributes (overridden by derived classes):</p>
<ul><li><strong>model_type</strong> (<code>str</code>) — An identifier for the model type, serialized into the JSON file, and used to recreate
the correct object in <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoConfig">AutoConfig</a>.</li>
<li><strong>is_composition</strong> (<code>bool</code>) — Whether the config class is composed of multiple sub-configs. In this case the
config has to be initialized from two or more configs of type <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> like:
<a href="/docs/transformers/pr_16143/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> or <a href="/docs/transformers/pr_16143/en/model_doc/rag#transformers.RagConfig">~RagConfig</a>.</li>
<li><strong>keys_to_ignore_at_inference</strong> (<code>List[str]</code>) — A list of keys to ignore by default when looking at dictionary
outputs of the model during inference.</li>
<li><strong>attribute_map</strong> (<code>Dict[str, str]</code>) — A dict that maps model specific attribute names to the standardized
naming of attributes.</li></ul>
<p>Common attributes (present in all subclasses):</p>
<ul><li><strong>vocab_size</strong> (<code>int</code>) — The number of tokens in the vocabulary, which is also the first dimension of the
embeddings matrix (this attribute may be missing for models that don’t have a text modality like ViT).</li>
<li><strong>hidden_size</strong> (<code>int</code>) — The hidden size of the model.</li>
<li><strong>num_attention_heads</strong> (<code>int</code>) — The number of attention heads used in the multi-head attention layers of the
model.</li>
<li><strong>num_hidden_layers</strong> (<code>int</code>) — The number of blocks in the model.</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your config in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add config"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your config (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your config in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the configuration file to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig
config = AutoConfig.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the config to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
config.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the config to your namespace with the name "my-finetuned-bert" with no local clone.</span>
config.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the config to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
config.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
config.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.dict_torch_dtype_to_str"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>dict_torch_dtype_to_str</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.dict_torch_dtype_to_str" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.dict_torch_dtype_to_str"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L851" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">d<span class="opacity-60">: typing.Dict[str, typing.Any]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Checks whether the passed dictionary and its nested dicts have a <em>torch_dtype</em> key and if it’s not None,
converts torch.dtype to a string of just the type. For example, <code>torch.float32</code> get converted into <em>“float32”</em>
string, which can then be stored in the json format.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.from_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.from_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.from_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L653" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config_dict<span class="opacity-60">: typing.Dict[str, typing.Any]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig"
>PretrainedConfig</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_dict.config_dict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_dict.config_dict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config_dict</strong> (<code>Dict[str, Any]</code>) —
Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
retrieved from a pretrained checkpoint by leveraging the <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.get_config_dict">get_config_dict()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_dict.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_dict.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (<code>Dict[str, Any]</code>) —
Additional parameters from which to initialize the configuration object.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PretrainedConfig.from_dict.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig"
>PretrainedConfig</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The configuration object instantiated from those parameters.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Instantiates a <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> from a Python dictionary of parameters.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.from_json_file"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_json_file</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.from_json_file" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.from_json_file"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L691" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">json_file<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig"
>PretrainedConfig</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_json_file.json_file" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_json_file.json_file"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>json_file</strong> (<code>str</code> or <code>os.PathLike</code>) —
Path to the JSON file containing the parameters.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PretrainedConfig.from_json_file.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig"
>PretrainedConfig</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The configuration object instantiated from that JSON file.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Instantiates a <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> from the path to a JSON file of parameters.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.from_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.from_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.from_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L446" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig"
>PretrainedConfig</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained model configuration hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a configuration file saved using the
<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>a path or url to a saved configuration JSON <em>file</em>, e.g., <code>./my_model_directory/configuration.json</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.cache_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.cache_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.force_download" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.force_download"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to force to (re-)download the configuration files and override the cached versions if
they exist.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.resume_download" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.resume_download"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to delete incompletely received file. Attempts to resume the download if such a file
exists.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.proxies" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.proxies"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.</code> The proxies are used on each request.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.revision(str," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.revision(str,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>"main"</code>) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.return_unused_kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.return_unused_kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If <code>False</code>, then this function returns just the final configuration object.</p>
<p>If <code>True</code>, then this functions returns a <code>Tuple(config, unused_kwargs)</code> where <em>unused_kwargs</em> is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of <code>kwargs</code> which has not been used to update <code>config</code> and is otherwise ignored.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.from_pretrained.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.from_pretrained.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) —
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
by the <code>return_unused_kwargs</code> keyword parameter.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PretrainedConfig.from_pretrained.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig"
>PretrainedConfig</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The configuration object instantiated from this pretrained model.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Instantiate a <a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> (or a derived class) from a pretrained model configuration.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Passing <code>use_auth_token=True</code> is required when you want to use a private model.</p></div>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># We can't instantiate directly the base class *PretrainedConfig* so let's show the examples on a</span>
<span class="hljs-comment"># derived class: BertConfig</span>
config = BertConfig.from_pretrained(
<span class="hljs-string">"bert-base-uncased"</span>
) <span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
config = BertConfig.from_pretrained(
<span class="hljs-string">"./test/saved_model/"</span>
) <span class="hljs-comment"># E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*</span>
config = BertConfig.from_pretrained(<span class="hljs-string">"./test/saved_model/my_configuration.json"</span>)
config = BertConfig.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>)
<span class="hljs-keyword">assert</span> config.output_attentions == <span class="hljs-literal">True</span>
config, unused_kwargs = BertConfig.from_pretrained(
<span class="hljs-string">"bert-base-uncased"</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
)
<span class="hljs-keyword">assert</span> config.output_attentions == <span class="hljs-literal">True</span>
<span class="hljs-keyword">assert</span> unused_kwargs == {<span class="hljs-string">"foo"</span>: <span class="hljs-literal">False</span>}<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.get_config_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_config_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.get_config_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.get_config_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L529" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Tuple[Dict, Dict]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.get_config_dict.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.get_config_dict.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PretrainedConfig.get_config_dict.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Tuple[Dict, Dict]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The dictionary(ies) that will be used to instantiate the configuration object.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>From a <code>pretrained_model_name_or_path</code>, resolve to a dictionary of parameters, to be used for instantiating a
<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> using <code>from_dict</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.register_for_auto_class"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>register_for_auto_class</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.register_for_auto_class" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.register_for_auto_class"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L863" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">auto_class<span class="opacity-60"> = 'AutoConfig'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.register_for_auto_class.auto_class" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.register_for_auto_class.auto_class"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"AutoConfig"</code>) —
The auto class to register this new configuration with.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Register this class with a given auto class. This should only be used for custom configurations as the ones in
the library are already mapped with <code>AutoConfig</code>.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This API is experimental and may have some slight breaking changes in the next releases.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L400" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory where the configuration JSON file will be saved (will be created if it does not exist).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.save_pretrained.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.save_pretrained.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save a configuration object to the directory <code>save_directory</code>, so that it can be re-loaded using the
<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a> class method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.to_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.to_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.to_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L751" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Dict[str, Any]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PretrainedConfig.to_dict.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Dict[str, Any]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Dictionary of all the attributes that make up this configuration instance.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Serializes this instance to a Python dictionary.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.to_diff_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_diff_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.to_diff_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.to_diff_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L719" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Dict[str, Any]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PretrainedConfig.to_diff_dict.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Dict[str, Any]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Dictionary of all the attributes that make up this configuration instance,</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Removes all attributes from config which correspond to the default config attributes for better readability and
serializes to a Python dictionary.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.to_json_file"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_json_file</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.to_json_file" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.to_json_file"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L789" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">json_file_path<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_diff<span class="opacity-60">: bool = True</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.to_json_file.json_file_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.to_json_file.json_file_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>json_file_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
Path to the JSON file in which this configuration instance’s parameters will be saved.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.to_json_file.use_diff" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.to_json_file.use_diff"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_diff</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If set to <code>True</code>, only the difference between the config instance and the default <code>PretrainedConfig()</code>
is serialized to JSON file.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save this instance to a JSON file.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.to_json_string"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_json_string</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.to_json_string" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.to_json_string"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L771" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_diff<span class="opacity-60">: bool = True</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.to_json_string.use_diff" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.to_json_string.use_diff"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_diff</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If set to <code>True</code>, only the difference between the config instance and the default <code>PretrainedConfig()</code>
is serialized to JSON string.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PretrainedConfig.to_json_string.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>String containing all the attributes that make up this configuration instance in JSON format.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Serializes this instance to a JSON string.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.update"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>update</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.update" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.update"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L803" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config_dict<span class="opacity-60">: typing.Dict[str, typing.Any]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.update.config_dict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.update.config_dict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config_dict</strong> (<code>Dict[str, Any]</code>) — Dictionary of attributes that should be updated for this class.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Updates attributes of this class with attributes from <code>config_dict</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PretrainedConfig.update_from_string"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>update_from_string</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PretrainedConfig.update_from_string" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PretrainedConfig.update_from_string"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/configuration_utils.py#L813" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">update_str<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PretrainedConfig.update_from_string.update_str" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PretrainedConfig.update_from_string.update_str"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>update_str</strong> (<code>str</code>) — String with attributes that should be updated for this class.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Updates attributes of this class with attributes from <code>update_str</code>.</p>
<p>The expected format is ints, floats and strings as is, and for booleans use <code>true</code> or <code>false</code>. For example:
“n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index”</p>
<p>The keys to change have to already exist in the config object.</p></div></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en | hf_public_repos/doc-build-dev/transformers/pr_16143/en/main_classes/processors.html | <meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{"local":"processors","sections":[{"local":"transformers.ProcessorMixin","title":"Multi-modal processors"},{"local":"transformers.DataProcessor","title":"Deprecated processors"},{"local":"transformers.glue_convert_examples_to_features","title":"GLUE"},{"local":"xnli","title":"XNLI"},{"local":"squad","sections":[{"local":"transformers.data.processors.squad.SquadProcessor","title":"Processors"},{"local":"example-usage","title":"Example usage"}],"title":"SQuAD"}],"title":"Processors"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="processors" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#processors"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Processors
</span></h1>
<p>Processors can mean two different things in the Transformers library:</p>
<ul><li>the objects that pre-process inputs for multi-modal models such as <a href="../model_doc/wav2vec2">Wav2Vec2</a> (speech and text)
or <a href="../model_doc/clip">CLIP</a> (text and vision)</li>
<li>deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD.</li></ul>
<h2 class="relative group"><a id="transformers.ProcessorMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ProcessorMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Multi-modal processors
</span></h2>
<p>Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text,
vision and audio). This is handled by objects called processors, which group tokenizers (for the text modality) and
feature extractors (for vision and audio).</p>
<p>Those processors inherit from the following base class that implements the saving and loading functionality:</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ProcessorMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ProcessorMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ProcessorMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ProcessorMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L44" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>This is a mixin used to provide saving/loading functionality for all processor classes.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ProcessorMixin.from_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ProcessorMixin.from_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ProcessorMixin.from_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L157" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ProcessorMixin.from_pretrained.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ProcessorMixin.from_pretrained.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or
namespaced under a user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.
**kwargs —
Additional keyword arguments passed along to both
<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a> and
<code>from_pretrained</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Instantiate a processor associated with a pretrained model.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>This class method is simply calling the feature extractor
<a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.from_pretrained">from_pretrained()</a> and the tokenizer
<code>from_pretrained</code> methods. Please refer to the docstrings of the
methods above for more information.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your processor in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add processor"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your processor (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your processor in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the processor files to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
processor = AutoProcessor.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the processor to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the processor to your namespace with the name "my-finetuned-bert" with no local clone.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the processor to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
processor.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ProcessorMixin.register_for_auto_class"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>register_for_auto_class</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ProcessorMixin.register_for_auto_class" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ProcessorMixin.register_for_auto_class"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L190" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">auto_class<span class="opacity-60"> = 'AutoProcessor'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ProcessorMixin.register_for_auto_class.auto_class" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ProcessorMixin.register_for_auto_class.auto_class"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"AutoProcessor"</code>) —
The auto class to register this new feature extractor with.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Register this class with a given auto class. This should only be used for custom feature extractors as the ones
in the library are already mapped with <code>AutoProcessor</code>.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This API is experimental and may have some slight breaking changes in the next releases.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ProcessorMixin.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ProcessorMixin.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ProcessorMixin.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/processing_utils.py#L95" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ProcessorMixin.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ProcessorMixin.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) —
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ProcessorMixin.save_pretrained.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ProcessorMixin.save_pretrained.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your processor to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div>
<p>kwargs —
Additional key word arguments passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.file_utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it
can be reloaded using the <a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.ProcessorMixin.from_pretrained">from_pretrained()</a> method.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>This class method is simply calling <a href="/docs/transformers/pr_16143/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> and
<code>save_pretrained</code>. Please refer to the docstrings of the methods
above for more information.</p></div></div></div>
<h2 class="relative group"><a id="transformers.DataProcessor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DataProcessor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Deprecated processors
</span></h2>
<p>All processors follow the same architecture which is that of the
<a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.DataProcessor">DataProcessor</a>. The processor returns a list of
<a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.InputExample">InputExample</a>. These
<a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.InputExample">InputExample</a> can be converted to
<a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.InputFeatures">InputFeatures</a> in order to be fed to the model.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DataProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DataProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L81" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Base class for data converters for sequence classification data sets.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataProcessor.get_dev_examples"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_dev_examples</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataProcessor.get_dev_examples" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataProcessor.get_dev_examples"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L98" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_dir<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Gets a collection of <a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.InputExample">InputExample</a> for the dev set.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataProcessor.get_example_from_tensor_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_example_from_tensor_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataProcessor.get_example_from_tensor_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataProcessor.get_example_from_tensor_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L84" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tensor_dict<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Gets an example from a dict with tensorflow tensors.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataProcessor.get_labels"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_labels</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataProcessor.get_labels" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataProcessor.get_labels"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L106" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Gets the list of labels for this data set.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataProcessor.get_test_examples"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_test_examples</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataProcessor.get_test_examples" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataProcessor.get_test_examples"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L102" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_dir<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Gets a collection of <a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.InputExample">InputExample</a> for the test set.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataProcessor.get_train_examples"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_train_examples</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataProcessor.get_train_examples" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataProcessor.get_train_examples"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L94" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_dir<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Gets a collection of <a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.InputExample">InputExample</a> for the train set.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DataProcessor.tfds_map"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tfds_map</span></h4><!-- HTML_TAG_END -->
<a id="transformers.DataProcessor.tfds_map" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DataProcessor.tfds_map"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L110" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">example<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.InputExample"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">InputExample</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.InputExample" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.InputExample"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L31" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">guid<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_a<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_b<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A single training/test example for simple sequence classification.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.InputExample.to_json_string"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_json_string</span></h4><!-- HTML_TAG_END -->
<a id="transformers.InputExample.to_json_string" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.InputExample.to_json_string"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L50" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Serializes this instance to a JSON string.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.InputFeatures"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">InputFeatures</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.InputFeatures" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.InputFeatures"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L56" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: typing.List[int]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attention_mask<span class="opacity-60">: typing.Optional[typing.List[int]] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_type_ids<span class="opacity-60">: typing.Optional[typing.List[int]] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label<span class="opacity-60">: typing.Union[int, float, NoneType] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A single set of features of data. Property names are the same names as the corresponding inputs to a model.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.InputFeatures.to_json_string"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_json_string</span></h4><!-- HTML_TAG_END -->
<a id="transformers.InputFeatures.to_json_string" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.InputFeatures.to_json_string"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/utils.py#L76" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Serializes this instance to a JSON string.</p></div></div>
<h2 class="relative group"><a id="transformers.glue_convert_examples_to_features" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.glue_convert_examples_to_features"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>GLUE
</span></h2>
<p><a href="https://gluebenchmark.com/" rel="nofollow">General Language Understanding Evaluation (GLUE)</a> is a benchmark that evaluates the
performance of models across a diverse set of existing NLU tasks. It was released together with the paper <a href="https://openreview.net/pdf?id=rJ4km2R5t7" rel="nofollow">GLUE: A
multi-task benchmark and analysis platform for natural language understanding</a></p>
<p>This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
QQP, QNLI, RTE and WNLI.</p>
<p>Those processors are:</p>
<ul><li><code>MrpcProcessor</code></li>
<li><code>MnliProcessor</code></li>
<li><code>MnliMismatchedProcessor</code></li>
<li><code>Sst2Processor</code></li>
<li><code>StsbProcessor</code></li>
<li><code>QqpProcessor</code></li>
<li><code>QnliProcessor</code></li>
<li><code>RteProcessor</code></li>
<li><code>WnliProcessor</code></li></ul>
<p>Additionally, the following method can be used to load values from a data file and convert them to a list of
<a href="/docs/transformers/pr_16143/en/main_classes/processors#transformers.InputExample">InputExample</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.glue_convert_examples_to_features"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.glue_convert_examples_to_features</span></h4><!-- HTML_TAG_END -->
<a id="transformers.glue_convert_examples_to_features" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.glue_convert_examples_to_features"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/glue.py#L42" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">examples<span class="opacity-60">: typing.Union[typing.List[transformers.data.processors.utils.InputExample], ForwardRef('tf.data.Dataset')]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizer</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_list<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_mode<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Loads a data file into a list of <code>InputFeatures</code></p></div>
<h2 class="relative group"><a id="xnli" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#xnli"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>XNLI
</span></h2>
<p><a href="https://www.nyu.edu/projects/bowman/xnli/" rel="nofollow">The Cross-Lingual NLI Corpus (XNLI)</a> is a benchmark that evaluates the
quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on <a href="http://www.nyu.edu/projects/bowman/multinli/" rel="nofollow"><em>MultiNLI</em></a>: pairs of text are labeled with textual entailment annotations for 15
different languages (including both high-resource language such as English and low-resource languages such as Swahili).</p>
<p>It was released together with the paper <a href="https://arxiv.org/abs/1809.05053" rel="nofollow">XNLI: Evaluating Cross-lingual Sentence Representations</a></p>
<p>This library hosts the processor to load the XNLI data:</p>
<ul><li><code>XnliProcessor</code></li></ul>
<p>Please note that since the gold labels are available on the test set, evaluation is performed on the test set.</p>
<p>An example using these processors is given in the <a href="https://github.com/huggingface/transformers/tree/master/examples/legacy/text-classification/run_xnli.py" rel="nofollow">run_xnli.py</a> script.</p>
<h2 class="relative group"><a id="squad" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#squad"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>SQuAD
</span></h2>
<p><a href="https://rajpurkar.github.io/SQuAD-explorer//" rel="nofollow">The Stanford Question Answering Dataset (SQuAD)</a> is a benchmark that
evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
(v1.1) was released together with the paper <a href="https://arxiv.org/abs/1606.05250" rel="nofollow">SQuAD: 100,000+ Questions for Machine Comprehension of Text</a>. The second version (v2.0) was released alongside the paper <a href="https://arxiv.org/abs/1806.03822" rel="nofollow">Know What You Don’t
Know: Unanswerable Questions for SQuAD</a>.</p>
<p>This library hosts a processor for each of the two versions:</p>
<h3 class="relative group"><a id="transformers.data.processors.squad.SquadProcessor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.data.processors.squad.SquadProcessor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Processors
</span></h3>
<p>Those processors are:</p>
<ul><li><code>SquadV1Processor</code></li>
<li><code>SquadV2Processor</code></li></ul>
<p>They both inherit from the abstract class <code>SquadProcessor</code></p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.data.processors.squad.SquadProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.data.processors.squad.</span><span class="font-semibold">SquadProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.data.processors.squad.SquadProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.data.processors.squad.SquadProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L543" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Processor for the SQuAD data set. overridden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and
version 2.0 of SQuAD, respectively.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.data.processors.squad.SquadProcessor.get_dev_examples"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_dev_examples</span></h4><!-- HTML_TAG_END -->
<a id="transformers.data.processors.squad.SquadProcessor.get_dev_examples" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.data.processors.squad.SquadProcessor.get_dev_examples"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L631" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_dir<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filename<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns the evaluation example from the data directory.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_examples_from_dataset</span></h4><!-- HTML_TAG_END -->
<a id="transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.data.processors.squad.SquadProcessor.get_examples_from_dataset"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L576" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataset<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">evaluate<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Creates a list of <code>SquadExample</code>using a TFDS dataset.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow_datasets <span class="hljs-keyword">as</span> tfds
<span class="hljs-meta">>>> </span>dataset = tfds.load(<span class="hljs-string">"squad"</span>)
<span class="hljs-meta">>>> </span>training_examples = get_examples_from_dataset(dataset, evaluate=<span class="hljs-literal">False</span>)
<span class="hljs-meta">>>> </span>evaluation_examples = get_examples_from_dataset(dataset, evaluate=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.data.processors.squad.SquadProcessor.get_train_examples"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_train_examples</span></h4><!-- HTML_TAG_END -->
<a id="transformers.data.processors.squad.SquadProcessor.get_train_examples" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.data.processors.squad.SquadProcessor.get_train_examples"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L609" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_dir<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filename<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns the training examples from the data directory.</p></div></div>
<p>Additionally, the following method can be used to convert SQuAD examples into
<code>SquadFeatures</code> that can be used as model inputs.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.squad_convert_examples_to_features"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.squad_convert_examples_to_features</span></h4><!-- HTML_TAG_END -->
<a id="transformers.squad_convert_examples_to_features" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.squad_convert_examples_to_features"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/data/processors/squad.py#L318" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">examples<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_seq_length<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">doc_stride<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_query_length<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_training<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding_strategy<span class="opacity-60"> = 'max_length'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dataset<span class="opacity-60"> = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">threads<span class="opacity-60"> = 1</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tqdm_enabled<span class="opacity-60"> = True</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Converts a list of examples into a list of features that can be directly given as input to a model. It is
model-dependant and takes advantage of many of the tokenizer’s features to create the model’s inputs.</p>
<p>Example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->processor = SquadV2Processor()
examples = processor.get_dev_examples(data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=<span class="hljs-keyword">not</span> evaluate,
)<!-- HTML_TAG_END --></pre></div></div>
<p>These processors as well as the aforementionned method can be used with files containing the data as well as with the
<em>tensorflow_datasets</em> package. Examples are given below.</p>
<h3 class="relative group"><a id="example-usage" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#example-usage"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Example usage
</span></h3>
<p>Here is an example using the processors as well as the conversion method using data files:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-comment"># Loading a V2 processor</span>
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
<span class="hljs-comment"># Loading a V1 processor</span>
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=<span class="hljs-keyword">not</span> evaluate,
)<!-- HTML_TAG_END --></pre></div>
<p>Using <em>tensorflow_datasets</em> is as easy as using a data file:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-comment"># tensorflow_datasets only handle Squad V1.</span>
tfds_examples = tfds.load(<span class="hljs-string">"squad"</span>)
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=<span class="hljs-keyword">not</span> evaluate,
)<!-- HTML_TAG_END --></pre></div>
<p>Another example using these processors is given in the <a href="https://github.com/huggingface/transformers/tree/master/examples/legacy/question-answering/run_squad.py" rel="nofollow">run_squad.py</a> script.</p>
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<h1 class="relative group"><a id="trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainer
</span></h1>
<p>The <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> class provides an API for feature-complete training in PyTorch for most standard use cases. It’s used in most of the <a href="../examples">example scripts</a>.</p>
<p>Before instantiating your <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, create a <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> to access all the points of customization during training.</p>
<p>The API supports distributed training on multiple GPUs/TPUs, mixed precision through <a href="https://github.com/NVIDIA/apex" rel="nofollow">NVIDIA Apex</a> and Native AMP for PyTorch.</p>
<p>The <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> contains the basic training loop which supports the above features. To inject custom behavior you can subclass them and override the following methods:</p>
<ul><li><strong>get_train_dataloader</strong> — Creates the training DataLoader.</li>
<li><strong>get_eval_dataloader</strong> — Creates the evaluation DataLoader.</li>
<li><strong>get_test_dataloader</strong> — Creates the test DataLoader.</li>
<li><strong>log</strong> — Logs information on the various objects watching training.</li>
<li><strong>create_optimizer_and_scheduler</strong> — Sets up the optimizer and learning rate scheduler if they were not passed at
init. Note, that you can also subclass or override the <code>create_optimizer</code> and <code>create_scheduler</code> methods
separately.</li>
<li><strong>create_optimizer</strong> — Sets up the optimizer if it wasn’t passed at init.</li>
<li><strong>create_scheduler</strong> — Sets up the learning rate scheduler if it wasn’t passed at init.</li>
<li><strong>compute_loss</strong> - Computes the loss on a batch of training inputs.</li>
<li><strong>training_step</strong> — Performs a training step.</li>
<li><strong>prediction_step</strong> — Performs an evaluation/test step.</li>
<li><strong>evaluate</strong> — Runs an evaluation loop and returns metrics.</li>
<li><strong>predict</strong> — Returns predictions (with metrics if labels are available) on a test set.</li></ul>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>The <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> class is optimized for 🤗 Transformers models and can have surprising behaviors
when you use it on other models. When using it on your own model, make sure:</p>
<ul><li>your model always return tuples or subclasses of <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a>.</li>
<li>your model can compute the loss if a <code>labels</code> argument is provided and that loss is returned as the first
element of the tuple (if your model returns tuples)</li>
<li>your model can accept multiple label arguments (use the <code>label_names</code> in your <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> to indicate their name to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) but none of them should be named <code>"label"</code>.</li></ul></div>
<p>Here is an example of how to customize <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> to use a weighted loss (useful when you have an unbalanced training set):</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer
<span class="hljs-keyword">class</span> <span class="hljs-title class_">CustomTrainer</span>(<span class="hljs-title class_ inherited__">Trainer</span>):
<span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_loss</span>(<span class="hljs-params">self, model, inputs, return_outputs=<span class="hljs-literal">False</span></span>):
labels = inputs.get(<span class="hljs-string">"labels"</span>)
<span class="hljs-comment"># forward pass</span>
outputs = model(**inputs)
logits = outputs.get(<span class="hljs-string">"logits"</span>)
<span class="hljs-comment"># compute custom loss (suppose one has 3 labels with different weights)</span>
loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([<span class="hljs-number">1.0</span>, <span class="hljs-number">2.0</span>, <span class="hljs-number">3.0</span>]))
loss = loss_fct(logits.view(-<span class="hljs-number">1</span>, self.model.config.num_labels), labels.view(-<span class="hljs-number">1</span>))
<span class="hljs-keyword">return</span> (loss, outputs) <span class="hljs-keyword">if</span> return_outputs <span class="hljs-keyword">else</span> loss<!-- HTML_TAG_END --></pre></div>
<p>Another way to customize the training loop behavior for the PyTorch <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> is to use <a href="callback">callbacks</a> that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms…) and take decisions (like early stopping).</p>
<h2 class="relative group"><a id="transformers.Trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainer
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Trainer</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Trainer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L199" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_collator<span class="opacity-60">: typing.Optional[DataCollator] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">train_dataset<span class="opacity-60">: typing.Optional[torch.utils.data.dataset.Dataset] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_dataset<span class="opacity-60">: typing.Optional[torch.utils.data.dataset.Dataset] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model_init<span class="opacity-60">: typing.Callable[[], transformers.modeling_utils.PreTrainedModel] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">compute_metrics<span class="opacity-60">: typing.Union[typing.Callable[[transformers.trainer_utils.EvalPrediction], typing.Dict], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">callbacks<span class="opacity-60">: typing.Optional[typing.List[transformers.trainer_callback.TrainerCallback]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizers<span class="opacity-60">: typing.Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">preprocess_logits_for_metrics<span class="opacity-60">: typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> or <code>torch.nn.Module</code>, <em>optional</em>) —
The model to train, evaluate or use for predictions. If not provided, a <code>model_init</code> must be passed.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p><a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> is optimized to work with the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a> provided by the library. You can still use
your own models defined as <code>torch.nn.Module</code> as long as they work the same way as the 🤗 Transformers
models.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>, <em>optional</em>) —
The arguments to tweak for training. Will default to a basic instance of <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> with the
<code>output_dir</code> set to a directory named <em>tmp_trainer</em> in the current directory if not provided.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.data_collator" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.data_collator"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data_collator</strong> (<code>DataCollator</code>, <em>optional</em>) —
The function to use to form a batch from a list of elements of <code>train_dataset</code> or <code>eval_dataset</code>. Will
default to <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.default_data_collator">default_data_collator()</a> if no <code>tokenizer</code> is provided, an instance of
<a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorWithPadding">DataCollatorWithPadding</a> otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.train_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.train_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>train_dataset</strong> (<code>torch.utils.data.Dataset</code> or <code>torch.utils.data.IterableDataset</code>, <em>optional</em>) —
The dataset to use for training. If it is an <code>datasets.Dataset</code>, columns not accepted by the
<code>model.forward()</code> method are automatically removed.</p>
<p>Note that if it’s a <code>torch.utils.data.IterableDataset</code> with some randomization and you are training in a
distributed fashion, your iterable dataset should either use a internal attribute <code>generator</code> that is a
<code>torch.Generator</code> for the randomization that must be identical on all processes (and the Trainer will
manually set the seed of this <code>generator</code> at each epoch) or have a <code>set_epoch()</code> method that internally
sets the seed of the RNGs used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.eval_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.eval_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_dataset</strong> (<code>torch.utils.data.Dataset</code>, <em>optional</em>) —
The dataset to use for evaluation. If it is an <code>datasets.Dataset</code>, columns not accepted by the
<code>model.forward()</code> method are automatically removed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a>, <em>optional</em>) —
The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs the
maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
interrupted training or reuse the fine-tuned model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.model_init" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.model_init"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_init</strong> (<code>Callable[[], PreTrainedModel]</code>, <em>optional</em>) —
A function that instantiates the model to be used. If provided, each call to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> will start
from a new instance of the model as given by this function.</p>
<p>The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
be able to choose different architectures according to hyper parameters (such as layer count, sizes of
inner layers, dropout probabilities etc).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.compute_metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.compute_metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>compute_metrics</strong> (<code>Callable[[EvalPrediction], Dict]</code>, <em>optional</em>) —
The function that will be used to compute metrics at evaluation. Must take a <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.EvalPrediction">EvalPrediction</a> and return
a dictionary string to metric values.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.callbacks" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.callbacks"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>callbacks</strong> (List of <a href="/docs/transformers/pr_16143/en/main_classes/callback#transformers.TrainerCallback">TrainerCallback</a>, <em>optional</em>) —
A list of callbacks to customize the training loop. Will add those to the list of default callbacks
detailed in <a href="callback">here</a>.</p>
<p>If you want to remove one of the default callbacks used, use the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.remove_callback">Trainer.remove_callback()</a> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.optimizers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.optimizers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizers</strong> (<code>Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]</code>, <em>optional</em>) — A tuple
containing the optimizer and the scheduler to use. Will default to an instance of <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> on your model
and a scheduler given by <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.get_linear_schedule_with_warmup">get_linear_schedule_with_warmup()</a> controlled by <code>args</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.preprocess_logits_for_metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.preprocess_logits_for_metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>preprocess_logits_for_metrics</strong> (<code>Callable[[torch.Tensor, torch.Tensor], torch.Tensor]</code>, <em>optional</em>) —
A function that preprocess the logits right before caching them at each evaluation step. Must take two
tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
by this function will be reflected in the predictions received by <code>compute_metrics</code>.</p>
<p>Note that the labels (second parameter) will be <code>None</code> if the dataset does not have them.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.</p>
<p>Important attributes:</p>
<ul><li><strong>model</strong> — Always points to the core model. If using a transformers model, it will be a <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>
subclass.</li>
<li><strong>model_wrapped</strong> — Always points to the most external model in case one or more other modules wrap the
original model. This is the model that should be used for the forward pass. For example, under <code>DeepSpeed</code>,
the inner model is wrapped in <code>DeepSpeed</code> and then again in <code>torch.nn.DistributedDataParallel</code>. If the inner
model hasn’t been wrapped, then <code>self.model_wrapped</code> is the same as <code>self.model</code>.</li>
<li><strong>is_model_parallel</strong> — Whether or not a model has been switched to a model parallel mode (different from
data parallelism, this means some of the model layers are split on different GPUs).</li>
<li><strong>place_model_on_device</strong> — Whether or not to automatically place the model on the device - it will be set
to <code>False</code> if model parallel or deepspeed is used, or if the default
<code>TrainingArguments.place_model_on_device</code> is overridden to return <code>False</code> .</li>
<li><strong>is_in_train</strong> — Whether or not a model is currently running <code>train</code> (e.g. when <code>evaluate</code> is called while
in <code>train</code>)</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.add_callback"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>add_callback</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.add_callback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.add_callback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L513" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">callback<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.add_callback.callback" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.add_callback.callback"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>callback</strong> (<code>type</code> or <code>TrainerCallback</code>) —
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will instantiate a member of that class.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Add a callback to the current list of <code>TrainerCallback</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.autocast_smart_context_manager"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>autocast_smart_context_manager</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.autocast_smart_context_manager" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.autocast_smart_context_manager"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L1956" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>A helper wrapper that creates an appropriate context manager for <code>autocast</code> while feeding it the desired
arguments, depending on the situation.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.compute_loss"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>compute_loss</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.compute_loss" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.compute_loss"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2020" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_outputs<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>How the loss is computed by Trainer. By default, all models return the loss in the first element.</p>
<p>Subclass and override for custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.create_optimizer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>create_optimizer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.create_optimizer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.create_optimizer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L836" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Setup the optimizer.</p>
<p>We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer’s init through <code>optimizers</code>, or subclass and override this method in a subclass.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.create_optimizer_and_scheduler"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>create_optimizer_and_scheduler</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.create_optimizer_and_scheduler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.create_optimizer_and_scheduler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L825" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Setup the optimizer and the learning rate scheduler.</p>
<p>We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer’s init through <code>optimizers</code>, or subclass and override this method (or <code>create_optimizer</code> and/or
<code>create_scheduler</code>) in a subclass.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.create_scheduler"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>create_scheduler</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.create_scheduler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.create_scheduler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L921" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60">: Optimizer = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.create_scheduler.num_training_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.create_scheduler.num_training_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_training_steps</strong> (int) — The number of training steps to do.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.evaluate"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>evaluate</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.evaluate" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.evaluate"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2234" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_dataset<span class="opacity-60">: typing.Optional[torch.utils.data.dataset.Dataset] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_key_prefix<span class="opacity-60">: str = 'eval'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.evaluate.eval_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.evaluate.eval_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_dataset</strong> (<code>Dataset</code>, <em>optional</em>) —
Pass a dataset if you wish to override <code>self.eval_dataset</code>. If it is an <code>datasets.Dataset</code>, columns not
accepted by the <code>model.forward()</code> method are automatically removed. It must implement the <code>__len__</code>
method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.evaluate.ignore_keys" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.evaluate.ignore_keys"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_keys</strong> (<code>Lst[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.evaluate.metric_key_prefix" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.evaluate.metric_key_prefix"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metric_key_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"eval"</code>) —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is “eval” (default)<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Run evaluation and returns metrics.</p>
<p>The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init <code>compute_metrics</code> argument).</p>
<p>You can also subclass and override this method to inject custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.evaluation_loop"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>evaluation_loop</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.evaluation_loop" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.evaluation_loop"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2362" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader<span class="opacity-60">: DataLoader</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">description<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prediction_loss_only<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_key_prefix<span class="opacity-60">: str = 'eval'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prediction/evaluation loop, shared by <code>Trainer.evaluate()</code> and <code>Trainer.predict()</code>.</p>
<p>Works both with or without labels.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.floating_point_ops"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>floating_point_ops</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.floating_point_ops" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.floating_point_ops"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2683" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.floating_point_ops.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.floating_point_ops.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>Dict[str, Union[torch.Tensor, Any]]</code>) —
The inputs and targets of the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Trainer.floating_point_ops.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The number of floating-point operations.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>For models that inherit from <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>, uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
model or subclass and override this method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.get_eval_dataloader"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_eval_dataloader</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.get_eval_dataloader" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.get_eval_dataloader"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L735" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_dataset<span class="opacity-60">: typing.Optional[torch.utils.data.dataset.Dataset] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.get_eval_dataloader.eval_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.get_eval_dataloader.eval_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_dataset</strong> (<code>torch.utils.data.Dataset</code>, <em>optional</em>) —
If provided, will override <code>self.eval_dataset</code>. If it is an <code>datasets.Dataset</code>, columns not accepted by
the <code>model.forward()</code> method are automatically removed. It must implement <code>__len__</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Returns the evaluation <code>DataLoader</code>.</p>
<p>Subclass and override this method if you want to inject some custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.get_optimizer_cls_and_kwargs"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_optimizer_cls_and_kwargs</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.get_optimizer_cls_and_kwargs" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.get_optimizer_cls_and_kwargs"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L873" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.get_optimizer_cls_and_kwargs.args" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.get_optimizer_cls_and_kwargs.args"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>args</strong> (<code>transformers.training_args.TrainingArguments</code>) —
The training arguments for the training session.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Returns the optimizer class and optimizer parameters based on the training arguments.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.get_test_dataloader"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_test_dataloader</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.get_test_dataloader" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.get_test_dataloader"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L782" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">test_dataset<span class="opacity-60">: Dataset</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.get_test_dataloader.test_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.get_test_dataloader.test_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>test_dataset</strong> (<code>torch.utils.data.Dataset</code>, <em>optional</em>) —
The test dataset to use. If it is an <code>datasets.Dataset</code>, columns not accepted by the <code>model.forward()</code>
method are automatically removed. It must implement <code>__len__</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Returns the test <code>DataLoader</code>.</p>
<p>Subclass and override this method if you want to inject some custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.get_train_dataloader"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_train_dataloader</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.get_train_dataloader" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.get_train_dataloader"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L660" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns the training <code>DataLoader</code>.</p>
<p>Will use no sampler if <code>self.train_dataset</code> does not implement <code>__len__</code>, a random sampler (adapted to
distributed training if necessary) otherwise.</p>
<p>Subclass and override this method if you want to inject some custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.hyperparameter_search"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>hyperparameter_search</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.hyperparameter_search" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.hyperparameter_search"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L1812" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hp_space<span class="opacity-60">: typing.Union[typing.Callable[[ForwardRef('optuna.Trial')], typing.Dict[str, float]], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">compute_objective<span class="opacity-60">: typing.Union[typing.Callable[[typing.Dict[str, float]], float], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">n_trials<span class="opacity-60">: int = 20</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">direction<span class="opacity-60">: str = 'minimize'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">backend<span class="opacity-60">: typing.Union[ForwardRef('str'), transformers.trainer_utils.HPSearchBackend, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hp_name<span class="opacity-60">: typing.Union[typing.Callable[[ForwardRef('optuna.Trial')], str], NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.hyperparameter_search.hp_space" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.hyperparameter_search.hp_space"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hp_space</strong> (<code>Callable[["optuna.Trial"], Dict[str, float]]</code>, <em>optional</em>) —
A function that defines the hyperparameter search space. Will default to
<code>default_hp_space_optuna()</code>or <code>default_hp_space_ray()</code>or
<code>default_hp_space_sigopt()</code>depending on your backend.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.hyperparameter_search.compute_objective" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.hyperparameter_search.compute_objective"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>compute_objective</strong> (<code>Callable[[Dict[str, float]], float]</code>, <em>optional</em>) —
A function computing the objective to minimize or maximize from the metrics returned by the <code>evaluate</code>
method. Will default to <code>default_compute_objective()</code><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.hyperparameter_search.n_trials" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.hyperparameter_search.n_trials"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>n_trials</strong> (<code>int</code>, <em>optional</em>, defaults to 100) —
The number of trial runs to test.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.hyperparameter_search.direction(str," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.hyperparameter_search.direction(str,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>direction(<code>str</code>,</strong> <em>optional</em>, defaults to <code>"minimize"</code>) —
Whether to optimize greater or lower objects. Can be <code>"minimize"</code> or <code>"maximize"</code>, you should pick
<code>"minimize"</code> when optimizing the validation loss, <code>"maximize"</code> when optimizing one or several metrics.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.hyperparameter_search.backend(str" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.hyperparameter_search.backend(str"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>backend(<code>str</code></strong> or <code>HPSearchBackend</code>, <em>optional</em>) —
The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending
on which one is installed. If all are installed, will default to optuna.
kwargs —
Additional keyword arguments passed along to <code>optuna.create_study</code> or <code>ray.tune.run</code>. For more
information see:</p>
<ul>
<li>the documentation of
<a href="https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html" rel="nofollow">optuna.create_study</a></li>
<li>the documentation of <a href="https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run" rel="nofollow">tune.run</a></li>
<li>the documentation of <a href="https://app.sigopt.com/docs/endpoints/experiments/create" rel="nofollow">sigopt</a></li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Launch an hyperparameter search using <code>optuna</code> or <code>Ray Tune</code> or <code>SigOpt</code>. The optimized quantity is determined
by <code>compute_objective</code>, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>To use this method, you need to have provided a <code>model_init</code> when initializing your <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>: we need to
reinitialize the model at each new run. This is incompatible with the <code>optimizers</code> argument, so you need to
subclass <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> and override the method <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.create_optimizer_and_scheduler">create_optimizer_and_scheduler()</a> for custom
optimizer/scheduler.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.init_git_repo"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>init_git_repo</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.init_git_repo" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.init_git_repo"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2701" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">at_init<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.init_git_repo.at_init" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.init_git_repo.at_init"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>at_init</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether this function is called before any training or not. If <code>self.args.overwrite_output_dir</code> is
<code>True</code> and <code>at_init</code> is <code>True</code>, the path to the repo (which is <code>self.args.output_dir</code>) might be wiped
out.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Initializes a git repo in <code>self.args.hub_model_id</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.is_local_process_zero"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>is_local_process_zero</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.is_local_process_zero" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.is_local_process_zero"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2044" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.is_world_process_zero"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>is_world_process_zero</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.is_world_process_zero" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.is_world_process_zero"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2051" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be <code>True</code> for one process).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.log"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>log</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.log" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.log"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L1905" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logs<span class="opacity-60">: typing.Dict[str, float]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.log.logs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.log.logs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logs</strong> (<code>Dict[str, float]</code>) —
The values to log.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Log <code>logs</code> on the various objects watching training.</p>
<p>Subclass and override this method to inject custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_pt_utils.log_metrics"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>log_metrics</span></h4><!-- HTML_TAG_END -->
<a id="transformers.trainer_pt_utils.log_metrics" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_pt_utils.log_metrics"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L855" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">split<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metrics<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.log_metrics.split" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.log_metrics.split"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>split</strong> (<code>str</code>) —
Mode/split name: one of <code>train</code>, <code>eval</code>, <code>test</code><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.log_metrics.metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.log_metrics.metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metrics</strong> (<code>Dict[str, float]</code>) —
The metrics returned from train/evaluate/predictmetrics: metrics dict<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Log metrics in a specially formatted way</p>
<p>Under distributed environment this is done only for a process with rank 0.</p>
<p>Notes on memory reports:</p>
<p>In order to get memory usage report you need to install <code>psutil</code>. You can do that with <code>pip install psutil</code>.</p>
<p>Now when this method is run, you will see a report that will include: :</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-attr">init_mem_cpu_alloc_delta</span> = <span class="hljs-number">1301</span>MB
<span class="hljs-attr">init_mem_cpu_peaked_delta</span> = <span class="hljs-number">154</span>MB
<span class="hljs-attr">init_mem_gpu_alloc_delta</span> = <span class="hljs-number">230</span>MB
<span class="hljs-attr">init_mem_gpu_peaked_delta</span> = <span class="hljs-number">0</span>MB
<span class="hljs-attr">train_mem_cpu_alloc_delta</span> = <span class="hljs-number">1345</span>MB
<span class="hljs-attr">train_mem_cpu_peaked_delta</span> = <span class="hljs-number">0</span>MB
<span class="hljs-attr">train_mem_gpu_alloc_delta</span> = <span class="hljs-number">693</span>MB
<span class="hljs-attr">train_mem_gpu_peaked_delta</span> = <span class="hljs-number">7</span>MB<!-- HTML_TAG_END --></pre></div>
<p><strong>Understanding the reports:</strong></p>
<ul><li>the first segment, e.g., <code>train__</code>, tells you which stage the metrics are for. Reports starting with <code>init_</code>
will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the
<code>__init__</code> will be reported along with the <code>eval_</code> metrics.</li>
<li>the third segment, is either <code>cpu</code> or <code>gpu</code>, tells you whether it’s the general RAM or the gpu0 memory
metric.</li>
<li><code>*_alloc_delta</code> - is the difference in the used/allocated memory counter between the end and the start of the
stage - it can be negative if a function released more memory than it allocated.</li>
<li><code>*_peaked_delta</code> - is any extra memory that was consumed and then freed - relative to the current allocated
memory counter - it is never negative. When you look at the metrics of any stage you add up <code>alloc_delta</code> +
<code>peaked_delta</code> and you know how much memory was needed to complete that stage.</li></ul>
<p>The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the
main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may
use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more
memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the
future these reports will evolve to measure those too.</p>
<p>The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the
memory shared with other processes. It is important to note that it does not include swapped out memory, so the
reports could be imprecise.</p>
<p>The CPU peak memory is measured using a sampling thread. Due to python’s GIL it may miss some of the peak memory if
that thread didn’t get a chance to run when the highest memory was used. Therefore this report can be less than
reality. Using <code>tracemalloc</code> would have reported the exact peak memory, but it doesn’t report memory allocations
outside of python. So if some C++ CUDA extension allocated its own memory it won’t be reported. And therefore it
was dropped in favor of the memory sampling approach, which reads the current process memory usage.</p>
<p>The GPU allocated and peak memory reporting is done with <code>torch.cuda.memory_allocated()</code> and
<code>torch.cuda.max_memory_allocated()</code>. This metric reports only “deltas” for pytorch-specific allocations, as
<code>torch.cuda</code> memory management system doesn’t track any memory allocated outside of pytorch. For example, the very
first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.</p>
<p>Note that this tracker doesn’t account for memory allocations outside of <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>’s <code>__init__</code>, <code>train</code>,
<code>evaluate</code> and <code>predict</code> calls.</p>
<p>Because <code>evaluation</code> calls may happen during <code>train</code>, we can’t handle nested invocations because
<code>torch.cuda.max_memory_allocated</code> is a single counter, so if it gets reset by a nested eval call, <code>train</code>’s tracker
will report incorrect info. If this <a href="https://github.com/pytorch/pytorch/issues/16266" rel="nofollow">pytorch issue</a> gets resolved
it will be possible to change this class to be re-entrant. Until then we will only track the outer level of
<code>train</code>, <code>evaluate</code> and <code>predict</code> methods. Which means that if <code>eval</code> is called during <code>train</code>, it’s the latter
that will account for its memory usage and that of the former.</p>
<p>This also means that if any other tool that is used along the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> calls
<code>torch.cuda.reset_peak_memory_stats</code>, the gpu peak memory stats could be invalid. And the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will disrupt
the normal behavior of any such tools that rely on calling <code>torch.cuda.reset_peak_memory_stats</code> themselves.</p>
<p>For best performance you may want to consider turning the memory profiling off for production runs.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_pt_utils.metrics_format"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>metrics_format</span></h4><!-- HTML_TAG_END -->
<a id="transformers.trainer_pt_utils.metrics_format" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_pt_utils.metrics_format"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L829" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metrics<span class="opacity-60">: typing.Dict[str, float]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>metrics (<code>Dict[str, float]</code>)</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.metrics_format.metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.metrics_format.metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metrics</strong> (<code>Dict[str, float]</code>) —
The metrics returned from train/evaluate/predict<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.trainer_pt_utils.metrics_format.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>metrics (<code>Dict[str, float]</code>)</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The reformatted metrics</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Reformat Trainer metrics values to a human-readable format</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.num_examples"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>num_examples</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.num_examples" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.num_examples"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L938" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader<span class="opacity-60">: DataLoader</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Helper to get number of samples in a <code>DataLoader</code> by accessing its dataset.</p>
<p>Will raise an exception if the underlying dataset does not implement method <code>__len__</code></p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.pop_callback"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>pop_callback</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.pop_callback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.pop_callback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L524" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">callback<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>TrainerCallback</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.pop_callback.callback" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.pop_callback.callback"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>callback</strong> (<code>type</code> or <code>TrainerCallback</code>) —
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will pop the first member of that class found in the list of callbacks.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Trainer.pop_callback.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>TrainerCallback</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The callback removed, if found.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Remove a callback from the current list of <code>TrainerCallback</code> and returns it.</p>
<p>If the callback is not found, returns <code>None</code> (and no error is raised).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.predict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>predict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.predict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.predict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2303" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">test_dataset<span class="opacity-60">: Dataset</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_key_prefix<span class="opacity-60">: str = 'test'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.predict.test_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.predict.test_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>test_dataset</strong> (<code>Dataset</code>) —
Dataset to run the predictions on. If it is an <code>datasets.Dataset</code>, columns not accepted by the
<code>model.forward()</code> method are automatically removed. Has to implement the method <code>__len__</code><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.predict.ignore_keys" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.predict.ignore_keys"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_keys</strong> (<code>Lst[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.predict.metric_key_prefix" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.predict.metric_key_prefix"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metric_key_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"test"</code>) —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“test_bleu” if the prefix is “test” (default)<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Run prediction and returns predictions and potential metrics.</p>
<p>Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in <code>evaluate()</code>.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If your predictions or labels have different sequence length (for instance because you’re doing dynamic padding
in a token classification task) the predictions will be padded (on the right) to allow for concatenation into
one array. The padding index is -100.</p></div>
<p>Returns: <em>NamedTuple</em> A namedtuple with the following keys:</p>
<ul><li>predictions (<code>np.ndarray</code>): The predictions on <code>test_dataset</code>.</li>
<li>label_ids (<code>np.ndarray</code>, <em>optional</em>): The labels (if the dataset contained some).</li>
<li>metrics (<code>Dict[str, float]</code>, <em>optional</em>): The potential dictionary of metrics (if the dataset contained
labels).</li></ul></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.prediction_loop"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>prediction_loop</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.prediction_loop" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.prediction_loop"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2878" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader<span class="opacity-60">: DataLoader</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">description<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prediction_loss_only<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_key_prefix<span class="opacity-60">: str = 'eval'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Prediction/evaluation loop, shared by <code>Trainer.evaluate()</code> and <code>Trainer.predict()</code>.</p>
<p>Works both with or without labels.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.prediction_step"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>prediction_step</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.prediction_step" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.prediction_step"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2586" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: Module</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prediction_loss_only<span class="opacity-60">: bool</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.prediction_step.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.prediction_step.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<code>nn.Module</code>) —
The model to evaluate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.prediction_step.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.prediction_step.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>Dict[str, Union[torch.Tensor, Any]]</code>) —
The inputs and targets of the model.</p>
<p>The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument <code>labels</code>. Check your model’s documentation for all accepted arguments.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.prediction_step.prediction_loss_only" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.prediction_step.prediction_loss_only"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>prediction_loss_only</strong> (<code>bool</code>) —
Whether or not to return the loss only.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.prediction_step.ignore_keys" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.prediction_step.ignore_keys"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_keys</strong> (<code>Lst[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Trainer.prediction_step.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A tuple with the loss,
logits and labels (each being optional).</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Perform an evaluation step on <code>model</code> using <code>inputs</code>.</p>
<p>Subclass and override to inject custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2824" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = 'End of training'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">blocking<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"End of training"</code>) —
Message to commit while pushing.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.push_to_hub.blocking" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.push_to_hub.blocking"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>blocking</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether the function should return only when the <code>git push</code> has finished.
kwargs —
Additional keyword arguments passed along to <code>create_model_card()</code><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Upload <em>self.model</em> and <em>self.tokenizer</em> to the 🤗 model hub on the repo <em>self.args.hub_model_id</em>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.remove_callback"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>remove_callback</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.remove_callback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.remove_callback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L540" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">callback<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.remove_callback.callback" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.remove_callback.callback"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>callback</strong> (<code>type</code> or <code>TrainerCallback</code>) —
A <code>TrainerCallback</code> class or an instance of a <code>TrainerCallback</code>. In the
first case, will remove the first member of that class found in the list of callbacks.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Remove a callback from the current list of <code>TrainerCallback</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_pt_utils.save_metrics"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_metrics</span></h4><!-- HTML_TAG_END -->
<a id="transformers.trainer_pt_utils.save_metrics" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_pt_utils.save_metrics"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L945" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">split<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metrics<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">combined<span class="opacity-60"> = True</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.save_metrics.split" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.save_metrics.split"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>split</strong> (<code>str</code>) —
Mode/split name: one of <code>train</code>, <code>eval</code>, <code>test</code>, <code>all</code><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.save_metrics.metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.save_metrics.metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metrics</strong> (<code>Dict[str, float]</code>) —
The metrics returned from train/evaluate/predict<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.save_metrics.combined" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.save_metrics.combined"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>combined</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Creates combined metrics by updating <code>all_results.json</code> with metrics of this call<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save metrics into a json file for that split, e.g. <code>train_results.json</code>.</p>
<p>Under distributed environment this is done only for a process with rank 0.</p>
<p>To understand the metrics please read the docstring of <code>log_metrics()</code> The only difference is that raw
unformatted numbers are saved in the current method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.save_model"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_model</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.save_model" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.save_model"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L2063" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_dir<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">_internal_call<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Will save the model, so you can reload it using <code>from_pretrained()</code>.</p>
<p>Will only save from the main process.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_pt_utils.save_state"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_state</span></h4><!-- HTML_TAG_END -->
<a id="transformers.trainer_pt_utils.save_state" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_pt_utils.save_state"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L983" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model</p>
<p>Under distributed environment this is done only for a process with rank 0.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.train"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>train</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.train" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.train"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L1102" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">resume_from_checkpoint<span class="opacity-60">: typing.Union[str, bool, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">trial<span class="opacity-60">: typing.Union[ForwardRef('optuna.Trial'), typing.Dict[str, typing.Any]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys_for_eval<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.train.resume_from_checkpoint" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.train.resume_from_checkpoint"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>resume_from_checkpoint</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) —
If a <code>str</code>, local path to a saved checkpoint as saved by a previous instance of <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>. If a
<code>bool</code> and equals <code>True</code>, load the last checkpoint in <em>args.output_dir</em> as saved by a previous instance
of <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>. If present, training will resume from the model/optimizer/scheduler states loaded here.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.train.trial" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.train.trial"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>trial</strong> (<code>optuna.Trial</code> or <code>Dict[str, Any]</code>, <em>optional</em>) —
The trial run or the hyperparameter dictionary for hyperparameter search.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.train.ignore_keys_for_eval" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.train.ignore_keys_for_eval"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_keys_for_eval</strong> (<code>List[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions for evaluation during the training.
kwargs —
Additional keyword arguments used to hide deprecated arguments<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Main training entry point.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Trainer.training_step"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>training_step</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Trainer.training_step" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Trainer.training_step"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer.py#L1971" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: Module</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: typing.Dict[str, typing.Union[torch.Tensor, typing.Any]]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.Tensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.training_step.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.training_step.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<code>nn.Module</code>) —
The model to train.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Trainer.training_step.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Trainer.training_step.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>Dict[str, Union[torch.Tensor, Any]]</code>) —
The inputs and targets of the model.</p>
<p>The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument <code>labels</code>. Check your model’s documentation for all accepted arguments.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Trainer.training_step.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.Tensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The tensor with training loss on this batch.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Perform a training step on a batch of inputs.</p>
<p>Subclass and override to inject custom behavior.</p></div></div>
<h2 class="relative group"><a id="transformers.Seq2SeqTrainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Seq2SeqTrainer
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Seq2SeqTrainer"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Seq2SeqTrainer</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Seq2SeqTrainer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Seq2SeqTrainer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_seq2seq.py#L30" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Union[transformers.modeling_utils.PreTrainedModel, torch.nn.modules.module.Module] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: TrainingArguments = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_collator<span class="opacity-60">: typing.Optional[DataCollator] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">train_dataset<span class="opacity-60">: typing.Optional[torch.utils.data.dataset.Dataset] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_dataset<span class="opacity-60">: typing.Optional[torch.utils.data.dataset.Dataset] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model_init<span class="opacity-60">: typing.Callable[[], transformers.modeling_utils.PreTrainedModel] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">compute_metrics<span class="opacity-60">: typing.Union[typing.Callable[[transformers.trainer_utils.EvalPrediction], typing.Dict], NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">callbacks<span class="opacity-60">: typing.Optional[typing.List[transformers.trainer_callback.TrainerCallback]] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizers<span class="opacity-60">: typing.Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">preprocess_logits_for_metrics<span class="opacity-60">: typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Seq2SeqTrainer.evaluate"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>evaluate</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Seq2SeqTrainer.evaluate" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Seq2SeqTrainer.evaluate"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_seq2seq.py#L31" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_dataset<span class="opacity-60">: typing.Optional[torch.utils.data.dataset.Dataset] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_key_prefix<span class="opacity-60">: str = 'eval'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.evaluate.eval_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.evaluate.eval_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_dataset</strong> (<code>Dataset</code>, <em>optional</em>) —
Pass a dataset if you wish to override <code>self.eval_dataset</code>. If it is an <code>datasets.Dataset</code>, columns not
accepted by the <code>model.forward()</code> method are automatically removed. It must implement the <code>__len__</code>
method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.evaluate.ignore_keys" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.evaluate.ignore_keys"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_keys</strong> (<code>List[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.evaluate.metric_key_prefix" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.evaluate.metric_key_prefix"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metric_key_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"eval"</code>) —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is <code>"eval"</code> (default)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.evaluate.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.evaluate.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
The maximum target length to use when predicting with the generate method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.evaluate.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.evaluate.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>, <em>optional</em>) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Run evaluation and returns metrics.</p>
<p>The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init <code>compute_metrics</code> argument).</p>
<p>You can also subclass and override this method to inject custom behavior.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Seq2SeqTrainer.predict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>predict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Seq2SeqTrainer.predict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Seq2SeqTrainer.predict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_seq2seq.py#L72" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">test_dataset<span class="opacity-60">: Dataset</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_keys<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_key_prefix<span class="opacity-60">: str = 'test'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.predict.test_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.predict.test_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>test_dataset</strong> (<code>Dataset</code>) —
Dataset to run the predictions on. If it is an <code>datasets.Dataset</code>, columns not accepted by the
<code>model.forward()</code> method are automatically removed. Has to implement the method <code>__len__</code><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.predict.ignore_keys" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.predict.ignore_keys"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_keys</strong> (<code>List[str]</code>, <em>optional</em>) —
A list of keys in the output of your model (if it is a dictionary) that should be ignored when
gathering predictions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.predict.metric_key_prefix" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.predict.metric_key_prefix"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metric_key_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"eval"</code>) —
An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named
“eval_bleu” if the prefix is <code>"eval"</code> (default)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.predict.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.predict.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
The maximum target length to use when predicting with the generate method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainer.predict.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainer.predict.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>, <em>optional</em>) —
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Run prediction and returns predictions and potential metrics.</p>
<p>Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in <code>evaluate()</code>.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If your predictions or labels have different sequence lengths (for instance because you’re doing dynamic
padding in a token classification task) the predictions will be padded (on the right) to allow for
concatenation into one array. The padding index is -100.</p></div>
<p>Returns: <em>NamedTuple</em> A namedtuple with the following keys:</p>
<ul><li>predictions (<code>np.ndarray</code>): The predictions on <code>test_dataset</code>.</li>
<li>label_ids (<code>np.ndarray</code>, <em>optional</em>): The labels (if the dataset contained some).</li>
<li>metrics (<code>Dict[str, float]</code>, <em>optional</em>): The potential dictionary of metrics (if the dataset contained
labels).</li></ul></div></div>
<h2 class="relative group"><a id="transformers.TrainingArguments" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TrainingArguments
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainingArguments"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TrainingArguments</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TrainingArguments" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainingArguments"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L86" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_dir<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite_output_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_train<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_eval<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_predict<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">evaluation_strategy<span class="opacity-60">: IntervalStrategy = 'no'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prediction_loss_only<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_device_train_batch_size<span class="opacity-60">: int = 8</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_device_eval_batch_size<span class="opacity-60">: int = 8</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_gpu_train_batch_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_gpu_eval_batch_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">gradient_accumulation_steps<span class="opacity-60">: int = 1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_accumulation_steps<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">learning_rate<span class="opacity-60">: float = 5e-05</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">weight_decay<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_beta1<span class="opacity-60">: float = 0.9</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_beta2<span class="opacity-60">: float = 0.999</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_epsilon<span class="opacity-60">: float = 1e-08</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_grad_norm<span class="opacity-60">: float = 1.0</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_train_epochs<span class="opacity-60">: float = 3.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_steps<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">lr_scheduler_type<span class="opacity-60">: SchedulerType = 'linear'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">warmup_ratio<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">warmup_steps<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">log_level<span class="opacity-60">: typing.Optional[str] = 'passive'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">log_level_replica<span class="opacity-60">: typing.Optional[str] = 'passive'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">log_on_each_node<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_dir<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_strategy<span class="opacity-60">: IntervalStrategy = 'steps'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_first_step<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_steps<span class="opacity-60">: int = 500</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_nan_inf_filter<span class="opacity-60">: str = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_strategy<span class="opacity-60">: IntervalStrategy = 'steps'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_steps<span class="opacity-60">: int = 500</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_total_limit<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_on_each_node<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">no_cuda<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">seed<span class="opacity-60">: int = 42</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_seed<span class="opacity-60">: int = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bf16<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16_opt_level<span class="opacity-60">: str = 'O1'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">half_precision_backend<span class="opacity-60">: str = 'auto'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bf16_full_eval<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16_full_eval<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tf32<span class="opacity-60">: bool = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">local_rank<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">xpu_backend<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tpu_num_cores<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tpu_metrics_debug<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">debug<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader_drop_last<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_steps<span class="opacity-60">: int = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader_num_workers<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_index<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">run_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">disable_tqdm<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">remove_unused_columns<span class="opacity-60">: typing.Optional[bool] = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_names<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">load_best_model_at_end<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_for_best_model<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">greater_is_better<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_data_skip<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sharded_ddp<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">deepspeed<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_smoothing_factor<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optim<span class="opacity-60">: OptimizerNames = 'adamw_hf'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adafactor<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">group_by_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">length_column_name<span class="opacity-60">: typing.Optional[str] = 'length'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">report_to<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ddp_find_unused_parameters<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ddp_bucket_cap_mb<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader_pin_memory<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_memory_metrics<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_legacy_prediction_loop<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">resume_from_checkpoint<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_model_id<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_strategy<span class="opacity-60">: HubStrategy = 'every_save'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_token<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">gradient_checkpointing<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16_backend<span class="opacity-60">: str = 'auto'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub_model_id<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub_organization<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub_token<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mp_parameters<span class="opacity-60">: str = ''</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.output_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.output_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_dir</strong> (<code>str</code>) —
The output directory where the model predictions and checkpoints will be written.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.overwrite_output_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.overwrite_output_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite_output_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If <code>True</code>, overwrite the content of the output directory. Use this to continue training if <code>output_dir</code>
points to a checkpoint directory.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.do_train" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.do_train"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_train</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run training or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used
by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.do_eval" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.do_eval"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_eval</strong> (<code>bool</code>, <em>optional</em>) —
Whether to run evaluation on the validation set or not. Will be set to <code>True</code> if <code>evaluation_strategy</code> is
different from <code>"no"</code>. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your
training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.do_predict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.do_predict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_predict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run predictions on the test set or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s
intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.evaluation_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.evaluation_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>evaluation_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"no"</code>) —
The evaluation strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No evaluation is done during training.</li>
<li><code>"steps"</code>: Evaluation is done (and logged) every <code>eval_steps</code>.</li>
<li><code>"epoch"</code>: Evaluation is done at the end of each epoch.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.prediction_loss_only" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.prediction_loss_only"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>prediction_loss_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When performing evaluation and generating predictions, only returns the loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.per_device_train_batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.per_device_train_batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>per_device_train_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.per_device_eval_batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.per_device_eval_batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>per_device_eval_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for evaluation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.gradient_accumulation_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.gradient_accumulation_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>gradient_accumulation_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
evaluation, save will be conducted every <code>gradient_accumulation_steps * xxx_step</code> training examples.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.eval_accumulation_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.eval_accumulation_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_accumulation_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
requires more memory).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.learning_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.learning_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>learning_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 5e-5) —
The initial learning rate for <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.weight_decay" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.weight_decay"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a>
optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.adam_beta1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.adam_beta1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_beta1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.adam_beta2" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.adam_beta2"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_beta2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.adam_epsilon" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.adam_epsilon"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-8) —
The epsilon hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.max_grad_norm" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.max_grad_norm"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_grad_norm</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Maximum gradient norm (for gradient clipping).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.num_train_epochs(float," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.num_train_epochs(float,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_train_epochs(<code>float</code>,</strong> <em>optional</em>, defaults to 3.0) —
Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
the last epoch before stopping training).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.max_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.max_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
If set to a positive number, the total number of training steps to perform. Overrides <code>num_train_epochs</code>.
In case of using a finite iterable dataset the training may stop before reaching the set number of steps
when all data is exhausted<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.lr_scheduler_type" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.lr_scheduler_type"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>lr_scheduler_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a>, <em>optional</em>, defaults to <code>"linear"</code>) —
The scheduler type to use. See the documentation of <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a> for all possible values.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.warmup_ratio" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.warmup_ratio"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>warmup_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
Ratio of total training steps used for a linear warmup from 0 to <code>learning_rate</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>warmup_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of steps used for a linear warmup from 0 to <code>learning_rate</code>. Overrides any effect of <code>warmup_ratio</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.log_level" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.log_level"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>log_level</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’,
‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and lets the
application set the level.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.log_level_replica" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.log_level_replica"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>log_level_replica</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on replicas. Same choices as <code>log_level</code>”<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.log_on_each_node" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.log_on_each_node"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>log_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
In multinode distributed training, whether to log using <code>log_level</code> once per node, or only on the main
node.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.logging_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.logging_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_dir</strong> (<code>str</code>, <em>optional</em>) —
<a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a> log directory. Will default to
*output_dir/runs/<strong>CURRENT_DATETIME_HOSTNAME*</strong>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.logging_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.logging_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The logging strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No logging is done during training.</li>
<li><code>"epoch"</code>: Logging is done at the end of each epoch.</li>
<li><code>"steps"</code>: Logging is done every <code>logging_steps</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.logging_first_step" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.logging_first_step"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_first_step</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to log and evaluate the first <code>global_step</code> or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.logging_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.logging_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of update steps between two logs if <code>logging_strategy="steps"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.logging_nan_inf_filter" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.logging_nan_inf_filter"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_nan_inf_filter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to filter <code>nan</code> and <code>inf</code> losses for logging. If set to <code>True</code> the loss of every step that is <code>nan</code>
or <code>inf</code> is filtered and the average loss of the current logging window is taken instead.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p><code>logging_nan_inf_filter</code> only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.save_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.save_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The checkpoint save strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No save is done during training.</li>
<li><code>"epoch"</code>: Save is done at the end of each epoch.</li>
<li><code>"steps"</code>: Save is done every <code>save_steps</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.save_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.save_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of updates steps before two checkpoint saves if <code>save_strategy="steps"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.save_total_limit" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.save_total_limit"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_total_limit</strong> (<code>int</code>, <em>optional</em>) —
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
<code>output_dir</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.save_on_each_node" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.save_on_each_node"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
the main one.</p>
<p>This should not be activated when the different nodes use the same storage as the files will be saved with
the same names for each node.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.no_cuda" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.no_cuda"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>no_cuda</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to not use CUDA even when it is available or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.seed" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.seed"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>seed</strong> (<code>int</code>, <em>optional</em>, defaults to 42) —
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
<code>model_init</code> function to instantiate the model if it has some randomly initialized parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.data_seed" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.data_seed"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data_seed</strong> (<code>int</code>, <em>optional</em>) —
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as <code>seed</code>. This can be used to ensure reproducibility of data sampling, independent of the model
seed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.bf16" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.bf16"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bf16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
NVIDIA architecture. This is an experimental API and it may change.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.fp16" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.fp16"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.fp16_opt_level" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.fp16_opt_level"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16_opt_level</strong> (<code>str</code>, <em>optional</em>, defaults to ‘O1’) —
For <code>fp16</code> training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on
the <a href="https://nvidia.github.io/apex/amp" rel="nofollow">Apex documentation</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.fp16_backend" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.fp16_backend"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
This argument is deprecated. Use <code>half_precision_backend</code> instead.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.half_precision_backend" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.half_precision_backend"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>half_precision_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
The backend to use for mixed precision training. Must be one of <code>"auto"</code>, <code>"amp"</code> or <code>"apex"</code>. <code>"auto"</code>
will use AMP or APEX depending on the PyTorch version detected, while the other choices will force the
requested backend.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.bf16_full_eval" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.bf16_full_eval"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bf16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values. This is an experimental API and it may change.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.fp16_full_eval" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.fp16_full_eval"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.tf32" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.tf32"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tf32</strong> (<code>bool</code>, <em>optional</em>) —
Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API
and it may change.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.local_rank" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.local_rank"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>local_rank</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Rank of the process during distributed training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.xpu_backend" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.xpu_backend"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>xpu_backend</strong> (<code>str</code>, <em>optional</em>) —
The backend to use for xpu distributed training. Must be one of <code>"mpi"</code> or <code>"ccl"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.tpu_num_cores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.tpu_num_cores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tpu_num_cores</strong> (<code>int</code>, <em>optional</em>) —
When training on TPU, the number of TPU cores (automatically passed by launcher script).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.dataloader_drop_last" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.dataloader_drop_last"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dataloader_drop_last</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.eval_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.eval_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of update steps between two evaluations if <code>evaluation_strategy="steps"</code>. Will default to the same
value as <code>logging_steps</code> if not set.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.dataloader_num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.dataloader_num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dataloader_num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
main process.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.past_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.past_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_index</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Some models like <a href="../model_doc/transformerxl">TransformerXL</a> or <a href="../model_doc/xlnet">XLNet</a> can make use of
the past hidden states for their predictions. If this argument is set to a positive int, the <code>Trainer</code> will
use the corresponding output (usually index 2) as the past state and feed it to the model at the next
training step under the keyword argument <code>mems</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.run_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.run_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>run_name</strong> (<code>str</code>, <em>optional</em>) —
A descriptor for the run. Typically used for <a href="https://www.wandb.com/" rel="nofollow">wandb</a> and
<a href="https://www.mlflow.org/" rel="nofollow">mlflow</a> logging.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.disable_tqdm" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.disable_tqdm"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>disable_tqdm</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not to disable the tqdm progress bars and table of metrics produced by
<code>NotebookTrainingTracker</code> in Jupyter Notebooks. Will default to <code>True</code> if the logging level is
set to warn or lower (default), <code>False</code> otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.remove_unused_columns" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.remove_unused_columns"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>remove_unused_columns</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If using <code>datasets.Dataset</code> datasets, whether or not to automatically remove the columns unused by the
model forward method.</p>
<p>(Note that this behavior is not implemented for <code>TFTrainer</code>yet.)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.label_names" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.label_names"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_names</strong> (<code>List[str]</code>, <em>optional</em>) —
The list of keys in your dictionary of inputs that correspond to the labels.</p>
<p>Will eventually default to <code>["labels"]</code> except if the model used is one of the <code>XxxForQuestionAnswering</code> in
which case it will default to <code>["start_positions", "end_positions"]</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.load_best_model_at_end" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.load_best_model_at_end"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>load_best_model_at_end</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to load the best model found during training at the end of training.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p>When set to <code>True</code>, the parameters <code>save_strategy</code> needs to be the same as <code>eval_strategy</code>, and in the case
it is “steps”, <code>save_steps</code> must be a round multiple of <code>eval_steps</code>.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.metric_for_best_model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.metric_for_best_model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metric_for_best_model</strong> (<code>str</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> to specify the metric to use to compare two different
models. Must be the name of a metric returned by the evaluation with or without the prefix <code>"eval_"</code>. Will
default to <code>"loss"</code> if unspecified and <code>load_best_model_at_end=True</code> (to use the evaluation loss).</p>
<p>If you set this value, <code>greater_is_better</code> will default to <code>True</code>. Don’t forget to set it to <code>False</code> if
your metric is better when lower.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.greater_is_better" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.greater_is_better"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>greater_is_better</strong> (<code>bool</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> and <code>metric_for_best_model</code> to specify if better models
should have a greater metric or not. Will default to:</p>
<ul>
<li><code>True</code> if <code>metric_for_best_model</code> is set to a value that isn’t <code>"loss"</code> or <code>"eval_loss"</code>.</li>
<li><code>False</code> if <code>metric_for_best_model</code> is not set, or set to <code>"loss"</code> or <code>"eval_loss"</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.ignore_data_skip" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.ignore_data_skip"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_data_skip</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to <code>True</code>, the training will begin faster (as that skipping step
can take a long time) but will not yield the same results as the interrupted training would have.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.sharded_ddp" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.sharded_ddp"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sharded_ddp</strong> (<code>bool</code>, <code>str</code> or list of <code>ShardedDDPOption</code> <em>optional</em>, defaults to <code>False</code>) —
Use Sharded DDP training from <a href="https://github.com/facebookresearch/fairscale" rel="nofollow">FairScale</a> (in distributed
training only). This is an experimental feature.</p>
<p>A list of options along the following:</p>
<ul>
<li><code>"simple"</code>: to use first instance of sharded DDP released by fairscale (<code>ShardedDDP</code>) similar to ZeRO-2.</li>
<li><code>"zero_dp_2"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-2 mode (with <code>reshard_after_forward=False</code>).</li>
<li><code>"zero_dp_3"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-3 mode (with <code>reshard_after_forward=True</code>).</li>
<li><code>"offload"</code>: to add ZeRO-offload (only compatible with <code>"zero_dp_2"</code> and <code>"zero_dp_3"</code>).</li>
</ul>
<p>If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty
list for <code>False</code> and <code>["simple"]</code> for <code>True</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.deepspeed" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.deepspeed"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>deepspeed</strong> (<code>str</code> or <code>dict</code>, <em>optional</em>) —
Use <a href="https://github.com/microsoft/deepspeed" rel="nofollow">Deepspeed</a>. This is an experimental feature and its API may
evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
<code>ds_config.json</code>) or an already loaded json file as a <code>dict</code>”<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.label_smoothing_factor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.label_smoothing_factor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_smoothing_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
labels are changed from 0s and 1s to <code>label_smoothing_factor/num_labels</code> and <code>1 - label_smoothing_factor + label_smoothing_factor/num_labels</code> respectively.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.debug" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.debug"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>debug</strong> (<code>str</code> or list of <code>DebugOption</code> <em>optional</em>, defaults to <code>""</code>) —
Enable one or more debug features. This is an experimental feature.</p>
<p>Possible options are:</p>
<ul>
<li><code>"underflow_overflow"</code>: detects overflow in model’s input/outputs and reports the last frames that led to
the event</li>
<li><code>"tpu_metrics_debug"</code>: print debug metrics on TPU</li>
</ul>
<p>The options should be separated by whitespaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.optim" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.optim"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optim</strong> (<code>str</code> or <code>training_args.OptimizerNames</code> <em>optional</em>, defaults to <code>"adamw_hf"</code>) —
The optimizer to use: adamw_hf, adamw_torch, adamw_apex_fused, or adafactor.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.adafactor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.adafactor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adafactor</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
This argument is deprecated. Use <code>--optim adafactor</code> instead.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.group_by_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.group_by_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>group_by_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to group together samples of roughly the same length in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.length_column_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.length_column_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>length_column_name</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"length"</code>) —
Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
than computing them on train startup. Ignored unless <code>group_by_length</code> is <code>True</code> and the dataset is an
instance of <code>Dataset</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.report_to" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.report_to"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>report_to</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>, defaults to <code>"all"</code>) —
The list of integrations to report the results and logs to. Supported platforms are <code>"azure_ml"</code>,
<code>"comet_ml"</code>, <code>"mlflow"</code>, <code>"tensorboard"</code> and <code>"wandb"</code>. Use <code>"all"</code> to report to all integrations
installed, <code>"none"</code> for no integrations.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.ddp_find_unused_parameters" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.ddp_find_unused_parameters"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ddp_find_unused_parameters</strong> (<code>bool</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>find_unused_parameters</code> passed to
<code>DistributedDataParallel</code>. Will default to <code>False</code> if gradient checkpointing is used, <code>True</code> otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.ddp_bucket_cap_mb" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.ddp_bucket_cap_mb"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ddp_bucket_cap_mb</strong> (<code>int</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>bucket_cap_mb</code> passed to <code>DistributedDataParallel</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.dataloader_pin_memory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.dataloader_pin_memory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dataloader_pin_memory</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether you want to pin memory in data loaders or not. Will default to <code>True</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.skip_memory_metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.skip_memory_metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_memory_metrics</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
down the training and evaluation speed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push the model to the Hub every time the model is saved. If this is activated,
<code>output_dir</code> will begin a git directory synced with the the repo (determined by <code>hub_model_id</code>) and the
content will be pushed each time a save is triggered (depending on your <code>save_strategy</code>). Calling
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> will also trigger a push.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>If <code>output_dir</code> exists, it needs to be a local clone of the repository to which the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will be
pushed.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.resume_from_checkpoint" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.resume_from_checkpoint"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>resume_from_checkpoint</strong> (<code>str</code>, <em>optional</em>) —
The path to a folder with a valid checkpoint for your model. This argument is not directly used by
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.hub_model_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.hub_model_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_model_id</strong> (<code>str</code>, <em>optional</em>) —
The name of the repository to keep in sync with the local <em>output_dir</em>. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance <code>"user_name/model"</code>, which allows you to push to an organization you are a member of with
<code>"organization_name/model"</code>. Will default to <code>user_name/output_dir_name</code> with <em>output_dir_name</em> being the
name of <code>output_dir</code>.</p>
<p>Will default to to the name of <code>output_dir</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.hub_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.hub_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_strategy</strong> (<code>str</code> or <code>HubStrategy</code> <em>optional</em>, defaults to <code>"every_save"</code>) —
Defines the scope of what is pushed to the Hub and when. Possible values are:</p>
<ul>
<li><code>"end"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and a
draft of a model card when the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> method is called.</li>
<li><code>"every_save"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.</li>
<li><code>"checkpoint"</code>: like <code>"every_save"</code> but the latest checkpoint is also pushed in a subfolder named
last-checkpoint, allowing you to resume training easily with
<code>trainer.train(resume_from_checkpoint="last-checkpoint")</code>.</li>
<li><code>"all_checkpoints"</code>: like <code>"checkpoint"</code> but all checkpoints are pushed like they appear in the output
folder (so you will get one checkpoint folder per folder in your final repository)</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.hub_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.hub_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_token</strong> (<code>str</code>, <em>optional</em>) —
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
<code>huggingface-cli login</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.gradient_checkpointing" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.gradient_checkpointing"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>gradient_checkpointing</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If True, use gradient checkpointing to save memory at the expense of slower backward pass.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>TrainingArguments is the subset of the arguments we use in our example scripts <strong>which relate to the training loop
itself</strong>.</p>
<p>Using <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.HfArgumentParser">HfArgumentParser</a> we can turn this class into
<a href="https://docs.python.org/3/library/argparse#module-argparse" rel="nofollow">argparse</a> arguments that can be specified on the
command line.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainingArguments.get_process_log_level"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_process_log_level</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainingArguments.get_process_log_level" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainingArguments.get_process_log_level"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1199" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.</p>
<p>For the main process the log level defaults to <code>logging.INFO</code> unless overridden by <code>log_level</code> argument.</p>
<p>For the replica processes the log level defaults to <code>logging.WARNING</code> unless overridden by <code>log_level_replica</code>
argument.</p>
<p>The choice between the main and replica process settings is made according to the return value of <code>should_log</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainingArguments.get_warmup_steps"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_warmup_steps</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainingArguments.get_warmup_steps" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainingArguments.get_warmup_steps"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1285" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Get number of steps used for a linear warmup.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainingArguments.main_process_first"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>main_process_first</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainingArguments.main_process_first" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainingArguments.main_process_first"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1230" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">local<span class="opacity-60"> = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">desc<span class="opacity-60"> = 'work'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.main_process_first.local" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.main_process_first.local"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>local</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
if <code>True</code> first means process of rank 0 of each node if <code>False</code> first means process of rank 0 of node
rank 0 In multi-node environment with a shared filesystem you most likely will want to use
<code>local=False</code> so that only the main process of the first node will do the processing. If however, the
filesystem is not shared, then the main process of each node will need to do the processing, which is
the default behavior.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TrainingArguments.main_process_first.desc" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TrainingArguments.main_process_first.desc"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>desc</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"work"</code>) —
a work description to be used in debug logs<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A context manager for torch distributed environment where on needs to do something on the main process, while
blocking replicas, and when it’s finished releasing the replicas.</p>
<p>One such use is for <code>datasets</code>’s <code>map</code> feature which to be efficient should be run once on the main process,
which upon completion saves a cached version of results and which then automatically gets loaded by the
replicas.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainingArguments.to_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainingArguments.to_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainingArguments.to_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1294" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Serializes this instance while replace <code>Enum</code> by their values (for JSON serialization support). It obfuscates
the token values by removing their value.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainingArguments.to_json_string"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_json_string</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainingArguments.to_json_string" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainingArguments.to_json_string"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1309" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Serializes this instance to a JSON string.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TrainingArguments.to_sanitized_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_sanitized_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TrainingArguments.to_sanitized_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TrainingArguments.to_sanitized_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args.py#L1315" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Sanitized serialization to use with TensorBoard’s hparams</p></div></div>
<h2 class="relative group"><a id="transformers.Seq2SeqTrainingArguments" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Seq2SeqTrainingArguments
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Seq2SeqTrainingArguments"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Seq2SeqTrainingArguments</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Seq2SeqTrainingArguments" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Seq2SeqTrainingArguments"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/training_args_seq2seq.py#L28" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_dir<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite_output_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_train<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_eval<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_predict<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">evaluation_strategy<span class="opacity-60">: IntervalStrategy = 'no'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prediction_loss_only<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_device_train_batch_size<span class="opacity-60">: int = 8</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_device_eval_batch_size<span class="opacity-60">: int = 8</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_gpu_train_batch_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">per_gpu_eval_batch_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">gradient_accumulation_steps<span class="opacity-60">: int = 1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_accumulation_steps<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">learning_rate<span class="opacity-60">: float = 5e-05</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">weight_decay<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_beta1<span class="opacity-60">: float = 0.9</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_beta2<span class="opacity-60">: float = 0.999</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_epsilon<span class="opacity-60">: float = 1e-08</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_grad_norm<span class="opacity-60">: float = 1.0</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_train_epochs<span class="opacity-60">: float = 3.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_steps<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">lr_scheduler_type<span class="opacity-60">: SchedulerType = 'linear'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">warmup_ratio<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">warmup_steps<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">log_level<span class="opacity-60">: typing.Optional[str] = 'passive'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">log_level_replica<span class="opacity-60">: typing.Optional[str] = 'passive'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">log_on_each_node<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_dir<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_strategy<span class="opacity-60">: IntervalStrategy = 'steps'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_first_step<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_steps<span class="opacity-60">: int = 500</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logging_nan_inf_filter<span class="opacity-60">: str = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_strategy<span class="opacity-60">: IntervalStrategy = 'steps'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_steps<span class="opacity-60">: int = 500</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_total_limit<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_on_each_node<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">no_cuda<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">seed<span class="opacity-60">: int = 42</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data_seed<span class="opacity-60">: int = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bf16<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16_opt_level<span class="opacity-60">: str = 'O1'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">half_precision_backend<span class="opacity-60">: str = 'auto'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bf16_full_eval<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16_full_eval<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tf32<span class="opacity-60">: bool = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">local_rank<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">xpu_backend<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tpu_num_cores<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tpu_metrics_debug<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">debug<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader_drop_last<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_steps<span class="opacity-60">: int = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader_num_workers<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_index<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">run_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">disable_tqdm<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">remove_unused_columns<span class="opacity-60">: typing.Optional[bool] = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_names<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">load_best_model_at_end<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_for_best_model<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">greater_is_better<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ignore_data_skip<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sharded_ddp<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">deepspeed<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_smoothing_factor<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optim<span class="opacity-60">: OptimizerNames = 'adamw_hf'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adafactor<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">group_by_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">length_column_name<span class="opacity-60">: typing.Optional[str] = 'length'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">report_to<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ddp_find_unused_parameters<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ddp_bucket_cap_mb<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataloader_pin_memory<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_memory_metrics<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_legacy_prediction_loop<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">resume_from_checkpoint<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_model_id<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_strategy<span class="opacity-60">: HubStrategy = 'every_save'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_token<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">gradient_checkpointing<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fp16_backend<span class="opacity-60">: str = 'auto'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub_model_id<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub_organization<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub_token<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mp_parameters<span class="opacity-60">: str = ''</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sortish_sampler<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">predict_with_generate<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">generation_max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">generation_num_beams<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.output_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.output_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_dir</strong> (<code>str</code>) —
The output directory where the model predictions and checkpoints will be written.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.overwrite_output_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.overwrite_output_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite_output_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If <code>True</code>, overwrite the content of the output directory. Use this to continue training if <code>output_dir</code>
points to a checkpoint directory.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.do_train" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.do_train"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_train</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run training or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used
by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.do_eval" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.do_eval"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_eval</strong> (<code>bool</code>, <em>optional</em>) —
Whether to run evaluation on the validation set or not. Will be set to <code>True</code> if <code>evaluation_strategy</code> is
different from <code>"no"</code>. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your
training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.do_predict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.do_predict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_predict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to run predictions on the test set or not. This argument is not directly used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s
intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.evaluation_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.evaluation_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>evaluation_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"no"</code>) —
The evaluation strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No evaluation is done during training.</li>
<li><code>"steps"</code>: Evaluation is done (and logged) every <code>eval_steps</code>.</li>
<li><code>"epoch"</code>: Evaluation is done at the end of each epoch.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.prediction_loss_only" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.prediction_loss_only"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>prediction_loss_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When performing evaluation and generating predictions, only returns the loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.per_device_train_batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.per_device_train_batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>per_device_train_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.per_device_eval_batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.per_device_eval_batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>per_device_eval_batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 8) —
The batch size per GPU/TPU core/CPU for evaluation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>gradient_accumulation_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
evaluation, save will be conducted every <code>gradient_accumulation_steps * xxx_step</code> training examples.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.eval_accumulation_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.eval_accumulation_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_accumulation_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
requires more memory).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.learning_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.learning_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>learning_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 5e-5) —
The initial learning rate for <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.weight_decay" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.weight_decay"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a>
optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.adam_beta1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.adam_beta1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_beta1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.adam_beta2" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.adam_beta2"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_beta2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.adam_epsilon" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.adam_epsilon"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-8) —
The epsilon hyperparameter for the <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.AdamW">AdamW</a> optimizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.max_grad_norm" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.max_grad_norm"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_grad_norm</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Maximum gradient norm (for gradient clipping).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.num_train_epochs(float," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.num_train_epochs(float,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_train_epochs(<code>float</code>,</strong> <em>optional</em>, defaults to 3.0) —
Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
the last epoch before stopping training).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.max_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.max_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_steps</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
If set to a positive number, the total number of training steps to perform. Overrides <code>num_train_epochs</code>.
In case of using a finite iterable dataset the training may stop before reaching the set number of steps
when all data is exhausted<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.lr_scheduler_type" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.lr_scheduler_type"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>lr_scheduler_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a>, <em>optional</em>, defaults to <code>"linear"</code>) —
The scheduler type to use. See the documentation of <a href="/docs/transformers/pr_16143/en/main_classes/optimizer_schedules#transformers.SchedulerType">SchedulerType</a> for all possible values.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.warmup_ratio" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.warmup_ratio"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>warmup_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
Ratio of total training steps used for a linear warmup from 0 to <code>learning_rate</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>warmup_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of steps used for a linear warmup from 0 to <code>learning_rate</code>. Overrides any effect of <code>warmup_ratio</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.log_level" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.log_level"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>log_level</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’,
‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and lets the
application set the level.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.log_level_replica" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.log_level_replica"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>log_level_replica</strong> (<code>str</code>, <em>optional</em>, defaults to <code>passive</code>) —
Logger log level to use on replicas. Same choices as <code>log_level</code>”<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.log_on_each_node" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.log_on_each_node"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>log_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
In multinode distributed training, whether to log using <code>log_level</code> once per node, or only on the main
node.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.logging_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.logging_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_dir</strong> (<code>str</code>, <em>optional</em>) —
<a href="https://www.tensorflow.org/tensorboard" rel="nofollow">TensorBoard</a> log directory. Will default to
*output_dir/runs/<strong>CURRENT_DATETIME_HOSTNAME*</strong>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.logging_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.logging_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The logging strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No logging is done during training.</li>
<li><code>"epoch"</code>: Logging is done at the end of each epoch.</li>
<li><code>"steps"</code>: Logging is done every <code>logging_steps</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.logging_first_step" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.logging_first_step"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_first_step</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to log and evaluate the first <code>global_step</code> or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.logging_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.logging_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of update steps between two logs if <code>logging_strategy="steps"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.logging_nan_inf_filter" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.logging_nan_inf_filter"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logging_nan_inf_filter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to filter <code>nan</code> and <code>inf</code> losses for logging. If set to <code>True</code> the loss of every step that is <code>nan</code>
or <code>inf</code> is filtered and the average loss of the current logging window is taken instead.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p><code>logging_nan_inf_filter</code> only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.save_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.save_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"steps"</code>) —
The checkpoint save strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: No save is done during training.</li>
<li><code>"epoch"</code>: Save is done at the end of each epoch.</li>
<li><code>"steps"</code>: Save is done every <code>save_steps</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.save_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.save_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 500) —
Number of updates steps before two checkpoint saves if <code>save_strategy="steps"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.save_total_limit" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.save_total_limit"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_total_limit</strong> (<code>int</code>, <em>optional</em>) —
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
<code>output_dir</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.save_on_each_node" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.save_on_each_node"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_on_each_node</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
the main one.</p>
<p>This should not be activated when the different nodes use the same storage as the files will be saved with
the same names for each node.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.no_cuda" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.no_cuda"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>no_cuda</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to not use CUDA even when it is available or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.seed" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.seed"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>seed</strong> (<code>int</code>, <em>optional</em>, defaults to 42) —
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
<code>model_init</code> function to instantiate the model if it has some randomly initialized parameters.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.data_seed" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.data_seed"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data_seed</strong> (<code>int</code>, <em>optional</em>) —
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as <code>seed</code>. This can be used to ensure reproducibility of data sampling, independent of the model
seed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.bf16" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.bf16"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bf16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
NVIDIA architecture. This is an experimental API and it may change.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.fp16" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.fp16"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.fp16_opt_level" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.fp16_opt_level"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16_opt_level</strong> (<code>str</code>, <em>optional</em>, defaults to ‘O1’) —
For <code>fp16</code> training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on
the <a href="https://nvidia.github.io/apex/amp" rel="nofollow">Apex documentation</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.fp16_backend" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.fp16_backend"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
This argument is deprecated. Use <code>half_precision_backend</code> instead.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.half_precision_backend" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.half_precision_backend"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>half_precision_backend</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"auto"</code>) —
The backend to use for mixed precision training. Must be one of <code>"auto"</code>, <code>"amp"</code> or <code>"apex"</code>. <code>"auto"</code>
will use AMP or APEX depending on the PyTorch version detected, while the other choices will force the
requested backend.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.bf16_full_eval" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.bf16_full_eval"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bf16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values. This is an experimental API and it may change.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.fp16_full_eval" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.fp16_full_eval"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>fp16_full_eval</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.tf32" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.tf32"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tf32</strong> (<code>bool</code>, <em>optional</em>) —
Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental API
and it may change.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.local_rank" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.local_rank"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>local_rank</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Rank of the process during distributed training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.xpu_backend" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.xpu_backend"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>xpu_backend</strong> (<code>str</code>, <em>optional</em>) —
The backend to use for xpu distributed training. Must be one of <code>"mpi"</code> or <code>"ccl"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.tpu_num_cores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.tpu_num_cores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tpu_num_cores</strong> (<code>int</code>, <em>optional</em>) —
When training on TPU, the number of TPU cores (automatically passed by launcher script).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.dataloader_drop_last" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.dataloader_drop_last"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dataloader_drop_last</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.eval_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.eval_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_steps</strong> (<code>int</code>, <em>optional</em>) —
Number of update steps between two evaluations if <code>evaluation_strategy="steps"</code>. Will default to the same
value as <code>logging_steps</code> if not set.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.dataloader_num_workers" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.dataloader_num_workers"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dataloader_num_workers</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
main process.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.past_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.past_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_index</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
Some models like <a href="../model_doc/transformerxl">TransformerXL</a> or <a href="../model_doc/xlnet">XLNet</a> can make use of
the past hidden states for their predictions. If this argument is set to a positive int, the <code>Trainer</code> will
use the corresponding output (usually index 2) as the past state and feed it to the model at the next
training step under the keyword argument <code>mems</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.run_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.run_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>run_name</strong> (<code>str</code>, <em>optional</em>) —
A descriptor for the run. Typically used for <a href="https://www.wandb.com/" rel="nofollow">wandb</a> and
<a href="https://www.mlflow.org/" rel="nofollow">mlflow</a> logging.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.disable_tqdm" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.disable_tqdm"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>disable_tqdm</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not to disable the tqdm progress bars and table of metrics produced by
<code>NotebookTrainingTracker</code> in Jupyter Notebooks. Will default to <code>True</code> if the logging level is
set to warn or lower (default), <code>False</code> otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.remove_unused_columns" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.remove_unused_columns"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>remove_unused_columns</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If using <code>datasets.Dataset</code> datasets, whether or not to automatically remove the columns unused by the
model forward method.</p>
<p>(Note that this behavior is not implemented for <code>TFTrainer</code>yet.)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.label_names" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.label_names"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_names</strong> (<code>List[str]</code>, <em>optional</em>) —
The list of keys in your dictionary of inputs that correspond to the labels.</p>
<p>Will eventually default to <code>["labels"]</code> except if the model used is one of the <code>XxxForQuestionAnswering</code> in
which case it will default to <code>["start_positions", "end_positions"]</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.load_best_model_at_end" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.load_best_model_at_end"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>load_best_model_at_end</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to load the best model found during training at the end of training.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
<p>When set to <code>True</code>, the parameters <code>save_strategy</code> needs to be the same as <code>eval_strategy</code>, and in the case
it is “steps”, <code>save_steps</code> must be a round multiple of <code>eval_steps</code>.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.metric_for_best_model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.metric_for_best_model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metric_for_best_model</strong> (<code>str</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> to specify the metric to use to compare two different
models. Must be the name of a metric returned by the evaluation with or without the prefix <code>"eval_"</code>. Will
default to <code>"loss"</code> if unspecified and <code>load_best_model_at_end=True</code> (to use the evaluation loss).</p>
<p>If you set this value, <code>greater_is_better</code> will default to <code>True</code>. Don’t forget to set it to <code>False</code> if
your metric is better when lower.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.greater_is_better" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.greater_is_better"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>greater_is_better</strong> (<code>bool</code>, <em>optional</em>) —
Use in conjunction with <code>load_best_model_at_end</code> and <code>metric_for_best_model</code> to specify if better models
should have a greater metric or not. Will default to:</p>
<ul>
<li><code>True</code> if <code>metric_for_best_model</code> is set to a value that isn’t <code>"loss"</code> or <code>"eval_loss"</code>.</li>
<li><code>False</code> if <code>metric_for_best_model</code> is not set, or set to <code>"loss"</code> or <code>"eval_loss"</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.ignore_data_skip" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.ignore_data_skip"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ignore_data_skip</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to <code>True</code>, the training will begin faster (as that skipping step
can take a long time) but will not yield the same results as the interrupted training would have.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.sharded_ddp" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.sharded_ddp"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sharded_ddp</strong> (<code>bool</code>, <code>str</code> or list of <code>ShardedDDPOption</code> <em>optional</em>, defaults to <code>False</code>) —
Use Sharded DDP training from <a href="https://github.com/facebookresearch/fairscale" rel="nofollow">FairScale</a> (in distributed
training only). This is an experimental feature.</p>
<p>A list of options along the following:</p>
<ul>
<li><code>"simple"</code>: to use first instance of sharded DDP released by fairscale (<code>ShardedDDP</code>) similar to ZeRO-2.</li>
<li><code>"zero_dp_2"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-2 mode (with <code>reshard_after_forward=False</code>).</li>
<li><code>"zero_dp_3"</code>: to use the second instance of sharded DPP released by fairscale (<code>FullyShardedDDP</code>) in
Zero-3 mode (with <code>reshard_after_forward=True</code>).</li>
<li><code>"offload"</code>: to add ZeRO-offload (only compatible with <code>"zero_dp_2"</code> and <code>"zero_dp_3"</code>).</li>
</ul>
<p>If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty
list for <code>False</code> and <code>["simple"]</code> for <code>True</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.deepspeed" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.deepspeed"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>deepspeed</strong> (<code>str</code> or <code>dict</code>, <em>optional</em>) —
Use <a href="https://github.com/microsoft/deepspeed" rel="nofollow">Deepspeed</a>. This is an experimental feature and its API may
evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
<code>ds_config.json</code>) or an already loaded json file as a <code>dict</code>”<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.label_smoothing_factor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.label_smoothing_factor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_smoothing_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) —
The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
labels are changed from 0s and 1s to <code>label_smoothing_factor/num_labels</code> and <code>1 - label_smoothing_factor + label_smoothing_factor/num_labels</code> respectively.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.debug" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.debug"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>debug</strong> (<code>str</code> or list of <code>DebugOption</code> <em>optional</em>, defaults to <code>""</code>) —
Enable one or more debug features. This is an experimental feature.</p>
<p>Possible options are:</p>
<ul>
<li><code>"underflow_overflow"</code>: detects overflow in model’s input/outputs and reports the last frames that led to
the event</li>
<li><code>"tpu_metrics_debug"</code>: print debug metrics on TPU</li>
</ul>
<p>The options should be separated by whitespaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.optim" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.optim"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optim</strong> (<code>str</code> or <code>training_args.OptimizerNames</code> <em>optional</em>, defaults to <code>"adamw_hf"</code>) —
The optimizer to use: adamw_hf, adamw_torch, adamw_apex_fused, or adafactor.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.adafactor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.adafactor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adafactor</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
This argument is deprecated. Use <code>--optim adafactor</code> instead.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.group_by_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.group_by_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>group_by_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to group together samples of roughly the same length in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.length_column_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.length_column_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>length_column_name</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"length"</code>) —
Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
than computing them on train startup. Ignored unless <code>group_by_length</code> is <code>True</code> and the dataset is an
instance of <code>Dataset</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.report_to" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.report_to"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>report_to</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>, defaults to <code>"all"</code>) —
The list of integrations to report the results and logs to. Supported platforms are <code>"azure_ml"</code>,
<code>"comet_ml"</code>, <code>"mlflow"</code>, <code>"tensorboard"</code> and <code>"wandb"</code>. Use <code>"all"</code> to report to all integrations
installed, <code>"none"</code> for no integrations.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.ddp_find_unused_parameters" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.ddp_find_unused_parameters"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ddp_find_unused_parameters</strong> (<code>bool</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>find_unused_parameters</code> passed to
<code>DistributedDataParallel</code>. Will default to <code>False</code> if gradient checkpointing is used, <code>True</code> otherwise.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.ddp_bucket_cap_mb" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.ddp_bucket_cap_mb"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ddp_bucket_cap_mb</strong> (<code>int</code>, <em>optional</em>) —
When using distributed training, the value of the flag <code>bucket_cap_mb</code> passed to <code>DistributedDataParallel</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.dataloader_pin_memory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.dataloader_pin_memory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dataloader_pin_memory</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether you want to pin memory in data loaders or not. Will default to <code>True</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.skip_memory_metrics" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.skip_memory_metrics"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_memory_metrics</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
down the training and evaluation speed.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push the model to the Hub every time the model is saved. If this is activated,
<code>output_dir</code> will begin a git directory synced with the the repo (determined by <code>hub_model_id</code>) and the
content will be pushed each time a save is triggered (depending on your <code>save_strategy</code>). Calling
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> will also trigger a push.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>If <code>output_dir</code> exists, it needs to be a local clone of the repository to which the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will be
pushed.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.resume_from_checkpoint" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.resume_from_checkpoint"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>resume_from_checkpoint</strong> (<code>str</code>, <em>optional</em>) —
The path to a folder with a valid checkpoint for your model. This argument is not directly used by
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, it’s intended to be used by your training/evaluation scripts instead. See the <a href="https://github.com/huggingface/transformers/tree/master/examples" rel="nofollow">example
scripts</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.hub_model_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.hub_model_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_model_id</strong> (<code>str</code>, <em>optional</em>) —
The name of the repository to keep in sync with the local <em>output_dir</em>. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance <code>"user_name/model"</code>, which allows you to push to an organization you are a member of with
<code>"organization_name/model"</code>. Will default to <code>user_name/output_dir_name</code> with <em>output_dir_name</em> being the
name of <code>output_dir</code>.</p>
<p>Will default to to the name of <code>output_dir</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.hub_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.hub_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_strategy</strong> (<code>str</code> or <code>HubStrategy</code> <em>optional</em>, defaults to <code>"every_save"</code>) —
Defines the scope of what is pushed to the Hub and when. Possible values are:</p>
<ul>
<li><code>"end"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and a
draft of a model card when the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.save_model">save_model()</a> method is called.</li>
<li><code>"every_save"</code>: push the model, its configuration, the tokenizer (if passed along to the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>) and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.</li>
<li><code>"checkpoint"</code>: like <code>"every_save"</code> but the latest checkpoint is also pushed in a subfolder named
last-checkpoint, allowing you to resume training easily with
<code>trainer.train(resume_from_checkpoint="last-checkpoint")</code>.</li>
<li><code>"all_checkpoints"</code>: like <code>"checkpoint"</code> but all checkpoints are pushed like they appear in the output
folder (so you will get one checkpoint folder per folder in your final repository)</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.hub_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.hub_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_token</strong> (<code>str</code>, <em>optional</em>) —
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
<code>huggingface-cli login</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Seq2SeqTrainingArguments.gradient_checkpointing" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Seq2SeqTrainingArguments.gradient_checkpointing"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>gradient_checkpointing</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
If True, use gradient checkpointing to save memory at the expense of slower backward pass.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>TrainingArguments is the subset of the arguments we use in our example scripts <strong>which relate to the training loop
itself</strong>.</p>
<p>Using <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.HfArgumentParser">HfArgumentParser</a> we can turn this class into
<a href="https://docs.python.org/3/library/argparse#module-argparse" rel="nofollow">argparse</a> arguments that can be specified on the
command line.</p>
<p>sortish_sampler (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to use a <em>sortish sampler</em> or not. Only possible if the underlying datasets are <em>Seq2SeqDataset</em> for
now but will become generally available in the near future.</p>
<p>It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for
the training set.
predict_with_generate (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
generation_max_length (<code>int</code>, <em>optional</em>):
The <code>max_length</code> to use on each evaluation loop when <code>predict_with_generate=True</code>. Will default to the
<code>max_length</code> value of the model configuration.
generation_num_beams (<code>int</code>, <em>optional</em>):
The <code>num_beams</code> to use on each evaluation loop when <code>predict_with_generate=True</code>. Will default to the
<code>num_beams</code> value of the model configuration.</p></div>
<h2 class="relative group"><a id="checkpoints" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#checkpoints"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Checkpoints
</span></h2>
<p>By default, <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will save all checkpoints in the <code>output_dir</code> you set in the
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> you are using. Those will go in subfolder named <code>checkpoint-xxx</code> with xxx
being the step at which the training was at.</p>
<p>Resuming training from a checkpoint can be done when calling <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">Trainer.train()</a> with either:</p>
<ul><li><code>resume_from_checkpoint=True</code> which will resume training from the latest checkpoint</li>
<li><code>resume_from_checkpoint=checkpoint_dir</code> which will resume training from the specific checkpoint in the directory
passed.</li></ul>
<p>In addition, you can easily save your checkpoints on the Model Hub when using <code>push_to_hub=True</code>. By default, all
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
the <code>hub-strategy</code> value of your <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> to either:</p>
<ul><li><code>"checkpoint"</code>: the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
resume training easily with <code>trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")</code>.</li>
<li><code>"all_checkpoints"</code>: all checkpoints are pushed like they appear in the output folder (so you will get one
checkpoint folder per folder in your final repository)</li></ul>
<h2 class="relative group"><a id="logging" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#logging"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Logging
</span></h2>
<p>By default <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will use <code>logging.INFO</code> for the main process and <code>logging.WARNING</code> for the replicas if any.</p>
<p>These defaults can be overridden to use any of the 5 <code>logging</code> levels with <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s
arguments:</p>
<ul><li><code>log_level</code> - for the main process</li>
<li><code>log_level_replica</code> - for the replicas</li></ul>
<p>Further, if <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>’s <code>log_on_each_node</code> is set to <code>False</code> only the main node will
use the log level settings for its main process, all other nodes will use the log level settings for replicas.</p>
<p>Note that <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> is going to set <code>transformers</code>’s log level separately for each node in its
<code>Trainer.__init__()</code> So you may want to set this sooner (see the next example) if you tap into other
<code>transformers</code> functionality before creating the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> object.</p>
<p>Here is an example of how this can be used in an application:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->[...]
logger = logging.getLogger(__name__)
<span class="hljs-comment"># Setup logging</span>
logging.basicConfig(
<span class="hljs-built_in">format</span>=<span class="hljs-string">"%(asctime)s - %(levelname)s - %(name)s - %(message)s"</span>,
datefmt=<span class="hljs-string">"%m/%d/%Y %H:%M:%S"</span>,
handlers=[logging.StreamHandler(sys.stdout)],
)
<span class="hljs-comment"># set the main code and the modules it uses to the same log-level according to the node</span>
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
trainer = Trainer(...)<!-- HTML_TAG_END --></pre></div>
<p>And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
warnings you could run it as:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->my_app.py ... --log_level warning --log_level_replica error<!-- HTML_TAG_END --></pre></div>
<p>In the multi-node environment if you also don’t want the logs to repeat for each node’s main process, you will want to
change the above to:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->my_app.py ... --log_level warning --log_level_replica error --log_on_each_node 0<!-- HTML_TAG_END --></pre></div>
<p>and then only the main process of the first node will log at the “warning” level, and all other processes on the main
node and all processes on other nodes will log at the “error” level.</p>
<p>If you need your application to be as quiet as possible you could do:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->my_app.py ... --log_level error --log_level_replica error --log_on_each_node 0<!-- HTML_TAG_END --></pre></div>
<p>(add <code>--log_on_each_node 0</code> if on multi-node environment)</p>
<h2 class="relative group"><a id="randomness" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#randomness"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Randomness
</span></h2>
<p>When resuming from a checkpoint generated by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> all efforts are made to restore the
<em>python</em>, <em>numpy</em> and <em>pytorch</em> RNG states to the same states as they were at the moment of saving that checkpoint,
which should make the “stop and resume” style of training as close as possible to non-stop training.</p>
<p>However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
determinism please refer to <a href="https://pytorch.org/docs/stable/notes/randomness" rel="nofollow">Controlling sources of randomness</a>. As explained in the document, that some of those settings
that make things deterministic (.e.g., <code>torch.backends.cudnn.deterministic</code>) may slow things down, therefore this
can’t be done by default, but you can enable those yourself if needed.</p>
<h2 class="relative group"><a id="specific-gpus-selection" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#specific-gpus-selection"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Specific GPUs Selection
</span></h2>
<p>Let’s discuss how you can tell your program which GPUs are to be used and in what order.</p>
<p>When using <a href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html" rel="nofollow"><code>DistributedDataParallel</code></a> to use only a subset of your GPUs, you simply specify the number of GPUs to use. For example, if you have 4 GPUs, but you wish to use the first 2 you can do:</p>
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<pre><!-- HTML_TAG_START -->python -m torch.distributed.launch --nproc_per_node=2 trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
<p>if you have either <a href="https://github.com/huggingface/accelerate" rel="nofollow"><code>accelerate</code></a> or <a href="https://github.com/microsoft/DeepSpeed" rel="nofollow"><code>deepspeed</code></a> installed you can also accomplish the same by using one of:</p>
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<pre><!-- HTML_TAG_START -->accelerate launch --num_processes 2 trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
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<pre><!-- HTML_TAG_START -->deepspeed --num_gpus 2 trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
<p>You don’t need to use the Accelerate or <a href="Deepspeed">the Deepspeed integration</a> features to use these launchers.</p>
<p>Until now you were able to tell the program how many GPUs to use. Now let’s discuss how to select specific GPUs and control their order.</p>
<p>The following environment variables help you control which GPUs to use and their order.</p>
<p><strong><code>CUDA_VISIBLE_DEVICES</code></strong></p>
<p>If you have multiple GPUs and you’d like to use only 1 or a few of those GPUs, set the environment variable <code>CUDA_VISIBLE_DEVICES</code> to a list of the GPUs to be used.</p>
<p>For example, let’s say you have 4 GPUs: 0, 1, 2 and 3. To run only on the physical GPUs 0 and 2, you can do:</p>
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<pre><!-- HTML_TAG_START -->CUDA_VISIBLE_DEVICES=0,2 python -m torch.distributed.launch trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
<p>So now pytorch will see only 2 GPUs, where your physical GPUs 0 and 2 are mapped to <code>cuda:0</code> and <code>cuda:1</code> correspondingly.</p>
<p>You can even change their order:</p>
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<pre><!-- HTML_TAG_START -->CUDA_VISIBLE_DEVICES=2,0 python -m torch.distributed.launch trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
<p>Here your physical GPUs 0 and 2 are mapped to <code>cuda:1</code> and <code>cuda:0</code> correspondingly.</p>
<p>The above examples were all for <code>DistributedDataParallel</code> use pattern, but the same method works for <a href="https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html" rel="nofollow"><code>DataParallel</code></a> as well:</p>
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<pre><!-- HTML_TAG_START -->CUDA_VISIBLE_DEVICES=2,0 python trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
<p>To emulate an environment without GPUs simply set this environment variable to an empty value like so:</p>
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<pre><!-- HTML_TAG_START -->CUDA_VISIBLE_DEVICES= python trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
<p>As with any environment variable you can, of course, export those instead of adding these to the command line, as in:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> CUDA_VISIBLE_DEVICES=0,2
python -m torch.distributed.launch trainer-program.py ...<!-- HTML_TAG_END --></pre></div>
<p>but this approach can be confusing since you may forget you set up the environment variable earlier and not understand why the wrong GPUs are used. Therefore, it’s a common practice to set the environment variable just for a specific run on the same command line as it’s shown in most examples of this section.</p>
<p><strong><code>CUDA_DEVICE_ORDER</code></strong></p>
<p>There is an additional environment variable <code>CUDA_DEVICE_ORDER</code> that controls how the physical devices are ordered. The two choices are:</p>
<ol><li>ordered by PCIe bus IDs (matches <code>nvidia-smi</code>’s order) - this is the default.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> CUDA_DEVICE_ORDER=PCI_BUS_ID<!-- HTML_TAG_END --></pre></div>
<ol start="2"><li>ordered by GPU compute capabilities</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> CUDA_DEVICE_ORDER=FASTEST_FIRST<!-- HTML_TAG_END --></pre></div>
<p>Most of the time you don’t need to care about this environment variable, but it’s very helpful if you have a lopsided setup where you have an old and a new GPUs physically inserted in such a way so that the slow older card appears to be first. One way to fix that is to swap the cards. But if you can’t swap the cards (e.g., if the cooling of the devices gets impacted) then setting <code>CUDA_DEVICE_ORDER=FASTEST_FIRST</code> will always put the newer faster card first. It’ll be somewhat confusing though since <code>nvidia-smi</code> will still report them in the PCIe order.</p>
<p>The other solution to swapping the order is to use:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> CUDA_VISIBLE_DEVICES=1,0<!-- HTML_TAG_END --></pre></div>
<p>In this example we are working with just 2 GPUs, but of course the same would apply to as many GPUs as your computer has.</p>
<p>Also if you do set this environment variable it’s the best to set it in your <code>~/.bashrc</code> file or some other startup config file and forget about it.</p>
<h2 class="relative group"><a id="trainer-integrations" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainer-integrations"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainer Integrations
</span></h2>
<p>The <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> has been extended to support libraries that may dramatically improve your training
time and fit much bigger models.</p>
<p>Currently it supports third party solutions, <a href="https://github.com/microsoft/DeepSpeed" rel="nofollow">DeepSpeed</a> and <a href="https://github.com/facebookresearch/fairscale/" rel="nofollow">FairScale</a>, which implement parts of the paper <a href="https://arxiv.org/abs/1910.02054" rel="nofollow">ZeRO: Memory Optimizations
Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He</a>.</p>
<p>This provided support is new and experimental as of this writing.</p>
<a id="zero-install-notes"></a>
<h3 class="relative group"><a id="cuda-extension-installation-notes" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#cuda-extension-installation-notes"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>CUDA Extension Installation Notes
</span></h3>
<p>As of this writing, both FairScale and Deepspeed require compilation of CUDA C++ code, before they can be used.</p>
<p>While all installation issues should be dealt with through the corresponding GitHub Issues of <a href="https://github.com/facebookresearch/fairscale/issues" rel="nofollow">FairScale</a> and <a href="https://github.com/microsoft/DeepSpeed/issues" rel="nofollow">Deepspeed</a>, there are a few common issues that one may encounter while building
any PyTorch extension that needs to build CUDA extensions.</p>
<p>Therefore, if you encounter a CUDA-related build issue while doing one of the following or both:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install fairscale
pip install deepspeed<!-- HTML_TAG_END --></pre></div>
<p>please, read the following notes first.</p>
<p>In these notes we give examples for what to do when <code>pytorch</code> has been built with CUDA <code>10.2</code>. If your situation is
different remember to adjust the version number to the one you are after.</p>
<h4 class="relative group"><a id="possible-problem-1" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#possible-problem-1"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Possible problem #1
</span></h4>
<p>While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA
installed system-wide.</p>
<p>For example, if you installed <code>pytorch</code> with <code>cudatoolkit==10.2</code> in the Python environment, you also need to have
CUDA <code>10.2</code> installed system-wide.</p>
<p>The exact location may vary from system to system, but <code>/usr/local/cuda-10.2</code> is the most common location on many
Unix systems. When CUDA is correctly set up and added to the <code>PATH</code> environment variable, one can find the
installation location by doing:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">which</span> nvcc<!-- HTML_TAG_END --></pre></div>
<p>If you don’t have CUDA installed system-wide, install it first. You will find the instructions by using your favorite
search engine. For example, if you’re on Ubuntu you may want to search for: <a href="https://www.google.com/search?q=ubuntu+cuda+10.2+install" rel="nofollow">ubuntu cuda 10.2 install</a>.</p>
<h4 class="relative group"><a id="possible-problem-2" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#possible-problem-2"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Possible problem #2
</span></h4>
<p>Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you
may have:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->/usr/local/cuda-10.2
/usr/local/cuda-11.0<!-- HTML_TAG_END --></pre></div>
<p>Now, in this situation you need to make sure that your <code>PATH</code> and <code>LD_LIBRARY_PATH</code> environment variables contain
the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the
last version was installed. If you encounter the problem, where the package build fails because it can’t find the right
CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned
environment variables.</p>
<p>First, you may look at their contents:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">echo</span> <span class="hljs-variable">$PATH</span>
<span class="hljs-built_in">echo</span> <span class="hljs-variable">$LD_LIBRARY_PATH</span><!-- HTML_TAG_END --></pre></div>
<p>so you get an idea of what is inside.</p>
<p>It’s possible that <code>LD_LIBRARY_PATH</code> is empty.</p>
<p><code>PATH</code> lists the locations of where executables can be found and <code>LD_LIBRARY_PATH</code> is for where shared libraries
are to looked for. In both cases, earlier entries have priority over the later ones. <code>:</code> is used to separate multiple
entries.</p>
<p>Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by
doing:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">export</span> PATH=/usr/local/cuda-10.2/bin:<span class="hljs-variable">$PATH</span>
<span class="hljs-built_in">export</span> LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:<span class="hljs-variable">$LD_LIBRARY_PATH</span><!-- HTML_TAG_END --></pre></div>
<p>Note that we aren’t overwriting the existing values, but prepending instead.</p>
<p>Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do
exist. <code>lib64</code> sub-directory is where the various CUDA <code>.so</code> objects, like <code>libcudart.so</code> reside, it’s unlikely
that your system will have it named differently, but if it is adjust it to reflect your reality.</p>
<h4 class="relative group"><a id="possible-problem-3" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#possible-problem-3"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Possible problem #3
</span></h4>
<p>Some older CUDA versions may refuse to build with newer compilers. For example, you my have <code>gcc-9</code> but it wants
<code>gcc-7</code>.</p>
<p>There are various ways to go about it.</p>
<p>If you can install the latest CUDA toolkit it typically should support the newer compiler.</p>
<p>Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may
already have it but it’s not the default one, so the build system can’t see it. If you have <code>gcc-7</code> installed but the
build system complains it can’t find it, the following might do the trick:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->sudo <span class="hljs-built_in">ln</span> -s /usr/bin/gcc-7 /usr/local/cuda-10.2/bin/gcc
sudo <span class="hljs-built_in">ln</span> -s /usr/bin/g++-7 /usr/local/cuda-10.2/bin/g++<!-- HTML_TAG_END --></pre></div>
<p>Here, we are making a symlink to <code>gcc-7</code> from <code>/usr/local/cuda-10.2/bin/gcc</code> and since
<code>/usr/local/cuda-10.2/bin/</code> should be in the <code>PATH</code> environment variable (see the previous problem’s solution), it
should find <code>gcc-7</code> (and <code>g++7</code>) and then the build will succeed.</p>
<p>As always make sure to edit the paths in the example to match your situation.</p>
<h3 class="relative group"><a id="fairscale" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#fairscale"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FairScale
</span></h3>
<p>By integrating <a href="https://github.com/facebookresearch/fairscale/" rel="nofollow">FairScale</a> the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>
provides support for the following features from <a href="https://arxiv.org/abs/1910.02054" rel="nofollow">the ZeRO paper</a>:</p>
<ol><li>Optimizer State Sharding</li>
<li>Gradient Sharding</li>
<li>Model Parameters Sharding (new and very experimental)</li>
<li>CPU offload (new and very experimental)</li></ol>
<p>You will need at least two GPUs to use this feature.</p>
<p><strong>Installation</strong>:</p>
<p>Install the library via pypi:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install fairscale<!-- HTML_TAG_END --></pre></div>
<p>or via <code>transformers</code>’ <code>extras</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install transformers[fairscale]<!-- HTML_TAG_END --></pre></div>
<p>(available starting from <code>transformers==4.6.0</code>) or find more details on <a href="https://github.com/facebookresearch/fairscale/#installation" rel="nofollow">the FairScale’s GitHub page</a>.</p>
<p>If you’re still struggling with the build, first make sure to read <a href="#zero-install-notes">CUDA Extension Installation Notes</a>.</p>
<p>If it’s still not resolved the build issue, here are a few more ideas.</p>
<p><code>fairscale</code> seems to have an issue with the recently introduced by pip build isolation feature. If you have a problem
with it, you may want to try one of:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install fairscale --no-build-isolation .<!-- HTML_TAG_END --></pre></div>
<p>or:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->git <span class="hljs-built_in">clone</span> https://github.com/facebookresearch/fairscale/
<span class="hljs-built_in">cd</span> fairscale
<span class="hljs-built_in">rm</span> -r dist build
python setup.py bdist_wheel
pip uninstall -y fairscale
pip install dist/fairscale-*.whl<!-- HTML_TAG_END --></pre></div>
<p><code>fairscale</code> also has issues with building against pytorch-nightly, so if you use it you may have to try one of:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip uninstall -y fairscale; pip install fairscale --pre \
-f https://download.pytorch.org/whl/nightly/cu110/torch_nightly \
--no-cache --no-build-isolation<!-- HTML_TAG_END --></pre></div>
<p>or:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install -v --disable-pip-version-check . \
-f https://download.pytorch.org/whl/nightly/cu110/torch_nightly --pre<!-- HTML_TAG_END --></pre></div>
<p>Of course, adjust the urls to match the cuda version you use.</p>
<p>If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of
<a href="https://github.com/facebookresearch/fairscale/issues" rel="nofollow">FairScale</a>.</p>
<p><strong>Usage</strong>:</p>
<p>To use the first version of Sharded data-parallelism, add <code>--sharded_ddp simple</code> to the command line arguments, and
make sure you have added the distributed launcher <code>-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE</code> if you haven’t been using it already.</p>
<p>For example here is how you could use it for <code>run_translation.py</code> with 2 GPUs:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
--model_name_or_path t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config <span class="hljs-string">"ro-en"</span> \
--source_lang en --target_lang ro \
--fp16 --sharded_ddp simple<!-- HTML_TAG_END --></pre></div>
<p>Notes:</p>
<ul><li>This feature requires distributed training (so multiple GPUs).</li>
<li>It is not implemented for TPUs.</li>
<li>It works with <code>--fp16</code> too, to make things even faster.</li>
<li>One of the main benefits of enabling <code>--sharded_ddp simple</code> is that it uses a lot less GPU memory, so you should be
able to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
significantly shorter training time.</li></ul>
<ol start="3"><li>To use the second version of Sharded data-parallelism, add <code>--sharded_ddp zero_dp_2</code> or <code>--sharded_ddp zero_dp_3</code> to the command line arguments, and make sure you have added the distributed launcher <code>-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE</code> if you haven’t been using it already.</li></ol>
<p>For example here is how you could use it for <code>run_translation.py</code> with 2 GPUs:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
--model_name_or_path t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config <span class="hljs-string">"ro-en"</span> \
--source_lang en --target_lang ro \
--fp16 --sharded_ddp zero_dp_2<!-- HTML_TAG_END --></pre></div>
<p><code>zero_dp_2</code> is an optimized version of the simple wrapper, while <code>zero_dp_3</code> fully shards model weights,
gradients and optimizer states.</p>
<p>Both are compatible with adding <code>cpu_offload</code> to enable ZeRO-offload (activate it like this: <code>--sharded_ddp "zero_dp_2 cpu_offload"</code>).</p>
<p>Notes:</p>
<ul><li>This feature requires distributed training (so multiple GPUs).</li>
<li>It is not implemented for TPUs.</li>
<li>It works with <code>--fp16</code> too, to make things even faster.</li>
<li>The <code>cpu_offload</code> additional option requires <code>--fp16</code>.</li>
<li>This is an area of active development, so make sure you have a source install of fairscale to use this feature as
some bugs you encounter may have been fixed there already.</li></ul>
<p>Known caveats:</p>
<ul><li>This feature is incompatible with <code>--predict_with_generate</code> in the <em>run_translation.py</em> script.</li>
<li>Using <code>--sharded_ddp zero_dp_3</code> requires wrapping each layer of the model in the special container
<code>FullyShardedDataParallelism</code> of fairscale. It should be used with the option <code>auto_wrap</code> if you are not
doing this yourself: <code>--sharded_ddp "zero_dp_3 auto_wrap"</code>.</li></ul>
<p>Sections that were moved:</p>
<p>[ <a href="./deepspeed#deepspeed-trainer-integration">DeepSpeed</a><a id="deepspeed"></a>
| <a href="./deepspeed#deepspeed-installation">Installation</a><a id="installation"></a>
| <a href="./deepspeed#deepspeed-multi-gpu">Deployment with multiple GPUs</a><a id="deployment-with-multiple-gpus"></a>
| <a href="./deepspeed#deepspeed-one-gpu">Deployment with one GPU</a><a id="deployment-with-one-gpu"></a>
| <a href="./deepspeed#deepspeed-notebook">Deployment in Notebooks</a><a id="deployment-in-notebooks"></a>
| <a href="./deepspeed#deepspeed-config">Configuration</a><a id="configuration"></a>
| <a href="./deepspeed#deepspeed-config-passing">Passing Configuration</a><a id="passing-configuration"></a>
| <a href="./deepspeed#deepspeed-config-shared">Shared Configuration</a><a id="shared-configuration"></a>
| <a href="./deepspeed#deepspeed-zero">ZeRO</a><a id="zero"></a>
| <a href="./deepspeed#deepspeed-zero2-config">ZeRO-2 Config</a><a id="zero-2-config"></a>
| <a href="./deepspeed#deepspeed-zero3-config">ZeRO-3 Config</a><a id="zero-3-config"></a>
| <a href="./deepspeed#deepspeed-nvme">NVMe Support</a><a id="nvme-support"></a>
| <a href="./deepspeed#deepspeed-zero2-zero3-performance">ZeRO-2 vs ZeRO-3 Performance</a><a id="zero-2-vs-zero-3-performance"></a>
| <a href="./deepspeed#deepspeed-zero2-example">ZeRO-2 Example</a><a id="zero-2-example"></a>
| <a href="./deepspeed#deepspeed-zero3-example">ZeRO-3 Example</a><a id="zero-3-example"></a>
| <a href="./deepspeed#deepspeed-optimizer">Optimizer</a><a id="optimizer"></a>
| <a href="./deepspeed#deepspeed-scheduler">Scheduler</a><a id="scheduler"></a>
| <a href="./deepspeed#deepspeed-fp32">fp32 Precision</a><a id="fp32-precision"></a>
| <a href="./deepspeed#deepspeed-amp">Automatic Mixed Precision</a><a id="automatic-mixed-precision"></a>
| <a href="./deepspeed#deepspeed-bs">Batch Size</a><a id="batch-size"></a>
| <a href="./deepspeed#deepspeed-grad-acc">Gradient Accumulation</a><a id="gradient-accumulation"></a>
| <a href="./deepspeed#deepspeed-grad-clip">Gradient Clipping</a><a id="gradient-clipping"></a>
| <a href="./deepspeed#deepspeed-weight-extraction">Getting The Model Weights Out</a><a id="getting-the-model-weights-out"></a>
]</p>
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<h1 class="relative group"><a id="tokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#tokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Tokenizer
</span></h1>
<p>A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
of the tokenizers are available in two flavors: a full python implementation and a “Fast” implementation based on the
Rust library <a href="https://github.com/huggingface/tokenizers" rel="nofollow">🤗 Tokenizers</a>. The “Fast” implementations allows:</p>
<ol><li>a significant speed-up in particular when doing batched tokenization and</li>
<li>additional methods to map between the original string (character and words) and the token space (e.g. getting the
index of the token comprising a given character or the span of characters corresponding to a given token). Currently
no “Fast” implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLM-RoBERTa
and XLNet models).</li></ol>
<p>The base classes <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> and <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>
implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
“Fast” tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
(downloaded from HuggingFace’s AWS S3 repository). They both rely on
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a> that contains the common methods, and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.SpecialTokensMixin">SpecialTokensMixin</a>.</p>
<p><a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> and <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> thus implement the main
methods for using all the tokenizers:</p>
<ul><li>Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and
encoding/decoding (i.e., tokenizing and converting to integers).</li>
<li>Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece…).</li>
<li>Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
tokenizer for easy access and making sure they are not split during tokenization.</li></ul>
<p><a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a> holds the output of the
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a>’s encoding methods (<code>__call__</code>,
<code>encode_plus</code> and <code>batch_encode_plus</code>) and is derived from a Python dictionary. When the tokenizer is a pure python
tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
these methods (<code>input_ids</code>, <code>attention_mask</code>…). When the tokenizer is a “Fast” tokenizer (i.e., backed by
HuggingFace <a href="https://github.com/huggingface/tokenizers" rel="nofollow">tokenizers library</a>), this class provides in addition
several advanced alignment methods which can be used to map between the original string (character and words) and the
token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
to a given token).</p>
<h2 class="relative group"><a id="transformers.PreTrainedTokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>PreTrainedTokenizer
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizer"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PreTrainedTokenizer</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils.py#L333" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.model_max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.model_max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_max_length</strong> (<code>int</code>, <em>optional</em>) —
The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
loaded with <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a>, this will be set to the
value stored for the associated model in <code>max_model_input_sizes</code> (see above). If no value is provided, will
default to VERY_LARGE_INTEGER (<code>int(1e30)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.padding_side" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.padding_side"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding_side</strong> (<code>str</code>, <em>optional</em>) —
The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.truncation_side" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.truncation_side"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation_side</strong> (<code>str</code>, <em>optional</em>) —
The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.model_input_names" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.model_input_names"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_input_names</strong> (<code>List[string]</code>, <em>optional</em>) —
The list of inputs accepted by the forward pass of the model (like <code>"token_type_ids"</code> or
<code>"attention_mask"</code>). Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.bos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.bos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the beginning of a sentence. Will be associated to <code>self.bos_token</code> and
<code>self.bos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.eos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.eos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the end of a sentence. Will be associated to <code>self.eos_token</code> and
<code>self.eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.unk_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.unk_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing an out-of-vocabulary token. Will be associated to <code>self.unk_token</code> and
<code>self.unk_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.sep_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.sep_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sep_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token separating two different sentences in the same input (used by BERT for instance). Will be
associated to <code>self.sep_token</code> and <code>self.sep_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.pad_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.pad_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation. Will be associated to <code>self.pad_token</code> and <code>self.pad_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.cls_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.cls_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the class of the input (used by BERT for instance). Will be associated to
<code>self.cls_token</code> and <code>self.cls_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.mask_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.mask_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT). Will be associated to <code>self.mask_token</code> and <code>self.mask_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.additional_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.additional_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>additional_special_tokens</strong> (tuple or list of <code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A tuple or a list of additional special tokens. Add them here to ensure they won’t be split by the
tokenization process. Will be associated to <code>self.additional_special_tokens</code> and
<code>self.additional_special_tokens_ids</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for all slow tokenizers.</p>
<p>Inherits from <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a>.</p>
<p>Handle all the shared methods for tokenization and special tokens as well as methods downloading/caching/loading
pretrained tokenizers as well as adding tokens to the vocabulary.</p>
<p>This class also contain the added tokens in a unified way on top of all tokenizers so we don’t have to handle the
specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece…).</p>
<p>Class attributes (overridden by derived classes)</p>
<ul><li><strong>vocab_files_names</strong> (<code>Dict[str, str]</code>) — A dictionary with, as keys, the <code>__init__</code> keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).</li>
<li><strong>pretrained_vocab_files_map</strong> (<code>Dict[str, Dict[str, str]]</code>) — A dictionary of dictionaries, with the
high-level keys being the <code>__init__</code> keyword name of each vocabulary file required by the model, the
low-level being the <code>short-cut-names</code> of the pretrained models with, as associated values, the <code>url</code> to the
associated pretrained vocabulary file.</li>
<li><strong>max_model_input_sizes</strong> (<code>Dict[str, Optional[int]]</code>) — A dictionary with, as keys, the <code>short-cut-names</code>
of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model,
or <code>None</code> if the model has no maximum input size.</li>
<li><strong>pretrained_init_configuration</strong> (<code>Dict[str, Dict[str, Any]]</code>) — A dictionary with, as keys, the
<code>short-cut-names</code> of the pretrained models, and as associated values, a dictionary of specific arguments to
pass to the <code>__init__</code> method of the tokenizer class for this pretrained model when loading the tokenizer
with the <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a> method.</li>
<li><strong>model_input_names</strong> (<code>List[str]</code>) — A list of inputs expected in the forward pass of the model.</li>
<li><strong>padding_side</strong> (<code>str</code>) — The default value for the side on which the model should have padding applied.
Should be <code>'right'</code> or <code>'left'</code>.</li>
<li><strong>truncation_side</strong> (<code>str</code>) — The default value for the side on which the model should have truncation
applied. Should be <code>'right'</code> or <code>'left'</code>.</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2379" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[typing.List[str]]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_pair<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_split_into_words<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_token_type_ids<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_overflowing_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_special_tokens_mask<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_offsets_mapping<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.text_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.text_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_pair</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_token_type_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_token_type_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_overflowing_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_overflowing_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_special_tokens_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_special_tokens_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_offsets_mapping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_offsets_mapping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.verbose" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.verbose"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> — List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> — List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>“token_type_ids”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>“attention_mask”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> — List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> — Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> — The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.batch_decode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>batch_decode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.batch_decode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.batch_decode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3250" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">clean_up_tokenization_spaces<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
List of tokenized input ids. Can be obtained using the <code>__call__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to clean up the tokenization spaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific decode method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.batch_decode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The list of decoded sentences.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Convert a list of lists of token ids into a list of strings by calling decode.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.decode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>decode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.decode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.decode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3283" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids<span class="opacity-60">: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">clean_up_tokenization_spaces<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.token_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.token_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids</strong> (<code>Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
List of tokenized input ids. Can be obtained using the <code>__call__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to clean up the tokenization spaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific decode method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.decode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The decoded sentence.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.</p>
<p>Similar to doing <code>self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.encode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>encode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.encode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.encode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2189" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_pair<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[int]</code>, <code>torch.Tensor</code>, <code>tf.Tensor</code> or <code>np.ndarray</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>) —
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
<code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.text_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.text_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_pair</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>, <em>optional</em>) —
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the <code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.encode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[int]</code>, <code>torch.Tensor</code>, <code>tf.Tensor</code> or <code>np.ndarray</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The tokenized ids of the text.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.</p>
<p>Same as doing <code>self.convert_tokens_to_ids(self.tokenize(text))</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your tokenizer in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add tokenizer"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your tokenizer (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your tokenizer in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the tokenizer files to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" with no local clone.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the tokenizer to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizer.convert_ids_to_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>convert_ids_to_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizer.convert_ids_to_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizer.convert_ids_to_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils.py#L881" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ids<span class="opacity-60">: typing.Union[int, typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code> or <code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.convert_ids_to_tokens.ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.convert_ids_to_tokens.ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ids</strong> (<code>int</code> or <code>List[int]</code>) —
The token id (or token ids) to convert to tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.convert_ids_to_tokens.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.convert_ids_to_tokens.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizer.convert_ids_to_tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code> or <code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The decoded token(s).</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
added tokens.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizer.convert_tokens_to_ids"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>convert_tokens_to_ids</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizer.convert_tokens_to_ids" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizer.convert_tokens_to_ids"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils.py#L560" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokens<span class="opacity-60">: typing.Union[str, typing.List[str]]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code> or <code>List[int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.convert_tokens_to_ids.tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.convert_tokens_to_ids.tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokens</strong> (<code>str</code> or <code>List[str]</code>) — One or several token(s) to convert to token id(s).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizer.convert_tokens_to_ids.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code> or <code>List[int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The token id or list of token ids.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
vocabulary.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizer.get_added_vocab"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_added_vocab</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizer.get_added_vocab" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizer.get_added_vocab"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils.py#L369" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Dict[str, int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizer.get_added_vocab.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Dict[str, int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The added tokens.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the added tokens in the vocabulary as a dictionary of token to index.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizer.num_special_tokens_to_add"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>num_special_tokens_to_add</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizer.num_special_tokens_to_add" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizer.num_special_tokens_to_add"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils.py#L458" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pair<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.num_special_tokens_to_add.pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.num_special_tokens_to_add.pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pair</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether the number of added tokens should be computed in the case of a sequence pair or a single
sequence.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizer.num_special_tokens_to_add.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Number of special tokens added to sequences.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the number of added tokens when encoding a sequence with special tokens.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put
this inside your training loop.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizer.prepare_for_tokenization"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>prepare_for_tokenization</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizer.prepare_for_tokenization" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizer.prepare_for_tokenization"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils.py#L819" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_split_into_words<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Tuple[str, Dict[str, Any]]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.prepare_for_tokenization.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.prepare_for_tokenization.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>) —
The text to prepare.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.prepare_for_tokenization.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.prepare_for_tokenization.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.
kwargs —
Keyword arguments to use for the tokenization.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizer.prepare_for_tokenization.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Tuple[str, Dict[str, Any]]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The prepared text and the unused kwargs.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Performs any necessary transformations before tokenization.</p>
<p>This method should pop the arguments from kwargs and return the remaining <code>kwargs</code> as well. We test the
<code>kwargs</code> at the end of the encoding process to be sure all the arguments have been used.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizer.tokenize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tokenize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizer.tokenize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizer.tokenize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils.py#L481" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.tokenize.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.tokenize.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>) —
The sequence to be encoded.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizer.tokenize.*kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizer.tokenize.*kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START -->*<strong>*kwargs</strong> (additional keyword arguments) —
Passed along to the model-specific <code>prepare_for_tokenization</code> preprocessing method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizer.tokenize.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The list of tokens.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a string in a sequence of tokens, using the tokenizer.</p>
<p>Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies
(BPE/SentencePieces/WordPieces). Takes care of added tokens.</p></div></div>
<h2 class="relative group"><a id="transformers.PreTrainedTokenizerFast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>PreTrainedTokenizerFast
</span></h2>
<p>The <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> depend on the <a href="https://huggingface.co/docs/tokenizers" rel="nofollow">tokenizers</a> library. The tokenizers obtained from the 🤗 tokenizers library can be
loaded very simply into 🤗 transformers. Take a look at the <a href="../fast_tokenizers">Using tokenizers from 🤗 tokenizers</a> page to understand how this is done.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerFast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PreTrainedTokenizerFast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerFast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerFast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_fast.py#L77" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.model_max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.model_max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_max_length</strong> (<code>int</code>, <em>optional</em>) —
The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
loaded with <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a>, this will be set to the
value stored for the associated model in <code>max_model_input_sizes</code> (see above). If no value is provided, will
default to VERY_LARGE_INTEGER (<code>int(1e30)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.padding_side" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.padding_side"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding_side</strong> (<code>str</code>, <em>optional</em>) —
The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.truncation_side" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.truncation_side"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation_side</strong> (<code>str</code>, <em>optional</em>) —
The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.model_input_names" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.model_input_names"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_input_names</strong> (<code>List[string]</code>, <em>optional</em>) —
The list of inputs accepted by the forward pass of the model (like <code>"token_type_ids"</code> or
<code>"attention_mask"</code>). Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.bos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.bos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the beginning of a sentence. Will be associated to <code>self.bos_token</code> and
<code>self.bos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.eos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.eos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the end of a sentence. Will be associated to <code>self.eos_token</code> and
<code>self.eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.unk_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.unk_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing an out-of-vocabulary token. Will be associated to <code>self.unk_token</code> and
<code>self.unk_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.sep_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.sep_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sep_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token separating two different sentences in the same input (used by BERT for instance). Will be
associated to <code>self.sep_token</code> and <code>self.sep_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.pad_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.pad_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation. Will be associated to <code>self.pad_token</code> and <code>self.pad_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.cls_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.cls_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the class of the input (used by BERT for instance). Will be associated to
<code>self.cls_token</code> and <code>self.cls_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.mask_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.mask_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT). Will be associated to <code>self.mask_token</code> and <code>self.mask_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.additional_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.additional_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>additional_special_tokens</strong> (tuple or list of <code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A tuple or a list of additional special tokens. Add them here to ensure they won’t be split by the
tokenization process. Will be associated to <code>self.additional_special_tokens</code> and
<code>self.additional_special_tokens_ids</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.tokenizer_object" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.tokenizer_object"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer_object</strong> (<code>tokenizers.Tokenizer</code>) —
A <code>tokenizers.Tokenizer</code> object from 🤗 tokenizers to instantiate from. See <a href="../fast_tokenizers">Using tokenizers from 🤗
tokenizers</a> for more information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.tokenizer_file" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.tokenizer_file"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer_file</strong> (<code>str</code>) —
A path to a local JSON file representing a previously serialized <code>tokenizers.Tokenizer</code> object from 🤗
tokenizers.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).</p>
<p>Inherits from <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a>.</p>
<p>Handles all the shared methods for tokenization and special tokens, as well as methods for
downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.</p>
<p>This class also contains the added tokens in a unified way on top of all tokenizers so we don’t have to handle the
specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece…).</p>
<p>Class attributes (overridden by derived classes)</p>
<ul><li><strong>vocab_files_names</strong> (<code>Dict[str, str]</code>) — A dictionary with, as keys, the <code>__init__</code> keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).</li>
<li><strong>pretrained_vocab_files_map</strong> (<code>Dict[str, Dict[str, str]]</code>) — A dictionary of dictionaries, with the
high-level keys being the <code>__init__</code> keyword name of each vocabulary file required by the model, the
low-level being the <code>short-cut-names</code> of the pretrained models with, as associated values, the <code>url</code> to the
associated pretrained vocabulary file.</li>
<li><strong>max_model_input_sizes</strong> (<code>Dict[str, Optional[int]]</code>) — A dictionary with, as keys, the <code>short-cut-names</code>
of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model,
or <code>None</code> if the model has no maximum input size.</li>
<li><strong>pretrained_init_configuration</strong> (<code>Dict[str, Dict[str, Any]]</code>) — A dictionary with, as keys, the
<code>short-cut-names</code> of the pretrained models, and as associated values, a dictionary of specific arguments to
pass to the <code>__init__</code> method of the tokenizer class for this pretrained model when loading the tokenizer
with the <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a> method.</li>
<li><strong>model_input_names</strong> (<code>List[str]</code>) — A list of inputs expected in the forward pass of the model.</li>
<li><strong>padding_side</strong> (<code>str</code>) — The default value for the side on which the model should have padding applied.
Should be <code>'right'</code> or <code>'left'</code>.</li>
<li><strong>truncation_side</strong> (<code>str</code>) — The default value for the side on which the model should have truncation
applied. Should be <code>'right'</code> or <code>'left'</code>.</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2379" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[typing.List[str]]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_pair<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_split_into_words<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_token_type_ids<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_overflowing_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_special_tokens_mask<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_offsets_mapping<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.text_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.text_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_pair</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_token_type_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_token_type_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_overflowing_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_overflowing_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_special_tokens_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_special_tokens_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_offsets_mapping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_offsets_mapping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.verbose" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.verbose"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> — List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> — List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>“token_type_ids”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>“attention_mask”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> — List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> — Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> — The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.batch_decode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>batch_decode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.batch_decode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.batch_decode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3250" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">clean_up_tokenization_spaces<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
List of tokenized input ids. Can be obtained using the <code>__call__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to clean up the tokenization spaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific decode method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.batch_decode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The list of decoded sentences.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Convert a list of lists of token ids into a list of strings by calling decode.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.decode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>decode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.decode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.decode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3283" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids<span class="opacity-60">: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">clean_up_tokenization_spaces<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.token_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.token_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids</strong> (<code>Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
List of tokenized input ids. Can be obtained using the <code>__call__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to clean up the tokenization spaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific decode method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.decode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The decoded sentence.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.</p>
<p>Similar to doing <code>self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.encode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>encode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.encode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.encode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2189" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_pair<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[int]</code>, <code>torch.Tensor</code>, <code>tf.Tensor</code> or <code>np.ndarray</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>) —
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
<code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.text_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.text_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_pair</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>, <em>optional</em>) —
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the <code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.encode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[int]</code>, <code>torch.Tensor</code>, <code>tf.Tensor</code> or <code>np.ndarray</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The tokenized ids of the text.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.</p>
<p>Same as doing <code>self.convert_tokens_to_ids(self.tokenize(text))</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your tokenizer in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add tokenizer"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your tokenizer (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your tokenizer in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the tokenizer files to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" with no local clone.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the tokenizer to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerFast.convert_ids_to_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>convert_ids_to_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerFast.convert_ids_to_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerFast.convert_ids_to_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_fast.py#L292" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ids<span class="opacity-60">: typing.Union[int, typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code> or <code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.convert_ids_to_tokens.ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.convert_ids_to_tokens.ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ids</strong> (<code>int</code> or <code>List[int]</code>) —
The token id (or token ids) to convert to tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.convert_ids_to_tokens.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.convert_ids_to_tokens.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerFast.convert_ids_to_tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code> or <code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The decoded token(s).</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
added tokens.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerFast.convert_tokens_to_ids"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>convert_tokens_to_ids</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerFast.convert_tokens_to_ids" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerFast.convert_tokens_to_ids"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_fast.py#L234" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokens<span class="opacity-60">: typing.Union[str, typing.List[str]]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code> or <code>List[int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.convert_tokens_to_ids.tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.convert_tokens_to_ids.tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokens</strong> (<code>str</code> or <code>List[str]</code>) — One or several token(s) to convert to token id(s).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerFast.convert_tokens_to_ids.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code> or <code>List[int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The token id or list of token ids.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
vocabulary.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerFast.get_added_vocab"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_added_vocab</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerFast.get_added_vocab" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerFast.get_added_vocab"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_fast.py#L155" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Dict[str, int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerFast.get_added_vocab.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Dict[str, int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The added tokens.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the added tokens in the vocabulary as a dictionary of token to index.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerFast.num_special_tokens_to_add"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>num_special_tokens_to_add</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerFast.num_special_tokens_to_add" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerFast.num_special_tokens_to_add"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_fast.py#L271" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pair<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.num_special_tokens_to_add.pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.num_special_tokens_to_add.pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pair</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether the number of added tokens should be computed in the case of a sequence pair or a single
sequence.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerFast.num_special_tokens_to_add.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Number of special tokens added to sequences.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the number of added tokens when encoding a sequence with special tokens.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put
this inside your training loop.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerFast.set_truncation_and_padding"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>set_truncation_and_padding</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerFast.set_truncation_and_padding" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerFast.set_truncation_and_padding"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_fast.py#L321" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding_strategy<span class="opacity-60">: PaddingStrategy</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation_strategy<span class="opacity-60">: TruncationStrategy</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.set_truncation_and_padding.padding_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.set_truncation_and_padding.padding_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding_strategy</strong> (<a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>) —
The kind of padding that will be applied to the input<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.set_truncation_and_padding.truncation_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.set_truncation_and_padding.truncation_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation_strategy</strong> (<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>) —
The kind of truncation that will be applied to the input<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.set_truncation_and_padding.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.set_truncation_and_padding.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>) —
The maximum size of a sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.set_truncation_and_padding.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.set_truncation_and_padding.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>) —
The stride to use when handling overflow.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.set_truncation_and_padding.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.set_truncation_and_padding.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers
library) and restore the tokenizer settings afterwards.</p>
<p>The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a
padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed
section.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerFast.train_new_from_iterator"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>train_new_from_iterator</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerFast.train_new_from_iterator" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerFast.train_new_from_iterator"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_fast.py#L604" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_iterator<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">vocab_size<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">new_special_tokens<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens_map<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast"
>PreTrainedTokenizerFast</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.train_new_from_iterator.text_iterator" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.train_new_from_iterator.text_iterator"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_iterator</strong> (generator of <code>List[str]</code>) —
The training corpus. Should be a generator of batches of texts, for instance a list of lists of texts
if you have everything in memory.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.train_new_from_iterator.vocab_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.train_new_from_iterator.vocab_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>vocab_size</strong> (<code>int</code>) —
The size of the vocabulary you want for your tokenizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.train_new_from_iterator.new_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.train_new_from_iterator.new_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>new_special_tokens</strong> (list of <code>str</code> or <code>AddedToken</code>, <em>optional</em>) —
A list of new special tokens to add to the tokenizer you are training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerFast.train_new_from_iterator.special_tokens_map" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerFast.train_new_from_iterator.special_tokens_map"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>special_tokens_map</strong> (<code>Dict[str, str]</code>, <em>optional</em>) —
If you want to rename some of the special tokens this tokenizer uses, pass along a mapping old special
token name to new special token name in this argument.
kwargs —
Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerFast.train_new_from_iterator.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast"
>PreTrainedTokenizerFast</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A new tokenizer of the same type as the original one, trained on
<code>text_iterator</code>.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline)
as the current one.</p></div></div>
<h2 class="relative group"><a id="transformers.BatchEncoding" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BatchEncoding
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">BatchEncoding</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L167" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60">: typing.Union[typing.Dict[str, typing.Any], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoding<span class="opacity-60">: typing.Union[tokenizers.Encoding, typing.Sequence[tokenizers.Encoding], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tensor_type<span class="opacity-60">: typing.Union[NoneType, str, transformers.file_utils.TensorType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prepend_batch_axis<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">n_sequences<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>dict</code>) —
Dictionary of lists/arrays/tensors returned by the encode/batch_encode methods (‘input_ids’,
‘attention_mask’, etc.).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.encoding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.encoding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoding</strong> (<code>tokenizers.Encoding</code> or <code>Sequence[tokenizers.Encoding]</code>, <em>optional</em>) —
If the tokenizer is a fast tokenizer which outputs additional information like mapping from word/character
space to token space the <code>tokenizers.Encoding</code> instance or list of instance (for batches) hold this
information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.tensor_type" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.tensor_type"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tensor_type</strong> (<code>Union[None, str, TensorType]</code>, <em>optional</em>) —
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.prepend_batch_axis" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.prepend_batch_axis"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>prepend_batch_axis</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to add a batch axis when converting to tensors (see <code>tensor_type</code> above).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.n_sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.n_sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>n_sequences</strong> (<code>Optional[int]</code>, <em>optional</em>) —
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Holds the output of the <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode_plus">encode_plus()</a> and
<code>batch_encode</code> methods (tokens, attention_masks, etc).</p>
<p>This class is derived from a python dictionary and can be used as a dictionary. In addition, this class exposes
utility methods to map from word/character space to token space.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.char_to_token"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>char_to_token</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.char_to_token" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.char_to_token"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L523" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_or_char_index<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">char_index<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequence_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.char_to_token.batch_or_char_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.char_to_token.batch_or_char_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_or_char_index</strong> (<code>int</code>) —
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the word in the sequence<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.char_to_token.char_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.char_to_token.char_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>char_index</strong> (<code>int</code>, <em>optional</em>) —
If a batch index is provided in <em>batch_or_token_index</em>, this can be the index of the word in the
sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.char_to_token.sequence_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.char_to_token.sequence_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequence_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided character index belongs to.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.char_to_token.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Index of the token.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the index of the token in the encoded output comprising a character in the original string for a sequence
of the batch.</p>
<p>Can be called as:</p>
<ul><li><code>self.char_to_token(char_index)</code> if batch size is 1</li>
<li><code>self.char_to_token(batch_index, char_index)</code> if batch size is greater or equal to 1</li></ul>
<p>This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words
are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized
words.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.char_to_word"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>char_to_word</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.char_to_word" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.char_to_word"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L609" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_or_char_index<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">char_index<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequence_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code> or <code>List[int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.char_to_word.batch_or_char_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.char_to_word.batch_or_char_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_or_char_index</strong> (<code>int</code>) —
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the character in the original string.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.char_to_word.char_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.char_to_word.char_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>char_index</strong> (<code>int</code>, <em>optional</em>) —
If a batch index is provided in <em>batch_or_token_index</em>, this can be the index of the character in the
original string.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.char_to_word.sequence_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.char_to_word.sequence_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequence_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided character index belongs to.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.char_to_word.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code> or <code>List[int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Index or indices of the associated encoded token(s).</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the word in the original string corresponding to a character in the original string of a sequence of the
batch.</p>
<p>Can be called as:</p>
<ul><li><code>self.char_to_word(char_index)</code> if batch size is 1</li>
<li><code>self.char_to_word(batch_index, char_index)</code> if batch size is greater than 1</li></ul>
<p>This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words
are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized
words.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.convert_to_tensors"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>convert_to_tensors</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.convert_to_tensors" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.convert_to_tensors"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L648" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tensor_type<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prepend_batch_axis<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.convert_to_tensors.tensor_type" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.convert_to_tensors.tensor_type"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tensor_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
The type of tensors to use. If <code>str</code>, should be one of the values of the enum
<a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>. If <code>None</code>, no modification is done.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.convert_to_tensors.prepend_batch_axis" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.convert_to_tensors.prepend_batch_axis"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>prepend_batch_axis</strong> (<code>int</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to add the batch dimension during the conversion.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Convert the inner content to tensors.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.sequence_ids"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>sequence_ids</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.sequence_ids" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.sequence_ids"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L301" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[Optional[int]]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.sequence_ids.batch_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.sequence_ids.batch_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) — The index to access in the batch.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.sequence_ids.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[Optional[int]]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A list indicating the sequence id corresponding to each token. Special tokens added
by the tokenizer are mapped to <code>None</code> and other tokens are mapped to the index of their corresponding
sequence.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Return a list mapping the tokens to the id of their original sentences:</p>
<ul><li><code>None</code> for special tokens added around or between sequences,</li>
<li><code>0</code> for tokens corresponding to words in the first sequence,</li>
<li><code>1</code> for tokens corresponding to words in the second sequence when a pair of sequences was jointly
encoded.</li></ul></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.to"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.to" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.to"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L731" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: typing.Union[str, ForwardRef('torch.device')]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.to.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.to.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>str</code> or <code>torch.device</code>) — The device to put the tensors on.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.to.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The same instance after modification.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Send all values to device by calling <code>v.to(device)</code> (PyTorch only).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.token_to_chars"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>token_to_chars</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.token_to_chars" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.token_to_chars"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L487" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_or_token_index<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_index<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.CharSpan"
>CharSpan</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.token_to_chars.batch_or_token_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.token_to_chars.batch_or_token_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_or_token_index</strong> (<code>int</code>) —
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the token in the sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.token_to_chars.token_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.token_to_chars.token_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_index</strong> (<code>int</code>, <em>optional</em>) —
If a batch index is provided in <em>batch_or_token_index</em>, this can be the index of the token or tokens in
the sequence.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.token_to_chars.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.CharSpan"
>CharSpan</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Span of characters in the original string.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the character span corresponding to an encoded token in a sequence of the batch.</p>
<p>Character spans are returned as a <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.CharSpan">CharSpan</a> with:</p>
<ul><li><strong>start</strong> — Index of the first character in the original string associated to the token.</li>
<li><strong>end</strong> — Index of the character following the last character in the original string associated to the
token.</li></ul>
<p>Can be called as:</p>
<ul><li><code>self.token_to_chars(token_index)</code> if batch size is 1</li>
<li><code>self.token_to_chars(batch_index, token_index)</code> if batch size is greater or equal to 1</li></ul></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.token_to_sequence"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>token_to_sequence</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.token_to_sequence" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.token_to_sequence"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L359" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_or_token_index<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_index<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.token_to_sequence.batch_or_token_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.token_to_sequence.batch_or_token_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_or_token_index</strong> (<code>int</code>) —
Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of
the token in the sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.token_to_sequence.token_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.token_to_sequence.token_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_index</strong> (<code>int</code>, <em>optional</em>) —
If a batch index is provided in <em>batch_or_token_index</em>, this can be the index of the token in the
sequence.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.token_to_sequence.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Index of the word in the input sequence.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the index of the sequence represented by the given token. In the general use case, this method returns <code>0</code>
for a single sequence or the first sequence of a pair, and <code>1</code> for the second sequence of a pair</p>
<p>Can be called as:</p>
<ul><li><code>self.token_to_sequence(token_index)</code> if batch size is 1</li>
<li><code>self.token_to_sequence(batch_index, token_index)</code> if batch size is greater than 1</li></ul>
<p>This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e.,
words are defined by the user). In this case it allows to easily associate encoded tokens with provided
tokenized words.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.token_to_word"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>token_to_word</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.token_to_word" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.token_to_word"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L398" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_or_token_index<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_index<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.token_to_word.batch_or_token_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.token_to_word.batch_or_token_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_or_token_index</strong> (<code>int</code>) —
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the token in the sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.token_to_word.token_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.token_to_word.token_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_index</strong> (<code>int</code>, <em>optional</em>) —
If a batch index is provided in <em>batch_or_token_index</em>, this can be the index of the token in the
sequence.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.token_to_word.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Index of the word in the input sequence.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the index of the word corresponding (i.e. comprising) to an encoded token in a sequence of the batch.</p>
<p>Can be called as:</p>
<ul><li><code>self.token_to_word(token_index)</code> if batch size is 1</li>
<li><code>self.token_to_word(batch_index, token_index)</code> if batch size is greater than 1</li></ul>
<p>This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e.,
words are defined by the user). In this case it allows to easily associate encoded tokens with provided
tokenized words.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L286" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.tokens.batch_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.tokens.batch_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) — The index to access in the batch.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The list of tokens at that index.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Return the list of tokens (sub-parts of the input strings after word/subword splitting and before conversion to
integer indices) at a given batch index (only works for the output of a fast tokenizer).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.word_ids"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>word_ids</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.word_ids" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.word_ids"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L343" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[Optional[int]]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.word_ids.batch_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.word_ids.batch_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) — The index to access in the batch.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.word_ids.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[Optional[int]]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A list indicating the word corresponding to each token. Special tokens added by the
tokenizer are mapped to <code>None</code> and other tokens are mapped to the index of their corresponding word
(several tokens will be mapped to the same word index if they are parts of that word).</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.word_to_chars"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>word_to_chars</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.word_to_chars" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.word_to_chars"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L564" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_or_word_index<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">word_index<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequence_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>CharSpan</code> or <code>List[CharSpan]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.word_to_chars.batch_or_word_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.word_to_chars.batch_or_word_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_or_word_index</strong> (<code>int</code>) —
Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of
the word in the sequence<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.word_to_chars.word_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.word_to_chars.word_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>word_index</strong> (<code>int</code>, <em>optional</em>) —
If a batch index is provided in <em>batch_or_token_index</em>, this can be the index of the word in the
sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.word_to_chars.sequence_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.word_to_chars.sequence_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequence_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided word index belongs to.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.word_to_chars.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>CharSpan</code> or <code>List[CharSpan]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Span(s) of the associated character or characters in the string. CharSpan
are NamedTuple with:</p>
<ul>
<li>start: index of the first character associated to the token in the original string</li>
<li>end: index of the character following the last character associated to the token in the original
string</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Get the character span in the original string corresponding to given word in a sequence of the batch.</p>
<p>Character spans are returned as a CharSpan NamedTuple with:</p>
<ul><li>start: index of the first character in the original string</li>
<li>end: index of the character following the last character in the original string</li></ul>
<p>Can be called as:</p>
<ul><li><code>self.word_to_chars(word_index)</code> if batch size is 1</li>
<li><code>self.word_to_chars(batch_index, word_index)</code> if batch size is greater or equal to 1</li></ul></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.word_to_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>word_to_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.word_to_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.word_to_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L436" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_or_word_index<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">word_index<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequence_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.word_to_tokens.batch_or_word_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.word_to_tokens.batch_or_word_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_or_word_index</strong> (<code>int</code>) —
Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of
the word in the sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.word_to_tokens.word_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.word_to_tokens.word_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>word_index</strong> (<code>int</code>, <em>optional</em>) —
If a batch index is provided in <em>batch_or_token_index</em>, this can be the index of the word in the
sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.word_to_tokens.sequence_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.word_to_tokens.sequence_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequence_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
or 1) the provided word index belongs to.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Get the encoded token span corresponding to a word in a sequence of the batch.</p>
<p>Token spans are returned as a <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.TokenSpan">TokenSpan</a> with:</p>
<ul><li><strong>start</strong> — Index of the first token.</li>
<li><strong>end</strong> — Index of the token following the last token.</li></ul>
<p>Can be called as:</p>
<ul><li><code>self.word_to_tokens(word_index, sequence_index: int = 0)</code> if batch size is 1</li>
<li><code>self.word_to_tokens(batch_index, word_index, sequence_index: int = 0)</code> if batch size is greater or equal to
1</li></ul>
<p>This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words
are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized
words.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BatchEncoding.words"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>words</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BatchEncoding.words" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BatchEncoding.words"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L322" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_index<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[Optional[int]]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BatchEncoding.words.batch_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BatchEncoding.words.batch_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_index</strong> (<code>int</code>, <em>optional</em>, defaults to 0) — The index to access in the batch.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BatchEncoding.words.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[Optional[int]]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A list indicating the word corresponding to each token. Special tokens added by the
tokenizer are mapped to <code>None</code> and other tokens are mapped to the index of their corresponding word
(several tokens will be mapped to the same word index if they are parts of that word).</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.</p></div></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en | hf_public_repos/doc-build-dev/transformers/pr_16143/en/main_classes/keras_callbacks.html | <meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{"local":"keras-callbacks","sections":[{"local":"transformers.KerasMetricCallback","title":"KerasMetricCallback"},{"local":"transformers.PushToHubCallback","title":"PushToHubCallback"}],"title":"Keras callbacks"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="keras-callbacks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#keras-callbacks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Keras callbacks
</span></h1>
<p>When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
tasks:</p>
<h2 class="relative group"><a id="transformers.KerasMetricCallback" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.KerasMetricCallback"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>KerasMetricCallback
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.KerasMetricCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">KerasMetricCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.KerasMetricCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.KerasMetricCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/keras_callbacks.py#L22" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">metric_fn<span class="opacity-60">: typing.Callable</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eval_dataset<span class="opacity-60">: typing.Union[tensorflow.python.data.ops.dataset_ops.DatasetV2, numpy.ndarray, tensorflow.python.framework.ops.Tensor, tuple, dict]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_cols<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_cols<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_size<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">predict_with_generate<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.KerasMetricCallback.metric_fn" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.KerasMetricCallback.metric_fn"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>metric_fn</strong> (<code>Callable</code>) —
Metric function provided by the user. It will be called with two arguments - <code>predictions</code> and <code>labels</code>.
These contain the model’s outputs and matching labels from the dataset. It should return a dict mapping
metric names to numerical values.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.KerasMetricCallback.eval_dataset" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.KerasMetricCallback.eval_dataset"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eval_dataset</strong> (<code>tf.data.Dataset</code> or <code>dict</code> or <code>tuple</code> or <code>np.ndarray</code> or <code>tf.Tensor</code>) —
Validation data to be used to generate predictions for the <code>metric_fn</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.KerasMetricCallback.output_cols" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.KerasMetricCallback.output_cols"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_cols</strong> (`List[str], <em>optional</em>) —
A list of columns to be retained from the model output as the predictions. Defaults to all.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.KerasMetricCallback.label_cols" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.KerasMetricCallback.label_cols"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_cols</strong> (’<code>List[str]</code>, <em>optional</em>’) —
A list of columns to be retained from the input dataset as the labels. Will be autodetected if this is not
supplied.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.KerasMetricCallback.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.KerasMetricCallback.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>) —
Batch size. Only used when the data is not a pre-batched <code>tf.data.Dataset</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.KerasMetricCallback.predict_with_generate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.KerasMetricCallback.predict_with_generate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>predict_with_generate</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether we should use <code>model.generate()</code> to get outputs for the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Callback to compute metrics at the end of every epoch. Unlike normal Keras metrics, these do not need to be
compilable by TF. It is particularly useful for common NLP metrics like BLEU and ROUGE that require string
operations or generation loops that cannot be compiled. Predictions (or generations) will be computed on the
<code>eval_dataset</code> before being passed to the <code>metric_fn</code> in <code>np.ndarray</code> format. The <code>metric_fn</code> should compute
metrics and return a dict mapping metric names to metric values.</p>
<p>We provide an example of a suitable metric_fn that computes ROUGE scores for a summarization model below. Note that
this example skips some post-processing for readability and simplicity, and should probably not be used as-is!</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric
rouge_metric = load_metric(<span class="hljs-string">"rouge"</span>)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">rouge_fn</span>(<span class="hljs-params">predictions, labels</span>):
decoded_predictions = tokenizer.batch_decode(predictions, skip_special_tokens=<span class="hljs-literal">True</span>)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=<span class="hljs-literal">True</span>)
result = rouge_metric.compute(predictions=decoded_predictions, references=decoded_labels)
<span class="hljs-keyword">return</span> {key: value.mid.fmeasure * <span class="hljs-number">100</span> <span class="hljs-keyword">for</span> key, value <span class="hljs-keyword">in</span> result.items()}<!-- HTML_TAG_END --></pre></div>
<p>The above function will return a dict containing values which will be logged like any other Keras metric:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->{'rouge1': <span class="hljs-number">37.4199</span>, 'rouge2': <span class="hljs-number">13.9768</span>, 'rougeL': <span class="hljs-number">34.361</span>, 'rougeLsum': <span class="hljs-number">35.0781</span><!-- HTML_TAG_END --></pre></div></div>
<h2 class="relative group"><a id="transformers.PushToHubCallback" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>PushToHubCallback
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PushToHubCallback"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PushToHubCallback</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PushToHubCallback" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PushToHubCallback"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/keras_callbacks.py#L242" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_dir<span class="opacity-60">: typing.Union[str, pathlib.Path]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_strategy<span class="opacity-60">: typing.Union[str, transformers.trainer_utils.IntervalStrategy] = 'epoch'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_steps<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: typing.Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_model_id<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hub_token<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">checkpoint<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_args<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PushToHubCallback.output_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback.output_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_dir</strong> (<code>str</code>) —
The output directory where the model predictions and checkpoints will be written and synced with the
repository on the Hub.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PushToHubCallback.save_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback.save_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/trainer_utils#transformers.IntervalStrategy">IntervalStrategy</a>, <em>optional</em>, defaults to <code>"epoch"</code>) —
The checkpoint save strategy to adopt during training. Possible values are:</p>
<ul>
<li><code>"no"</code>: Save is done at the end of training.</li>
<li><code>"epoch"</code>: Save is done at the end of each epoch.</li>
<li><code>"steps"</code>: Save is done every <code>save_steps</code></li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PushToHubCallback.save_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback.save_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_steps</strong> (<code>int</code>, <em>optional</em>) —
The number of steps between saves when using the “steps” <code>save_strategy</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PushToHubCallback.tokenizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback.tokenizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokenizer</strong> (<code>PreTrainedTokenizerBase</code>, <em>optional</em>) —
The tokenizer used by the model. If supplied, will be uploaded to the repo alongside the weights.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PushToHubCallback.hub_model_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback.hub_model_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_model_id</strong> (<code>str</code>, <em>optional</em>) —
The name of the repository to keep in sync with the local <code>output_dir</code>. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance <code>"user_name/model"</code>, which allows you to push to an organization you are a member of with
<code>"organization_name/model"</code>.</p>
<p>Will default to to the name of <code>output_dir</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PushToHubCallback.hub_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback.hub_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hub_token</strong> (<code>str</code>, <em>optional</em>) —
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
<code>huggingface-cli login</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PushToHubCallback.checkpoint" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PushToHubCallback.checkpoint"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>checkpoint</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to save full training checkpoints (including epoch and optimizer state) to allow training to be
resumed. Only usable when <code>save_strategy</code> is <code>"epoch"</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Callback that will save and push the model to the Hub regularly. By default, it pushes once per epoch, but this can
be changed with the <code>save_strategy</code> argument. Pushed models can be accessed like any other model on the hub, such
as with the <code>from_pretrained</code> method.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.keras_callbacks <span class="hljs-keyword">import</span> PushToHubCallback
push_to_hub_callback = PushToHubCallback(
output_dir=<span class="hljs-string">"./model_save"</span>,
tokenizer=tokenizer,
hub_model_id=<span class="hljs-string">"gpt5-7xlarge"</span>,
)
model.fit(train_dataset, callbacks=[push_to_hub_callback])<!-- HTML_TAG_END --></pre></div></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en | hf_public_repos/doc-build-dev/transformers/pr_16143/en/main_classes/optimizer_schedules.html | <meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{"local":"optimization","sections":[{"local":"transformers.AdamW","title":"AdamW (PyTorch)"},{"local":"transformers.Adafactor","title":"AdaFactor (PyTorch)"},{"local":"transformers.AdamWeightDecay","title":"AdamWeightDecay (TensorFlow)"},{"local":"schedules","sections":[{"local":"transformers.SchedulerType","title":"Learning Rate Schedules (Pytorch)"},{"local":"transformers.WarmUp","title":"Warmup (TensorFlow)"}],"title":"Schedules"},{"local":"gradient-strategies","sections":[{"local":"transformers.GradientAccumulator","title":"GradientAccumulator (TensorFlow)"}],"title":"Gradient Strategies"}],"title":"Optimization"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="optimization" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#optimization"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Optimization
</span></h1>
<p>The <code>.optimization</code> module provides:</p>
<ul><li>an optimizer with weight decay fixed that can be used to fine-tuned models, and</li>
<li>several schedules in the form of schedule objects that inherit from <code>_LRSchedule</code>:</li>
<li>a gradient accumulation class to accumulate the gradients of multiple batches</li></ul>
<h2 class="relative group"><a id="transformers.AdamW" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AdamW (PyTorch)
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AdamW"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">AdamW</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.AdamW" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AdamW"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L273" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">params<span class="opacity-60">: typing.Iterable[torch.nn.parameter.Parameter]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">lr<span class="opacity-60">: float = 0.001</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">betas<span class="opacity-60">: typing.Tuple[float, float] = (0.9, 0.999)</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eps<span class="opacity-60">: float = 1e-06</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">weight_decay<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">correct_bias<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">no_deprecation_warning<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.params" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.params"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>params</strong> (<code>Iterable[nn.parameter.Parameter]</code>) —
Iterable of parameters to optimize or dictionaries defining parameter groups.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.lr" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.lr"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>lr</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-3) —
The learning rate to use.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.betas" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.betas"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>betas</strong> (<code>Tuple[float,float]</code>, <em>optional</em>, defaults to (0.9, 0.999)) —
Adam’s betas parameters (b1, b2).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.eps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.eps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-6) —
Adam’s epsilon for numerical stability.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.weight_decay" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.weight_decay"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
Decoupled weight decay to apply.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.correct_bias" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.correct_bias"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>correct_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to correct bias in Adam (for instance, in Bert TF repository they use <code>False</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.no_deprecation_warning" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.no_deprecation_warning"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>no_deprecation_warning</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
A flag used to disable the deprecation warning (set to <code>True</code> to disable the warning).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Implements Adam algorithm with weight decay fix as introduced in <a href="https://arxiv.org/abs/1711.05101" rel="nofollow">Decoupled Weight Decay
Regularization</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AdamW.step"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>step</span></h4><!-- HTML_TAG_END -->
<a id="transformers.AdamW.step" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AdamW.step"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L323" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">closure<span class="opacity-60">: typing.Callable = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamW.step.closure" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamW.step.closure"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>closure</strong> (<code>Callable</code>, <em>optional</em>) — A closure that reevaluates the model and returns the loss.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Performs a single optimization step.</p></div></div>
<h2 class="relative group"><a id="transformers.Adafactor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AdaFactor (PyTorch)
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Adafactor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Adafactor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Adafactor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Adafactor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L385" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">params<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">lr<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eps<span class="opacity-60"> = (1e-30, 0.001)</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">clip_threshold<span class="opacity-60"> = 1.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decay_rate<span class="opacity-60"> = -0.8</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beta1<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">weight_decay<span class="opacity-60"> = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scale_parameter<span class="opacity-60"> = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">relative_step<span class="opacity-60"> = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">warmup_init<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.params" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.params"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>params</strong> (<code>Iterable[nn.parameter.Parameter]</code>) —
Iterable of parameters to optimize or dictionaries defining parameter groups.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.lr" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.lr"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>lr</strong> (<code>float</code>, <em>optional</em>) —
The external learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.eps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.eps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eps</strong> (<code>Tuple[float, float]</code>, <em>optional</em>, defaults to (1e-30, 1e-3)) —
Regularization constants for square gradient and parameter scale respectively<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.clip_threshold" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.clip_threshold"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clip_threshold</strong> (<code>float</code>, <em>optional</em>, defaults 1.0) —
Threshold of root mean square of final gradient update<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.decay_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.decay_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to -0.8) —
Coefficient used to compute running averages of square<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.beta1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.beta1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beta1</strong> (<code>float</code>, <em>optional</em>) —
Coefficient used for computing running averages of gradient<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.weight_decay" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.weight_decay"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>weight_decay</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
Weight decay (L2 penalty)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.scale_parameter" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.scale_parameter"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scale_parameter</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If True, learning rate is scaled by root mean square<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.relative_step" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.relative_step"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>relative_step</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
If True, time-dependent learning rate is computed instead of external learning rate<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.warmup_init" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.warmup_init"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>warmup_init</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Time-dependent learning rate computation depends on whether warm-up initialization is being used<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code:
<a href="https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py" rel="nofollow">https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py</a></p>
<p>Paper: <em>Adafactor: Adaptive Learning Rates with Sublinear Memory Cost</em> <a href="https://arxiv.org/abs/1804.04235" rel="nofollow">https://arxiv.org/abs/1804.04235</a> Note that
this optimizer internally adjusts the learning rate depending on the <code>scale_parameter</code>, <code>relative_step</code> and
<code>warmup_init</code> options. To use a manual (external) learning rate schedule you should set <code>scale_parameter=False</code> and
<code>relative_step=False</code>.</p>
<p>This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.</p>
<p>Recommended T5 finetuning settings (<a href="https://discuss.huggingface.co/t/t5-finetuning-tips/684/3" rel="nofollow">https://discuss.huggingface.co/t/t5-finetuning-tips/684/3</a>):</p>
<ul><li><p>Training without LR warmup or clip_threshold is not recommended.</p>
<ul><li>use scheduled LR warm-up to fixed LR</li>
<li>use clip_threshold=1.0 (<a href="https://arxiv.org/abs/1804.04235" rel="nofollow">https://arxiv.org/abs/1804.04235</a>)</li></ul></li>
<li><p>Disable relative updates</p></li>
<li><p>Use scale_parameter=False</p></li>
<li><p>Additional optimizer operations like gradient clipping should not be used alongside Adafactor</p></li></ul>
<p>Example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">False</span>, relative_step=<span class="hljs-literal">False</span>, warmup_init=<span class="hljs-literal">False</span>, lr=<span class="hljs-number">1e-3</span>)<!-- HTML_TAG_END --></pre></div>
<p>Others reported the following combination to work well:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">True</span>, relative_step=<span class="hljs-literal">True</span>, warmup_init=<span class="hljs-literal">True</span>, lr=<span class="hljs-literal">None</span>)<!-- HTML_TAG_END --></pre></div>
<p>When using <code>lr=None</code> with <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> you will most likely need to use <code>AdafactorSchedule</code>scheduler as following:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.optimization <span class="hljs-keyword">import</span> Adafactor, AdafactorSchedule
optimizer = Adafactor(model.parameters(), scale_parameter=<span class="hljs-literal">True</span>, relative_step=<span class="hljs-literal">True</span>, warmup_init=<span class="hljs-literal">True</span>, lr=<span class="hljs-literal">None</span>)
lr_scheduler = AdafactorSchedule(optimizer)
trainer = Trainer(..., optimizers=(optimizer, lr_scheduler))<!-- HTML_TAG_END --></pre></div>
<p>Usage:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-comment"># replace AdamW with Adafactor</span>
optimizer = Adafactor(
model.parameters(),
lr=<span class="hljs-number">1e-3</span>,
eps=(<span class="hljs-number">1e-30</span>, <span class="hljs-number">1e-3</span>),
clip_threshold=<span class="hljs-number">1.0</span>,
decay_rate=-<span class="hljs-number">0.8</span>,
beta1=<span class="hljs-literal">None</span>,
weight_decay=<span class="hljs-number">0.0</span>,
relative_step=<span class="hljs-literal">False</span>,
scale_parameter=<span class="hljs-literal">False</span>,
warmup_init=<span class="hljs-literal">False</span>,
)<!-- HTML_TAG_END --></pre></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Adafactor.step"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>step</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Adafactor.step" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Adafactor.step"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L531" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">closure<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Adafactor.step.closure" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Adafactor.step.closure"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>closure</strong> (callable, optional) — A closure that reevaluates the model
and returns the loss.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Performs a single optimization step</p></div></div>
<h2 class="relative group"><a id="transformers.AdamWeightDecay" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AdamWeightDecay (TensorFlow)
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AdamWeightDecay"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">AdamWeightDecay</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.AdamWeightDecay" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AdamWeightDecay"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L152" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">learning_rate<span class="opacity-60">: typing.Union[float, keras.optimizer_v2.learning_rate_schedule.LearningRateSchedule] = 0.001</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beta_1<span class="opacity-60">: float = 0.9</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beta_2<span class="opacity-60">: float = 0.999</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">epsilon<span class="opacity-60">: float = 1e-07</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">amsgrad<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">weight_decay_rate<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">include_in_weight_decay<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">exclude_from_weight_decay<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">name<span class="opacity-60">: str = 'AdamWeightDecay'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.learning_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.learning_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>learning_rate</strong> (<code>Union[float, tf.keras.optimizers.schedules.LearningRateSchedule]</code>, <em>optional</em>, defaults to 1e-3) —
The learning rate to use or a schedule.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.beta_1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.beta_1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beta_1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.beta_2" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.beta_2"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beta_2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.epsilon" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.epsilon"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-7) —
The epsilon parameter in Adam, which is a small constant for numerical stability.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.amsgrad" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.amsgrad"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>amsgrad</strong> (<code>bool</code>, <em>optional</em>, default to <code>False</code>) —
Whether to apply AMSGrad variant of this algorithm or not, see <a href="https://arxiv.org/abs/1904.09237" rel="nofollow">On the Convergence of Adam and
Beyond</a>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.weight_decay_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.weight_decay_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>weight_decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to apply.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.include_in_weight_decay" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.include_in_weight_decay"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>include_in_weight_decay</strong> (<code>List[str]</code>, <em>optional</em>) —
List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
applied to all parameters by default (unless they are in <code>exclude_from_weight_decay</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.exclude_from_weight_decay" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.exclude_from_weight_decay"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>exclude_from_weight_decay</strong> (<code>List[str]</code>, <em>optional</em>) —
List of the parameter names (or re patterns) to exclude from applying weight decay to. If a
<code>include_in_weight_decay</code> is passed, the names in it will supersede this list.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.AdamWeightDecay.name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.AdamWeightDecay.name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>name</strong> (<code>str</code>, <em>optional</em>, defaults to ‘AdamWeightDecay’) —
Optional name for the operations created when applying gradients.
kwargs —
Keyword arguments. Allowed to be {<code>clipnorm</code>, <code>clipvalue</code>, <code>lr</code>, <code>decay</code>}. <code>clipnorm</code> is clip gradients by
norm; <code>clipvalue</code> is clip gradients by value, <code>decay</code> is included for backward compatibility to allow time
inverse decay of learning rate. <code>lr</code> is included for backward compatibility, recommended to use
<code>learning_rate</code> instead.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Adam enables L2 weight decay and clip_by_global_norm on gradients. Just adding the square of the weights to the
loss function is <em>not</em> the correct way of using L2 regularization/weight decay with Adam, since that will interact
with the m and v parameters in strange ways as shown in <a href="https://arxiv.org/abs/1711.05101" rel="nofollow">Decoupled Weight Decay
Regularization</a>.</p>
<p>Instead we want ot decay the weights in a manner that doesn’t interact with the m/v parameters. This is equivalent
to adding the square of the weights to the loss with plain (non-momentum) SGD.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.AdamWeightDecay.from_config"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_config</span></h4><!-- HTML_TAG_END -->
<a id="transformers.AdamWeightDecay.from_config" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.AdamWeightDecay.from_config"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L209" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Creates an optimizer from its config with WarmUp custom object.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.create_optimizer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.create_optimizer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.create_optimizer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.create_optimizer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L82" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">init_lr<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_train_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_warmup_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_lr_ratio<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_beta1<span class="opacity-60">: float = 0.9</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_beta2<span class="opacity-60">: float = 0.999</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">adam_epsilon<span class="opacity-60">: float = 1e-08</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">weight_decay_rate<span class="opacity-60">: float = 0.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">power<span class="opacity-60">: float = 1.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">include_in_weight_decay<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.init_lr" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.init_lr"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>init_lr</strong> (<code>float</code>) —
The desired learning rate at the end of the warmup phase.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.num_train_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.num_train_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_train_steps</strong> (<code>int</code>) —
The total number of training steps.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.num_warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.num_warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_warmup_steps</strong> (<code>int</code>) —
The number of warmup steps.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.min_lr_ratio" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.min_lr_ratio"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_lr_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The final learning rate at the end of the linear decay will be <code>init_lr * min_lr_ratio</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.adam_beta1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.adam_beta1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_beta1</strong> (<code>float</code>, <em>optional</em>, defaults to 0.9) —
The beta1 to use in Adam.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.adam_beta2" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.adam_beta2"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_beta2</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) —
The beta2 to use in Adam.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.adam_epsilon" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.adam_epsilon"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>adam_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-8) —
The epsilon to use in Adam.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.weight_decay_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.weight_decay_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>weight_decay_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0) —
The weight decay to use.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.power" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.power"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>power</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
The power to use for PolynomialDecay.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.create_optimizer.include_in_weight_decay" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.create_optimizer.include_in_weight_decay"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>include_in_weight_decay</strong> (<code>List[str]</code>, <em>optional</em>) —
List of the parameter names (or re patterns) to apply weight decay to. If none is passed, weight decay is
applied to all parameters except bias and layer norm parameters.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay.</p></div>
<h2 class="relative group"><a id="schedules" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#schedules"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Schedules
</span></h2>
<h3 class="relative group"><a id="transformers.SchedulerType" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SchedulerType"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Learning Rate Schedules (Pytorch)
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SchedulerType"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">SchedulerType</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.SchedulerType" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SchedulerType"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_utils.py#L301" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">names<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">qualname<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60"> = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>An enumeration.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.get_scheduler"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.get_scheduler</span></h4><!-- HTML_TAG_END -->
<a id="transformers.get_scheduler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.get_scheduler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L233" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">name<span class="opacity-60">: typing.Union[str, transformers.trainer_utils.SchedulerType]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60">: Optimizer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_warmup_steps<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_scheduler.name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_scheduler.name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>name</strong> (<code>str</code> or <code>SchedulerType</code>) —
The name of the scheduler to use.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_scheduler.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_scheduler.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>torch.optim.Optimizer</code>) —
The optimizer that will be used during training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_scheduler.num_warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_scheduler.num_warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_warmup_steps</strong> (<code>int</code>, <em>optional</em>) —
The number of warmup steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it’s unset and the scheduler type requires it.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_scheduler.num_training_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_scheduler.num_training_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_training_steps</strong> (`int“, <em>optional</em>) —
The number of training steps to do. This is not required by all schedulers (hence the argument being
optional), the function will raise an error if it’s unset and the scheduler type requires it.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Unified API to get any scheduler from its name.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.get_constant_schedule"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.get_constant_schedule</span></h4><!-- HTML_TAG_END -->
<a id="transformers.get_constant_schedule" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.get_constant_schedule"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L34" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60">: Optimizer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_epoch<span class="opacity-60">: int = -1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_constant_schedule.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_constant_schedule.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_constant_schedule.last_epoch" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_constant_schedule.last_epoch"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Create a schedule with a constant learning rate, using the learning rate set in optimizer.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.get_constant_schedule_with_warmup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.get_constant_schedule_with_warmup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.get_constant_schedule_with_warmup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.get_constant_schedule_with_warmup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L50" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60">: Optimizer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_warmup_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_epoch<span class="opacity-60">: int = -1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_constant_schedule_with_warmup.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_constant_schedule_with_warmup.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_constant_schedule_with_warmup.num_warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_constant_schedule_with_warmup.num_warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_constant_schedule_with_warmup.last_epoch" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_constant_schedule_with_warmup.last_epoch"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
increases linearly between 0 and the initial lr set in the optimizer.</p></div>
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_constant_schedule.png">
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.get_cosine_schedule_with_warmup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.get_cosine_schedule_with_warmup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.get_cosine_schedule_with_warmup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.get_cosine_schedule_with_warmup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L104" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60">: Optimizer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_warmup_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_cycles<span class="opacity-60">: float = 0.5</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_epoch<span class="opacity-60">: int = -1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_schedule_with_warmup.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_schedule_with_warmup.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_schedule_with_warmup.num_warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_schedule_with_warmup.num_warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_schedule_with_warmup.num_training_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_schedule_with_warmup.num_training_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_training_steps</strong> (<code>int</code>) —
The total number of training steps.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_schedule_with_warmup.num_cycles" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_schedule_with_warmup.num_cycles"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_cycles</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) —
The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
following a half-cosine).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_schedule_with_warmup.last_epoch" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_schedule_with_warmup.last_epoch"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.</p></div>
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_schedule.png">
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.get_cosine_with_hard_restarts_schedule_with_warmup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.get_cosine_with_hard_restarts_schedule_with_warmup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.get_cosine_with_hard_restarts_schedule_with_warmup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.get_cosine_with_hard_restarts_schedule_with_warmup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L138" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60">: Optimizer</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_warmup_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_cycles<span class="opacity-60">: int = 1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_epoch<span class="opacity-60">: int = -1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_with_hard_restarts_schedule_with_warmup.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_with_hard_restarts_schedule_with_warmup.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_with_hard_restarts_schedule_with_warmup.num_warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_with_hard_restarts_schedule_with_warmup.num_warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_with_hard_restarts_schedule_with_warmup.num_training_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_with_hard_restarts_schedule_with_warmup.num_training_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_training_steps</strong> (<code>int</code>) —
The total number of training steps.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_with_hard_restarts_schedule_with_warmup.num_cycles" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_with_hard_restarts_schedule_with_warmup.num_cycles"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_cycles</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The number of hard restarts to use.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_cosine_with_hard_restarts_schedule_with_warmup.last_epoch" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_cosine_with_hard_restarts_schedule_with_warmup.last_epoch"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases
linearly between 0 and the initial lr set in the optimizer.</p></div>
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_cosine_hard_restarts_schedule.png">
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.get_linear_schedule_with_warmup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.get_linear_schedule_with_warmup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.get_linear_schedule_with_warmup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.get_linear_schedule_with_warmup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L75" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_warmup_steps<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_epoch<span class="opacity-60"> = -1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_linear_schedule_with_warmup.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_linear_schedule_with_warmup.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_linear_schedule_with_warmup.num_warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_linear_schedule_with_warmup.num_warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_linear_schedule_with_warmup.num_training_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_linear_schedule_with_warmup.num_training_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_training_steps</strong> (<code>int</code>) —
The total number of training steps.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_linear_schedule_with_warmup.last_epoch" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_linear_schedule_with_warmup.last_epoch"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after
a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer.</p></div>
<img alt="" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/warmup_linear_schedule.png">
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.get_polynomial_decay_schedule_with_warmup"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.get_polynomial_decay_schedule_with_warmup</span></h4><!-- HTML_TAG_END -->
<a id="transformers.get_polynomial_decay_schedule_with_warmup" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.get_polynomial_decay_schedule_with_warmup"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization.py#L173" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_warmup_steps<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_training_steps<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">lr_end<span class="opacity-60"> = 1e-07</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">power<span class="opacity-60"> = 1.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_epoch<span class="opacity-60"> = -1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_polynomial_decay_schedule_with_warmup.optimizer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_polynomial_decay_schedule_with_warmup.optimizer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>optimizer</strong> (<code>Optimizer</code>) —
The optimizer for which to schedule the learning rate.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_polynomial_decay_schedule_with_warmup.num_warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_polynomial_decay_schedule_with_warmup.num_warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup phase.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_polynomial_decay_schedule_with_warmup.num_training_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_polynomial_decay_schedule_with_warmup.num_training_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_training_steps</strong> (<code>int</code>) —
The total number of training steps.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_polynomial_decay_schedule_with_warmup.lr_end" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_polynomial_decay_schedule_with_warmup.lr_end"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>lr_end</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-7) —
The end LR.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_polynomial_decay_schedule_with_warmup.power" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_polynomial_decay_schedule_with_warmup.power"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>power</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Power factor.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.get_polynomial_decay_schedule_with_warmup.last_epoch" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.get_polynomial_decay_schedule_with_warmup.last_epoch"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_epoch</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The index of the last epoch when resuming training.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by <em>lr_end</em>, after a warmup period during which it increases linearly from 0 to the
initial lr set in the optimizer.</p>
<p>Note: <em>power</em> defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT
implementation at
<a href="https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37" rel="nofollow">https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37</a></p></div>
<h3 class="relative group"><a id="transformers.WarmUp" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.WarmUp"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Warmup (TensorFlow)
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.WarmUp"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">WarmUp</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.WarmUp" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.WarmUp"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L24" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">initial_learning_rate<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decay_schedule_fn<span class="opacity-60">: typing.Callable</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">warmup_steps<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">power<span class="opacity-60">: float = 1.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">name<span class="opacity-60">: str = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.WarmUp.initial_learning_rate" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.WarmUp.initial_learning_rate"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>initial_learning_rate</strong> (<code>float</code>) —
The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end
of the warmup).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.WarmUp.decay_schedule_fn" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.WarmUp.decay_schedule_fn"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decay_schedule_fn</strong> (<code>Callable</code>) —
The schedule function to apply after the warmup for the rest of training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.WarmUp.warmup_steps" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.WarmUp.warmup_steps"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>warmup_steps</strong> (<code>int</code>) —
The number of steps for the warmup part of training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.WarmUp.power" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.WarmUp.power"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>power</strong> (<code>float</code>, <em>optional</em>, defaults to 1) —
The power to use for the polynomial warmup (defaults is a linear warmup).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.WarmUp.name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.WarmUp.name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>name</strong> (<code>str</code>, <em>optional</em>) —
Optional name prefix for the returned tensors during the schedule.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Applies a warmup schedule on a given learning rate decay schedule.</p></div>
<h2 class="relative group"><a id="gradient-strategies" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#gradient-strategies"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Gradient Strategies
</span></h2>
<h3 class="relative group"><a id="transformers.GradientAccumulator" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.GradientAccumulator"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>GradientAccumulator (TensorFlow)
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.GradientAccumulator"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">GradientAccumulator</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.GradientAccumulator" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.GradientAccumulator"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L282" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Gradient accumulation utility. When used with a distribution strategy, the accumulator should be called in a
replica context. Gradients will be accumulated locally on each replica and without synchronization. Users should
then call <code>.gradients</code>, scale the gradients if required, and pass the result to <code>apply_gradients</code>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.GradientAccumulator.reset"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>reset</span></h4><!-- HTML_TAG_END -->
<a id="transformers.GradientAccumulator.reset" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.GradientAccumulator.reset"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/optimization_tf.py#L344" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Resets the accumulated gradients on the current replica.</p></div></div>
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<h1 class="relative group"><a id="deepspeed-integration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#deepspeed-integration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DeepSpeed Integration
</span></h1>
<p><a href="https://github.com/microsoft/DeepSpeed" rel="nofollow">DeepSpeed</a> implements everything described in the <a href="https://arxiv.org/abs/1910.02054" rel="nofollow">ZeRO paper</a>. Currently it provides full support for:</p>
<ol><li>Optimizer state partitioning (ZeRO stage 1)</li>
<li>Gradient partitioning (ZeRO stage 2)</li>
<li>Parameter partitioning (ZeRO stage 3)</li>
<li>Custom mixed precision training handling</li>
<li>A range of fast CUDA-extension-based optimizers</li>
<li>ZeRO-Offload to CPU and NVMe</li></ol>
<p>ZeRO-Offload has its own dedicated paper: <a href="https://arxiv.org/abs/2101.06840" rel="nofollow">ZeRO-Offload: Democratizing Billion-Scale Model Training</a>. And NVMe-support is described in the paper <a href="https://arxiv.org/abs/2104.07857" rel="nofollow">ZeRO-Infinity: Breaking the GPU
Memory Wall for Extreme Scale Deep Learning</a>.</p>
<p>DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference.</p>
<p>DeepSpeed ZeRO-3 can be used for inference as well, since it allows huge models to be loaded on multiple GPUs, which
won’t be possible on a single GPU.</p>
<p>🤗 Transformers integrates <a href="https://github.com/microsoft/DeepSpeed" rel="nofollow">DeepSpeed</a> via 2 options:</p>
<ol><li>Integration of the core DeepSpeed features via <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>. This is an everything-done-for-you type
of integration - just supply your custom config file or use our template and you have nothing else to do. Most of
this document is focused on this feature.</li>
<li>If you don’t use <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> and want to use your own Trainer where you integrated DeepSpeed
yourself, core functionality functions like <code>from_pretrained</code> and <code>from_config</code> include integration of essential
parts of DeepSpeed like <code>zero.Init</code> for ZeRO stage 3 and higher. To tap into this feature read the docs on
<a href="#deepspeed-non-trainer-integration">deepspeed-non-trainer-integration</a>.</li></ol>
<p>What is integrated:</p>
<p>Training:</p>
<ol><li>DeepSpeed ZeRO training supports the full ZeRO stages 1, 2 and 3 with ZeRO-Infinity (CPU and NVME offload).</li></ol>
<p>Inference:</p>
<ol><li>DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but
it doesn’t use an optimizer and a lr scheduler and only stage 3 is relevant. For more details see:
<a href="#deepspeed-zero-inference">deepspeed-zero-inference</a>.</li></ol>
<p>There is also DeepSpeed Inference - this is a totally different technology which uses Tensor Parallelism instead of
ZeRO (coming soon).</p>
<a id="deepspeed-trainer-integration"></a>
<h2 class="relative group"><a id="trainer-deepspeed-integration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainer-deepspeed-integration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainer Deepspeed Integration
</span></h2>
<a id="deepspeed-installation"></a>
<h3 class="relative group"><a id="installation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#installation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Installation
</span></h3>
<p>Install the library via pypi:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START -->pip install deepspeed<!-- HTML_TAG_END --></pre></div>
<p>or via <code>transformers</code>’ <code>extras</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->pip install transformers[deepspeed]<!-- HTML_TAG_END --></pre></div>
<p>or find more details on <a href="https://github.com/microsoft/deepspeed#installation" rel="nofollow">the DeepSpeed’s GitHub page</a> and
<a href="https://www.deepspeed.ai/tutorials/advanced-install/" rel="nofollow">advanced install</a>.</p>
<p>If you’re still struggling with the build, first make sure to read <a href="#zero-install-notes">zero-install-notes</a>.</p>
<p>If you don’t prebuild the extensions and rely on them to be built at run time and you tried all of the above solutions
to no avail, the next thing to try is to pre-build the modules before installing them.</p>
<p>To make a local build for DeepSpeed:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->git <span class="hljs-built_in">clone</span> https://github.com/microsoft/DeepSpeed/
<span class="hljs-built_in">cd</span> DeepSpeed
<span class="hljs-built_in">rm</span> -rf build
TORCH_CUDA_ARCH_LIST=<span class="hljs-string">"8.6"</span> DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 pip install . \
--global-option=<span class="hljs-string">"build_ext"</span> --global-option=<span class="hljs-string">"-j8"</span> --no-cache -v \
--disable-pip-version-check 2>&1 | <span class="hljs-built_in">tee</span> build.log<!-- HTML_TAG_END --></pre></div>
<p>If you intend to use NVMe offload you will also need to include <code>DS_BUILD_AIO=1</code> in the instructions above (and also
install <em>libaio-dev</em> system-wide).</p>
<p>Edit <code>TORCH_CUDA_ARCH_LIST</code> to insert the code for the architectures of the GPU cards you intend to use. Assuming all
your cards are the same you can get the arch via:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->CUDA_VISIBLE_DEVICES=0 python -c <span class="hljs-string">"import torch; print(torch.cuda.get_device_capability())"</span><!-- HTML_TAG_END --></pre></div>
<p>So if you get <code>8, 6</code>, then use <code>TORCH_CUDA_ARCH_LIST="8.6"</code>. If you have multiple different cards, you can list all
of them like so <code>TORCH_CUDA_ARCH_LIST="6.1;8.6"</code></p>
<p>If you need to use the same setup on multiple machines, make a binary wheel:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->git <span class="hljs-built_in">clone</span> https://github.com/microsoft/DeepSpeed/
<span class="hljs-built_in">cd</span> DeepSpeed
<span class="hljs-built_in">rm</span> -rf build
TORCH_CUDA_ARCH_LIST=<span class="hljs-string">"8.6"</span> DS_BUILD_CPU_ADAM=1 DS_BUILD_UTILS=1 \
python setup.py build_ext -j8 bdist_wheel<!-- HTML_TAG_END --></pre></div>
<p>it will generate something like <code>dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl</code> which now you can install
as <code>pip install deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl</code> locally or on any other machine.</p>
<p>Again, remember to ensure to adjust <code>TORCH_CUDA_ARCH_LIST</code> to the target architectures.</p>
<p>You can find the complete list of NVIDIA GPUs and their corresponding <strong>Compute Capabilities</strong> (same as arch in this
context) <a href="https://developer.nvidia.com/cuda-gpus" rel="nofollow">here</a>.</p>
<p>You can check the archs pytorch was built with using:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python -c <span class="hljs-string">"import torch; print(torch.cuda.get_arch_list())"</span><!-- HTML_TAG_END --></pre></div>
<p>Here is how to find out the arch for one of the installed GPUs. For example, for GPU 0:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->CUDA_VISIBLE_DEVICES=0 python -c <span class="hljs-string">"import torch; \
print(torch.cuda.get_device_properties(torch.device('cuda')))"</span><!-- HTML_TAG_END --></pre></div>
<p>If the output is:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->_CudaDeviceProperties(name=<span class="hljs-string">'GeForce RTX 3090'</span>, major=8, minor=6, total_memory=24268MB, multi_processor_count=82)<!-- HTML_TAG_END --></pre></div>
<p>then you know that this card’s arch is <code>8.6</code>.</p>
<p>You can also leave <code>TORCH_CUDA_ARCH_LIST</code> out completely and then the build program will automatically query the
architecture of the GPUs the build is made on. This may or may not match the GPUs on the target machines, that’s why
it’s best to specify the desired archs explicitly.</p>
<p>If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of
<a href="https://github.com/microsoft/DeepSpeed/issues" rel="nofollow">Deepspeed</a>,</p>
<a id="deepspeed-multi-gpu"></a>
<h3 class="relative group"><a id="deployment-with-multiple-gpus" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#deployment-with-multiple-gpus"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Deployment with multiple GPUs
</span></h3>
<p>To deploy this feature with multiple GPUs adjust the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments as
following:</p>
<ol><li>replace <code>python -m torch.distributed.launch</code> with <code>deepspeed</code>.</li>
<li>add a new argument <code>--deepspeed ds_config.json</code>, where <code>ds_config.json</code> is the DeepSpeed configuration file as
documented <a href="https://www.deepspeed.ai/docs/config-json/" rel="nofollow">here</a>. The file naming is up to you.</li></ol>
<p>Therefore, if your original command line looked as follows:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python -m torch.distributed.launch --nproc_per_node=2 your_program.py <normal cl args><!-- HTML_TAG_END --></pre></div>
<p>Now it should be:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->deepspeed --num_gpus=2 your_program.py <normal cl args> --deepspeed ds_config.json<!-- HTML_TAG_END --></pre></div>
<p>Unlike, <code>torch.distributed.launch</code> where you have to specify how many GPUs to use with <code>--nproc_per_node</code>, with the
<code>deepspeed</code> launcher you don’t have to use the corresponding <code>--num_gpus</code> if you want all of your GPUs used. The
full details on how to configure various nodes and GPUs can be found <a href="https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node" rel="nofollow">here</a>.</p>
<p>In fact, you can continue using <code>-m torch.distributed.launch</code> with DeepSpeed as long as you don’t need to use
<code>deepspeed</code> launcher-specific arguments. Typically if you don’t need a multi-node setup you’re not required to use
the <code>deepspeed</code> launcher. But since in the DeepSpeed documentation it’ll be used everywhere, for consistency we will
use it here as well.</p>
<p>Here is an example of running <code>run_translation.py</code> under DeepSpeed deploying all available GPUs:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->deepspeed examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config <span class="hljs-string">"ro-en"</span> \
--source_lang en --target_lang ro<!-- HTML_TAG_END --></pre></div>
<p>Note that in the DeepSpeed documentation you are likely to see <code>--deepspeed --deepspeed_config ds_config.json</code> - i.e.
two DeepSpeed-related arguments, but for the sake of simplicity, and since there are already so many arguments to deal
with, we combined the two into a single argument.</p>
<p>For some practical usage examples, please, see this <a href="https://github.com/huggingface/transformers/issues/8771#issuecomment-759248400" rel="nofollow">post</a>.</p>
<a id="deepspeed-one-gpu"></a>
<h3 class="relative group"><a id="deployment-with-one-gpu" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#deployment-with-one-gpu"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Deployment with one GPU
</span></h3>
<p>To deploy DeepSpeed with one GPU adjust the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments as follows:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero2.json \
--model_name_or_path t5-small --per_device_train_batch_size 1 \
--output_dir output_dir --overwrite_output_dir --fp16 \
--do_train --max_train_samples 500 --num_train_epochs 1 \
--dataset_name wmt16 --dataset_config <span class="hljs-string">"ro-en"</span> \
--source_lang en --target_lang ro<!-- HTML_TAG_END --></pre></div>
<p>This is almost the same as with multiple-GPUs, but here we tell DeepSpeed explicitly to use just one GPU via
<code>--num_gpus=1</code>. By default, DeepSpeed deploys all GPUs it can see on the given node. If you have only 1 GPU to start
with, then you don’t need this argument. The following <a href="https://www.deepspeed.ai/getting-started/#resource-configuration-multi-node" rel="nofollow">documentation</a> discusses the launcher options.</p>
<p>Why would you want to use DeepSpeed with just one GPU?</p>
<ol><li>It has a ZeRO-offload feature which can delegate some computations and memory to the host’s CPU and RAM, and thus
leave more GPU resources for model’s needs - e.g. larger batch size, or enabling a fitting of a very big model which
normally won’t fit.</li>
<li>It provides a smart GPU memory management system, that minimizes memory fragmentation, which again allows you to fit
bigger models and data batches.</li></ol>
<p>While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU
with DeepSpeed is to have at least the following configuration in the configuration file:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_partitions"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_scatter"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>which enables optimizer offload and some other important features. You may experiment with the buffer sizes, you will
find more details in the discussion below.</p>
<p>For a practical usage example of this type of deployment, please, see this <a href="https://github.com/huggingface/transformers/issues/8771#issuecomment-759176685" rel="nofollow">post</a>.</p>
<p>You may also try the ZeRO-3 with CPU and NVMe offload as explained further in this document.</p>
<p>Notes:</p>
<ul><li><p>if you need to run on a specific GPU, which is different from GPU 0, you can’t use <code>CUDA_VISIBLE_DEVICES</code> to limit
the visible scope of available GPUs. Instead, you have to use the following syntax:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->deepspeed --include localhost:1 examples/pytorch/translation/run_translation.py ...<!-- HTML_TAG_END --></pre></div>
<p>In this example, we tell DeepSpeed to use GPU 1 (second gpu).</p></li></ul>
<a id="deepspeed-notebook"></a>
<h3 class="relative group"><a id="deployment-in-notebooks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#deployment-in-notebooks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Deployment in Notebooks
</span></h3>
<p>The problem with running notebook cells as a script is that there is no normal <code>deepspeed</code> launcher to rely on, so
under certain setups we have to emulate it.</p>
<p>If you’re using only 1 GPU, here is how you’d have to adjust your training code in the notebook to use DeepSpeed.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-comment"># DeepSpeed requires a distributed environment even when only one process is used.</span>
<span class="hljs-comment"># This emulates a launcher in the notebook</span>
<span class="hljs-keyword">import</span> os
os.environ[<span class="hljs-string">"MASTER_ADDR"</span>] = <span class="hljs-string">"localhost"</span>
os.environ[<span class="hljs-string">"MASTER_PORT"</span>] = <span class="hljs-string">"9994"</span> <span class="hljs-comment"># modify if RuntimeError: Address already in use</span>
os.environ[<span class="hljs-string">"RANK"</span>] = <span class="hljs-string">"0"</span>
os.environ[<span class="hljs-string">"LOCAL_RANK"</span>] = <span class="hljs-string">"0"</span>
os.environ[<span class="hljs-string">"WORLD_SIZE"</span>] = <span class="hljs-string">"1"</span>
<span class="hljs-comment"># Now proceed as normal, plus pass the deepspeed config file</span>
training_args = TrainingArguments(..., deepspeed=<span class="hljs-string">"ds_config_zero3.json"</span>)
trainer = Trainer(...)
trainer.train()<!-- HTML_TAG_END --></pre></div>
<p>Note: <code>...</code> stands for the normal arguments that you’d pass to the functions.</p>
<p>If you want to use more than 1 GPU, you must use a multi-process environment for DeepSpeed to work. That is, you have
to use the launcher for that purpose and this cannot be accomplished by emulating the distributed environment presented
at the beginning of this section.</p>
<p>If you want to create the config file on the fly in the notebook in the current directory, you could have a dedicated
cell with:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->%%bash
cat <<<span class="hljs-string">'EOT'</span> > ds_config_zero3.json
{
<span class="hljs-string">"fp16"</span>: {
<span class="hljs-string">"enabled"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"loss_scale"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"loss_scale_window"</span>: <span class="hljs-number">1000</span>,
<span class="hljs-string">"initial_scale_power"</span>: <span class="hljs-number">16</span>,
<span class="hljs-string">"hysteresis"</span>: <span class="hljs-number">2</span>,
<span class="hljs-string">"min_loss_scale"</span>: <span class="hljs-number">1</span>
},
<span class="hljs-string">"optimizer"</span>: {
<span class="hljs-string">"type"</span>: <span class="hljs-string">"AdamW"</span>,
<span class="hljs-string">"params"</span>: {
<span class="hljs-string">"lr"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"betas"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"eps"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"weight_decay"</span>: <span class="hljs-string">"auto"</span>
}
},
<span class="hljs-string">"scheduler"</span>: {
<span class="hljs-string">"type"</span>: <span class="hljs-string">"WarmupLR"</span>,
<span class="hljs-string">"params"</span>: {
<span class="hljs-string">"warmup_min_lr"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"warmup_max_lr"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"warmup_num_steps"</span>: <span class="hljs-string">"auto"</span>
}
},
<span class="hljs-string">"zero_optimization"</span>: {
<span class="hljs-string">"stage"</span>: <span class="hljs-number">3</span>,
<span class="hljs-string">"offload_optimizer"</span>: {
<span class="hljs-string">"device"</span>: <span class="hljs-string">"cpu"</span>,
<span class="hljs-string">"pin_memory"</span>: true
},
<span class="hljs-string">"offload_param"</span>: {
<span class="hljs-string">"device"</span>: <span class="hljs-string">"cpu"</span>,
<span class="hljs-string">"pin_memory"</span>: true
},
<span class="hljs-string">"overlap_comm"</span>: true,
<span class="hljs-string">"contiguous_gradients"</span>: true,
<span class="hljs-string">"sub_group_size"</span>: <span class="hljs-number">1e9</span>,
<span class="hljs-string">"reduce_bucket_size"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"stage3_prefetch_bucket_size"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"stage3_param_persistence_threshold"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"stage3_max_live_parameters"</span>: <span class="hljs-number">1e9</span>,
<span class="hljs-string">"stage3_max_reuse_distance"</span>: <span class="hljs-number">1e9</span>,
<span class="hljs-string">"stage3_gather_16bit_weights_on_model_save"</span>: true
},
<span class="hljs-string">"gradient_accumulation_steps"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"gradient_clipping"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"steps_per_print"</span>: <span class="hljs-number">2000</span>,
<span class="hljs-string">"train_batch_size"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"train_micro_batch_size_per_gpu"</span>: <span class="hljs-string">"auto"</span>,
<span class="hljs-string">"wall_clock_breakdown"</span>: false
}
EOT<!-- HTML_TAG_END --></pre></div>
<p>If the training script is in a normal file and not in the notebook cells, you can launch <code>deepspeed</code> normally via
shell from a cell. For example, to use <code>run_translation.py</code> you would launch it with:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->!git clone https://github.com/huggingface/transformers
!cd transformers; deepspeed examples/pytorch/translation/run_translation.py ...<!-- HTML_TAG_END --></pre></div>
<p>or with <code>%%bash</code> magic, where you can write a multi-line code for the shell program to run:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->%%bash
git clone https://github.com/huggingface/transformers
cd transformers
deepspeed examples/pytorch/translation/run_translation.py ...<!-- HTML_TAG_END --></pre></div>
<p>In such case you don’t need any of the code presented at the beginning of this section.</p>
<p>Note: While <code>%%bash</code> magic is neat, but currently it buffers the output so you won’t see the logs until the process
completes.</p>
<a id="deepspeed-config"></a>
<h3 class="relative group"><a id="configuration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#configuration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Configuration
</span></h3>
<p>For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer
to the <a href="https://www.deepspeed.ai/docs/config-json/" rel="nofollow">following documentation</a>.</p>
<p>You can find dozens of DeepSpeed configuration examples that address various practical needs in <a href="https://github.com/microsoft/DeepSpeedExamples" rel="nofollow">the DeepSpeedExamples
repo</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->git <span class="hljs-built_in">clone</span> https://github.com/microsoft/DeepSpeedExamples
<span class="hljs-built_in">cd</span> DeepSpeedExamples
find . -name <span class="hljs-string">'*json'</span><!-- HTML_TAG_END --></pre></div>
<p>Continuing the code from above, let’s say you’re looking to configure the Lamb optimizer. So you can search through the
example <code>.json</code> files with:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->grep -i Lamb $(find . -name <span class="hljs-string">'*json'</span>)<!-- HTML_TAG_END --></pre></div>
<p>Some more examples are to be found in the <a href="https://github.com/microsoft/DeepSpeed" rel="nofollow">main repo</a> as well.</p>
<p>When using DeepSpeed you always need to supply a DeepSpeed configuration file, yet some configuration parameters have
to be configured via the command line. You will find the nuances in the rest of this guide.</p>
<p>To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features,
including optimizer states cpu offload, uses <code>AdamW</code> optimizer and <code>WarmupLR</code> scheduler and will enable mixed
precision training if <code>--fp16</code> is passed:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"AdamW"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"betas"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"eps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"weight_decay"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_partitions"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_scatter"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"gradient_accumulation_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"gradient_clipping"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_batch_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_micro_batch_size_per_gpu"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>When you execute the program, DeepSpeed will log the configuration it received from the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>
to the console, so you can see exactly what was the final configuration passed to it.</p>
<a id="deepspeed-config-passing"></a>
<h3 class="relative group"><a id="passing-configuration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#passing-configuration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Passing Configuration
</span></h3>
<p>As discussed in this document normally the DeepSpeed configuration is passed as a path to a json file, but if you’re
not using the command line interface to configure the training, and instead instantiate the
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> via <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> then for the <code>deepspeed</code> argument you can
pass a nested <code>dict</code>. This allows you to create the configuration on the fly and doesn’t require you to write it to
the file system before passing it to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</p>
<p>To summarize you can do:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->TrainingArguments(..., deepspeed=<span class="hljs-string">"/path/to/ds_config.json"</span>)<!-- HTML_TAG_END --></pre></div>
<p>or:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->ds_config_dict = <span class="hljs-built_in">dict</span>(scheduler=scheduler_params, optimizer=optimizer_params)
TrainingArguments(..., deepspeed=ds_config_dict)<!-- HTML_TAG_END --></pre></div>
<a id="deepspeed-config-shared"></a>
<h3 class="relative group"><a id="shared-configuration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#shared-configuration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Shared Configuration
</span></h3>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This section is a must-read</p></div>
<p>Some configuration values are required by both the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> and DeepSpeed to function correctly,
therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those
via the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments.</p>
<p>Additionally, some configuration values are derived automatically based on the model’s configuration, so instead of
remembering to manually adjust multiple values, it’s the best to let the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> do the majority
of configuration for you.</p>
<p>Therefore, in the rest of this guide you will find a special configuration value: <code>auto</code>, which when set will be
automatically replaced with the correct or most efficient value. Please feel free to choose to ignore this
recommendation and set the values explicitly, in which case be very careful that your the
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> arguments and DeepSpeed configurations agree. For example, are you using the same
learning rate, or batch size, or gradient accumulation settings? if these mismatch the training may fail in very
difficult to detect ways. You have been warned.</p>
<p>There are multiple other values that are specific to DeepSpeed-only and those you will have to set manually to suit
your needs.</p>
<p>In your own programs, you can also use the following approach if you’d like to modify the DeepSpeed config as a master
and configure <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> based on that. The steps are:</p>
<ol><li>Create or load the DeepSpeed configuration to be used as a master configuration</li>
<li>Create the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> object based on these values</li></ol>
<p>Do note that some values, such as <code>scheduler.params.total_num_steps</code> are calculated by
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> during <code>train</code>, but you can of course do the math yourself.</p>
<a id="deepspeed-zero"></a>
<h3 class="relative group"><a id="zero" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO
</span></h3>
<p><a href="https://www.deepspeed.ai/tutorials/zero/" rel="nofollow">Zero Redundancy Optimizer (ZeRO)</a> is the workhorse of DeepSpeed. It
supports 3 different levels (stages) of optimization. The first one is not quite interesting for scalability purposes,
therefore this document focuses on stages 2 and 3. Stage 3 is further improved by the latest addition of ZeRO-Infinity.
You will find more indepth information in the DeepSpeed documentation.</p>
<p>The <code>zero_optimization</code> section of the configuration file is the most important part (<a href="https://www.deepspeed.ai/docs/config-json/#zero-optimizations-for-fp16-training" rel="nofollow">docs</a>), since that is where you define
which ZeRO stages you want to enable and how to configure them. You will find the explanation for each parameter in the
DeepSpeed docs.</p>
<p>This section has to be configured exclusively via DeepSpeed configuration - the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> provides
no equivalent command line arguments.</p>
<p>Note: currently DeepSpeed doesn’t validate parameter names, so if you misspell any, it’ll use the default setting for
the parameter that got misspelled. You can watch the DeepSpeed engine start up log messages to see what values it is
going to use.</p>
<a id="deepspeed-zero2-config"></a>
<h4 class="relative group"><a id="zero2-config" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero2-config"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO-2 Config
</span></h4>
<p>The following is an example of configuration for ZeRO stage 2:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_partitions"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">5e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_scatter"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">5e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p><strong>Performance tuning:</strong></p>
<ul><li>enabling <code>offload_optimizer</code> should reduce GPU RAM usage (it requires <code>"stage": 2</code>)</li>
<li><code>"overlap_comm": true</code> trades off increased GPU RAM usage to lower all-reduce latency. <code>overlap_comm</code> uses 4.5x
the <code>allgather_bucket_size</code> and <code>reduce_bucket_size</code> values. So if they are set to 5e8, this requires a 9GB
footprint (<code>5e8 x 2Bytes x 2 x 4.5</code>). Therefore, if you have a GPU with 8GB or less RAM, to avoid getting
OOM-errors you will need to reduce those parameters to about <code>2e8</code>, which would require 3.6GB. You will want to do
the same on larger capacity GPU as well, if you’re starting to hit OOM.</li>
<li>when reducing these buffers you’re trading communication speed to avail more GPU RAM. The smaller the buffer size is,
the slower the communication gets, and the more GPU RAM will be available to other tasks. So if a bigger batch size is
important, getting a slightly slower training time could be a good trade.</li></ul>
<p>Additionally, <code>deepspeed==0.4.4</code> added a new option <code>round_robin_gradients</code> which you can enable with:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"round_robin_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>This is a stage 2 optimization for CPU offloading that parallelizes gradient copying to CPU memory among ranks by fine-grained gradient partitioning. Performance benefit grows with gradient accumulation steps (more copying between optimizer steps) or GPU count (increased parallelism).</p>
<a id="deepspeed-zero3-config"></a>
<h4 class="relative group"><a id="zero3-config" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero3-config"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO-3 Config
</span></h4>
<p>The following is an example of configuration for ZeRO stage 3:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_param"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"sub_group_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_prefetch_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_param_persistence_threshold"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_live_parameters"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_reuse_distance"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_gather_16bit_weights_on_model_save"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>If you are getting OOMs, because your model or activations don’t fit into the GPU memory and you have unutilized CPU
memory offloading the optimizer states and parameters to CPU memory with <code>"device": "cpu"</code> may solve this limitation.
If you don’t want to offload to CPU memory, use <code>none</code> instead of <code>cpu</code> for the <code>device</code> entry. Offloading to
NVMe is discussed further down.</p>
<p>Pinned memory is enabled with <code>pin_memory</code> set to <code>true</code>. This feature can improve the throughput at the cost of
making less memory available to other processes. Pinned memory is set aside to the specific process that requested it
and its typically accessed much faster than normal CPU memory.</p>
<p><strong>Performance tuning:</strong></p>
<ul><li><code>stage3_max_live_parameters</code>: <code>1e9</code></li>
<li><code>stage3_max_reuse_distance</code>: <code>1e9</code></li></ul>
<p>If hitting OOM reduce <code>stage3_max_live_parameters</code> and <code>stage3_max_reuse_distance</code>. They should have minimal impact
on performance unless you are doing activation checkpointing. <code>1e9</code> would consume ~2GB. The memory is shared by
<code>stage3_max_live_parameters</code> and <code>stage3_max_reuse_distance</code>, so it’s not additive, it’s just 2GB total.</p>
<p><code>stage3_max_live_parameters</code> is the upper limit on how many full parameters you want to keep on the GPU at any given
time. “reuse distance” is a metric we are using to figure out when will a parameter be used again in the future, and we
use the <code>stage3_max_reuse_distance</code> to decide whether to throw away the parameter or to keep it. If a parameter is
going to be used again in near future (less than <code>stage3_max_reuse_distance</code>) then we keep it to reduce communication
overhead. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and
backward passes a a single layer granularity and want to keep the parameter in the forward recompute till the backward</p>
<p>The following configuration values depend on the model’s hidden size:</p>
<ul><li><code>reduce_bucket_size</code>: <code>hidden_size*hidden_size</code></li>
<li><code>stage3_prefetch_bucket_size</code>: <code>0.9 * hidden_size * hidden_size</code></li>
<li><code>stage3_param_persistence_threshold</code>: <code>10 * hidden_size</code></li></ul>
<p>therefore set these values to <code>auto</code> and the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will automatically assign the recommended
values. But, of course, feel free to set these explicitly as well.</p>
<p><code>stage3_gather_16bit_weights_on_model_save</code> enables model fp16 weights consolidation when model gets saved. With large
models and multiple GPUs this is an expensive operation both in terms of memory and speed. It’s currently required if
you plan to resume the training. Watch out for future updates that will remove this limitation and make things more
flexible.</p>
<p>If you’re migrating from ZeRO-2 configuration note that <code>allgather_partitions</code>, <code>allgather_bucket_size</code> and
<code>reduce_scatter</code> configuration parameters are not used in ZeRO-3. If you keep these in the config file they will just
be ignored.</p>
<ul><li><code>sub_group_size</code>: <code>1e9</code></li></ul>
<p><code>sub_group_size</code> controls the granularity in which parameters are updated during optimizer steps. Parameters are
grouped into buckets of <code>sub_group_size</code> and each buckets is updated one at a time. When used with NVMe offload in
ZeRO-Infinity, <code>sub_group_size</code> therefore controls the granularity in which model states are moved in and out of CPU
memory from NVMe during the optimizer step. This prevents running out of CPU memory for extremely large models.</p>
<p>You can leave <code>sub_group_size</code> to its default value of <em>1e9</em> when not using NVMe offload. You may want to change its
default value in the following cases:</p>
<ol><li>Running into OOM during optimizer step: Reduce <code>sub_group_size</code> to reduce memory utilization of temporary buffers</li>
<li>Optimizer Step is taking a long time: Increase <code>sub_group_size</code> to improve bandwidth utilization as a result of
the increased data buffers.</li></ol>
<a id="deepspeed-nvme"></a>
<h3 class="relative group"><a id="nvme-support" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#nvme-support"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>NVMe Support
</span></h3>
<p>ZeRO-Infinity allows for training incredibly large models by extending GPU and CPU memory with NVMe memory. Thanks to
smart partitioning and tiling algorithms each GPU needs to send and receive very small amounts of data during
offloading so modern NVMe proved to be fit to allow for an even larger total memory pool available to your training
process. ZeRO-Infinity requires ZeRO-3 enabled.</p>
<p>The following configuration example enables NVMe to offload both optimizer states and the params:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"nvme"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"nvme_path"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"/local_nvme"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"buffer_count"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">4</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"fast_init"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">false</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_param"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"nvme"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"nvme_path"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"/local_nvme"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"buffer_count"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">5</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"buffer_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"max_in_cpu"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"aio"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"block_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">262144</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"queue_depth"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">32</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"thread_count"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"single_submit"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">false</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_events"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"sub_group_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_prefetch_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_param_persistence_threshold"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_live_parameters"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_reuse_distance"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_gather_16bit_weights_on_model_save"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>You can choose to offload both optimizer states and params to NVMe, or just one of them or none. For example, if you
have copious amounts of CPU memory available, by all means offload to CPU memory only as it’d be faster (hint:
<em>“device”: “cpu”</em>).</p>
<p>Here is the full documentation for offloading <a href="https://www.deepspeed.ai/docs/config-json/#optimizer-offloading" rel="nofollow">optimizer states</a> and <a href="https://www.deepspeed.ai/docs/config-json/#parameter-offloading" rel="nofollow">parameters</a>.</p>
<p>Make sure that your <code>nvme_path</code> is actually an NVMe, since it will work with the normal hard drive or SSD, but it’ll
be much much slower. The fast scalable training was designed with modern NVMe transfer speeds in mind (as of this
writing one can have ~3.5GB/s read, ~3GB/s write peak speeds).</p>
<p>In order to figure out the optimal <code>aio</code> configuration block you must run a benchmark on your target setup, as
<a href="https://github.com/microsoft/DeepSpeed/issues/998" rel="nofollow">explained here</a>.</p>
<a id="deepspeed-zero2-zero3-performance"></a>
<h4 class="relative group"><a id="zero2-vs-zero3-performance" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero2-vs-zero3-performance"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO-2 vs ZeRO-3 Performance
</span></h4>
<p>ZeRO-3 is likely to be slower than ZeRO-2 if everything else is configured the same because the former has to gather
model weights in addition to what ZeRO-2 does. If ZeRO-2 meets your needs and you don’t need to scale beyond a few GPUs
then you may choose to stick to it. It’s important to understand that ZeRO-3 enables a much higher scalability capacity
at a cost of speed.</p>
<p>It’s possible to adjust ZeRO-3 configuration to make it perform closer to ZeRO-2:</p>
<ul><li>set <code>stage3_param_persistence_threshold</code> to a very large number - larger than the largest parameter, e.g., <code>6 * hidden_size * hidden_size</code>. This will keep the parameters on the GPUs.</li>
<li>turn off <code>offload_params</code> since ZeRO-2 doesn’t have that option.</li></ul>
<p>The performance will likely improve significantly with just <code>offload_params</code> turned off, even if you don’t change
<code>stage3_param_persistence_threshold</code>. Of course, these changes will impact the size of the model you can train. So
these help you to trade scalability for speed depending on your needs.</p>
<a id="deepspeed-zero2-example"></a>
<h4 class="relative group"><a id="zero2-example" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero2-example"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO-2 Example
</span></h4>
<p>Here is a full ZeRO-2 auto-configuration file <code>ds_config_zero2.json</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"AdamW"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"betas"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"eps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"weight_decay"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_partitions"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_scatter"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"gradient_accumulation_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"gradient_clipping"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"steps_per_print"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_batch_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_micro_batch_size_per_gpu"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"wall_clock_breakdown"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">false</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>Here is a full ZeRO-2 all-enabled manually set configuration file. It is here mainly for you to see what the typical
values look like, but we highly recommend using the one with multiple <code>auto</code> settings in it.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"AdamW"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3e-5</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"betas"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span><span class="hljs-number">0.8</span><span class="hljs-punctuation">,</span> <span class="hljs-number">0.999</span><span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"eps"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e-8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"weight_decay"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3e-7</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3e-5</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">500</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_partitions"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"allgather_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_scatter"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2e8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"steps_per_print"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"wall_clock_breakdown"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">false</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<a id="deepspeed-zero3-example"></a>
<h4 class="relative group"><a id="zero3-example" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero3-example"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO-3 Example
</span></h4>
<p>Here is a full ZeRO-3 auto-configuration file <code>ds_config_zero3.json</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"AdamW"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"betas"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"eps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"weight_decay"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_param"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"sub_group_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_prefetch_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_param_persistence_threshold"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_live_parameters"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_reuse_distance"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_gather_16bit_weights_on_model_save"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"gradient_accumulation_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"gradient_clipping"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"steps_per_print"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_batch_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_micro_batch_size_per_gpu"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"wall_clock_breakdown"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">false</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>Here is a full ZeRO-3 all-enabled manually set configuration file. It is here mainly for you to see what the typical
values look like, but we highly recommend using the one with multiple <code>auto</code> settings in it.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"AdamW"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3e-5</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"betas"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span><span class="hljs-number">0.8</span><span class="hljs-punctuation">,</span> <span class="hljs-number">0.999</span><span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"eps"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e-8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"weight_decay"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3e-7</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3e-5</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">500</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"offload_param"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"device"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"cpu"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"pin_memory"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"overlap_comm"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"contiguous_gradients"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"sub_group_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"reduce_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e6</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_prefetch_bucket_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0.94e6</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_param_persistence_threshold"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e4</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_live_parameters"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_max_reuse_distance"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e9</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"stage3_gather_16bit_weights_on_model_save"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"steps_per_print"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"wall_clock_breakdown"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">false</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="optimizer-and-scheduler" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#optimizer-and-scheduler"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Optimizer and Scheduler
</span></h3>
<p>As long as you don’t enable <code>offload_optimizer</code> you can mix and match DeepSpeed and HuggingFace schedulers and
optimizers, with the exception of using the combination of HuggingFace scheduler and DeepSpeed optimizer:</p>
<p>| Combos | HF Scheduler | DS Scheduler |
| HF Optimizer | Yes | Yes |
| DS Optimizer | No | Yes |</p>
<p>It is possible to use a non-DeepSpeed optimizer when <code>offload_optimizer</code> is enabled, as long as it has both CPU and
GPU implementation (except LAMB).</p>
<a id="deepspeed-optimizer"></a>
<h4 class="relative group"><a id="optimizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#optimizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Optimizer
</span></h4>
<p>DeepSpeed’s main optimizers are Adam, AdamW, OneBitAdam, and Lamb. These have been thoroughly tested with ZeRO and are
thus recommended to be used. It, however, can import other optimizers from <code>torch</code>. The full documentation is <a href="https://www.deepspeed.ai/docs/config-json/#optimizer-parameters" rel="nofollow">here</a>.</p>
<p>If you don’t configure the <code>optimizer</code> entry in the configuration file, the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will
automatically set it to <code>AdamW</code> and will use the supplied values or the defaults for the following command line
arguments: <code>--learning_rate</code>, <code>--adam_beta1</code>, <code>--adam_beta2</code>, <code>--adam_epsilon</code> and <code>--weight_decay</code>.</p>
<p>Here is an example of the auto-configured <code>optimizer</code> entry for <code>AdamW</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"AdamW"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"betas"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"eps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"weight_decay"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>Note that the command line arguments will set the values in the configuration file. This is so that there is one
definitive source of the values and to avoid hard to find errors when for example, the learning rate is set to
different values in different places. Command line rules. The values that get overridden are:</p>
<ul><li><code>lr</code> with the value of <code>--learning_rate</code></li>
<li><code>betas</code> with the value of <code>--adam_beta1 --adam_beta2</code></li>
<li><code>eps</code> with the value of <code>--adam_epsilon</code></li>
<li><code>weight_decay</code> with the value of <code>--weight_decay</code></li></ul>
<p>Therefore please remember to tune the shared hyperparameters on the command line.</p>
<p>You can also set the values explicitly:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"AdamW"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0.001</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"betas"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span><span class="hljs-number">0.8</span><span class="hljs-punctuation">,</span> <span class="hljs-number">0.999</span><span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"eps"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1e-8</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"weight_decay"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3e-7</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>But then you’re on your own synchronizing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments and the DeepSpeed
configuration.</p>
<p>If you want to use another optimizer which is not listed above, you will have to add to the top level configuration.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"zero_allow_untested_optimizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>Similarly to <code>AdamW</code>, you can configure other officially supported optimizers. Just remember that may have different
config values. e.g. for Adam you will want <code>weight_decay</code> around <code>0.01</code>.</p>
<a id="deepspeed-scheduler"></a>
<h4 class="relative group"><a id="scheduler" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#scheduler"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Scheduler
</span></h4>
<p>DeepSpeed supports <code>LRRangeTest</code>, <code>OneCycle</code>, <code>WarmupLR</code> and <code>WarmupDecayLR</code> learning rate schedulers. The full
documentation is <a href="https://www.deepspeed.ai/docs/config-json/#scheduler-parameters" rel="nofollow">here</a>.</p>
<p>Here is where the schedulers overlap between 🤗 Transformers and DeepSpeed:</p>
<ul><li><code>WarmupLR</code> via <code>--lr_scheduler_type constant_with_warmup</code></li>
<li><code>WarmupDecayLR</code> via <code>--lr_scheduler_type linear</code>. This is also the default value for <code>--lr_scheduler_type</code>,
therefore, if you don’t configure the scheduler this is scheduler that will get configured by default.</li></ul>
<p>If you don’t configure the <code>scheduler</code> entry in the configuration file, the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will use
the values of <code>--lr_scheduler_type</code>, <code>--learning_rate</code> and <code>--warmup_steps</code> or <code>--warmup_ratio</code> to configure a
🤗 Transformers version of it.</p>
<p>Here is an example of the auto-configured <code>scheduler</code> entry for <code>WarmupLR</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>Since <em>“auto”</em> is used the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> arguments will set the correct values in the configuration
file. This is so that there is one definitive source of the values and to avoid hard to find errors when, for example,
the learning rate is set to different values in different places. Command line rules. The values that get set are:</p>
<ul><li><code>warmup_min_lr</code> with the value of <code>0</code>.</li>
<li><code>warmup_max_lr</code> with the value of <code>--learning_rate</code>.</li>
<li><code>warmup_num_steps</code> with the value of <code>--warmup_steps</code> if provided. Otherwise will use <code>--warmup_ratio</code>
multiplied by the number of training steps and rounded up.</li>
<li><code>total_num_steps</code> with either the value of <code>--max_steps</code> or if it is not provided, derived automatically at run
time based on the environment and the size of the dataset and other command line arguments (needed for
<code>WarmupDecayLR</code>).</li></ul>
<p>You can, of course, take over any or all of the configuration values and set those yourself:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0.001</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>But then you’re on your own synchronizing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments and the DeepSpeed
configuration.</p>
<p>For example, for <code>WarmupDecayLR</code>, you can use the following entry:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"WarmupDecayLR"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"params"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"last_batch_iteration"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">-1</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"total_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_min_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_max_lr"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"warmup_num_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>and <code>total_num_steps</code>, <code>warmup_max_lr</code>, <code>warmup_num_steps</code> and <code>total_num_steps</code> will be set at loading time.</p>
<a id="deepspeed-fp32"></a>
<h3 class="relative group"><a id="fp32-precision" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#fp32-precision"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>fp32 Precision
</span></h3>
<p>Deepspeed supports the full fp32 and the fp16 mixed precision.</p>
<p>Because of the much reduced memory needs and faster speed one gets with the fp16 mixed precision, the only time you
will want to not use it is when the model you’re using doesn’t behave well under this training mode. Typically this
happens when the model wasn’t pretrained in the fp16 mixed precision (e.g. often this happens with bf16-pretrained
models). Such models may overflow or underflow leading to <code>NaN</code> loss. If this is your case then you will want to use
the full fp32 mode, by explicitly disabling the otherwise default fp16 mixed precision mode with:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"false"</span><span class="hljs-punctuation">,</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>If you’re using the Ampere-architecture based GPU, pytorch version 1.7 and higher will automatically switch to using
the much more efficient tf32 format for some operations, but the results will still be in fp32. For details and
benchmarks, please, see <a href="https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" rel="nofollow">TensorFloat-32(TF32) on Ampere devices</a>. The document includes
instructions on how to disable this automatic conversion if for some reason you prefer not to use it.</p>
<p>With the 🤗 Trainer you can use <code>--tf32</code> to enable it, or disable it with <code>--tf32 0</code> or <code>--no_tf32</code>. By default the PyTorch default is used.</p>
<a id="deepspeed-amp"></a>
<h3 class="relative group"><a id="automatic-mixed-precision" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#automatic-mixed-precision"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Automatic Mixed Precision
</span></h3>
<p>You can use automatic mixed precision with either a pytorch-like AMP way or the apex-like way:</p>
<h3 class="relative group"><a id="fp16" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#fp16"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>fp16
</span></h3>
<p>To configure pytorch AMP-like mode with fp16 (float16) set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>and the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will automatically enable or disable it based on the value of
<code>args.fp16_backend</code>. The rest of config values are up to you.</p>
<p>This mode gets enabled when <code>--fp16 --fp16_backend amp</code> or <code>--fp16_full_eval</code> command line args are passed.</p>
<p>You can also enable/disable this mode explicitly:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>But then you’re on your own synchronizing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments and the DeepSpeed
configuration.</p>
<p>Here is the <a href="https://www.deepspeed.ai/docs/config-json/#fp16-training-options" rel="nofollow">documentation</a>.</p>
<h3 class="relative group"><a id="bf16" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#bf16"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>bf16
</span></h3>
<p>If bf16 (bfloat16) is desired instead of fp16 then the following configuration section is to be used:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"bf16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>bf16 has the same dynamic range as fp32 and thus doesn’t require loss scaling.</p>
<p>This mode gets enabled when <code>--bf16</code> or <code>--bf16_full_eval</code> command line args are passed.</p>
<p>You can also enable/disable this mode explicitly:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"bf16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>As of <code>deepspeed==0.6.0</code> the bf16 support is new and experimental.</p>
<p>If you use <a href="#gradient-accumulation">gradient accumulation</a> with bf16-enabled, you need to be aware that it’ll accumulate gradients in bf16, which may not be what you want due to this format’s low precision, as it may lead to a lossy accumulation.</p></div>
<h3 class="relative group"><a id="apex" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#apex"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>apex
</span></h3>
<p>To configure apex AMP-like mode set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-attr">"amp"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"opt_level"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>and the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will automatically configure it based on the values of <code>args.fp16_backend</code> and
<code>args.fp16_opt_level</code>.</p>
<p>This mode gets enabled when <code>--fp16 --fp16_backend apex --fp16_opt_level 01</code> command line args are passed.</p>
<p>You can also configure this mode explicitly:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"amp"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"opt_level"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"O1"</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>But then you’re on your own synchronizing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments and the DeepSpeed
configuration.</p>
<p>Here is the <a href="https://www.deepspeed.ai/docs/config-json/#automatic-mixed-precision-amp-training-options" rel="nofollow">documentation</a>.</p>
<a id="deepspeed-bs"></a>
<h3 class="relative group"><a id="batch-size" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#batch-size"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Batch Size
</span></h3>
<p>To configure batch size, use:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"train_batch_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_micro_batch_size_per_gpu"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>and the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will automatically set <code>train_micro_batch_size_per_gpu</code> to the value of
<code>args.per_device_train_batch_size</code> and <code>train_batch_size</code> to <code>args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps</code>.</p>
<p>You can also set the values explicitly:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"train_batch_size"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">12</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"train_micro_batch_size_per_gpu"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">4</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>But then you’re on your own synchronizing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments and the DeepSpeed
configuration.</p>
<a id="deepspeed-grad-acc"></a>
<h3 class="relative group"><a id="gradient-accumulation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#gradient-accumulation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Gradient Accumulation
</span></h3>
<p>To configure gradient accumulation set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"gradient_accumulation_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>and the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will automatically set it to the value of <code>args.gradient_accumulation_steps</code>.</p>
<p>You can also set the value explicitly:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"gradient_accumulation_steps"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">3</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>But then you’re on your own synchronizing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments and the DeepSpeed
configuration.</p>
<a id="deepspeed-grad-clip"></a>
<h3 class="relative group"><a id="gradient-clipping" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#gradient-clipping"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Gradient Clipping
</span></h3>
<p>To configure gradient gradient clipping set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"gradient_clipping"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>and the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will automatically set it to the value of <code>args.max_grad_norm</code>.</p>
<p>You can also set the value explicitly:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"gradient_clipping"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1.0</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>But then you’re on your own synchronizing the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> command line arguments and the DeepSpeed
configuration.</p>
<a id="deepspeed-weight-extraction"></a>
<h3 class="relative group"><a id="getting-the-model-weights-out" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#getting-the-model-weights-out"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Getting The Model Weights Out
</span></h3>
<p>As long as you continue training and resuming using DeepSpeed you don’t need to worry about anything. DeepSpeed stores
fp32 master weights in its custom checkpoint optimizer files, which are <code>global_step*/*optim_states.pt</code> (this is glob
pattern), and are saved under the normal checkpoint.</p>
<p><strong>FP16 Weights:</strong></p>
<p>When a model is saved under ZeRO-2, you end up having the normal <code>pytorch_model.bin</code> file with the model weights, but
they are only the fp16 version of the weights.</p>
<p>Under ZeRO-3, things are much more complicated, since the model weights are partitioned out over multiple GPUs,
therefore <code>"stage3_gather_16bit_weights_on_model_save": true</code> is required to get the <code>Trainer</code> to save the fp16
version of the weights. If this setting is <code>False</code> <code>pytorch_model.bin</code> won’t be created. This is because by default DeepSpeed’s <code>state_dict</code> contains a placeholder and not the real weights. If we were to save this <code>state_dict</code> it won’t be possible to load it back.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"zero_optimization"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"stage3_gather_16bit_weights_on_model_save"</span><span class="hljs-punctuation">:</span> <span class="hljs-keyword">true</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p><strong>FP32 Weights:</strong></p>
<p>While the fp16 weights are fine for resuming training, if you finished finetuning your model and want to upload it to
the <a href="https://huggingface.co/models" rel="nofollow">models hub</a> or pass it to someone else you most likely will want to get the fp32
weights. This ideally shouldn’t be done during training since this is a process that requires a lot of memory, and
therefore best to be performed offline after the training is complete. But if desired and you have plenty of free CPU
memory it can be done in the same training script. The following sections will discuss both approaches.</p>
<p><strong>Live FP32 Weights Recovery:</strong></p>
<p>This approach may not work if you model is large and you have little free CPU memory left, at the end of the training.</p>
<p>If you have saved at least one checkpoint, and you want to use the latest one, you can do the following:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.trainer_utils <span class="hljs-keyword">import</span> get_last_checkpoint
<span class="hljs-keyword">from</span> deepspeed.utils.zero_to_fp32 <span class="hljs-keyword">import</span> load_state_dict_from_zero_checkpoint
checkpoint_dir = get_last_checkpoint(trainer.args.output_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)<!-- HTML_TAG_END --></pre></div>
<p>If you’re using the <code>--load_best_model_at_end</code> class:<em>~transformers.TrainingArguments</em> argument (to track the best
checkpoint), then you can finish the training by first saving the final model explicitly and then do the same as above:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> deepspeed.utils.zero_to_fp32 <span class="hljs-keyword">import</span> load_state_dict_from_zero_checkpoint
checkpoint_dir = os.path.join(trainer.args.output_dir, <span class="hljs-string">"checkpoint-final"</span>)
trainer.deepspeed.save_checkpoint(checkpoint_dir)
fp32_model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Note, that once <code>load_state_dict_from_zero_checkpoint</code> was run, the <code>model</code> will no longer be useable in the
DeepSpeed context of the same application. i.e. you will need to re-initialize the deepspeed engine, since
<code>model.load_state_dict(state_dict)</code> will remove all the DeepSpeed magic from it. So do this only at the very end
of the training.</p></div>
<p>Of course, you don’t have to use class:<em>~transformers.Trainer</em> and you can adjust the examples above to your own
trainer.</p>
<p>If for some reason you want more refinement, you can also extract the fp32 <code>state_dict</code> of the weights and apply
these yourself as is shown in the following example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> deepspeed.utils.zero_to_fp32 <span class="hljs-keyword">import</span> get_fp32_state_dict_from_zero_checkpoint
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) <span class="hljs-comment"># already on cpu</span>
model = model.cpu()
model.load_state_dict(state_dict)<!-- HTML_TAG_END --></pre></div>
<p><strong>Offline FP32 Weights Recovery:</strong></p>
<p>DeepSpeed creates a special conversion script <code>zero_to_fp32.py</code> which it places in the top-level of the checkpoint
folder. Using this script you can extract the weights at any point. The script is standalone and you no longer need to
have the configuration file or a <code>Trainer</code> to do the extraction.</p>
<p>Let’s say your checkpoint folder looks like this:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->$ <span class="hljs-built_in">ls</span> -l output_dir/checkpoint-1/
-rw-rw-r-- 1 stas stas 1.4K Mar 27 20:42 config.json
drwxrwxr-x 2 stas stas 4.0K Mar 25 19:52 global_step1/
-rw-rw-r-- 1 stas stas 12 Mar 27 13:16 latest
-rw-rw-r-- 1 stas stas 827K Mar 27 20:42 optimizer.pt
-rw-rw-r-- 1 stas stas 231M Mar 27 20:42 pytorch_model.bin
-rw-rw-r-- 1 stas stas 623 Mar 27 20:42 scheduler.pt
-rw-rw-r-- 1 stas stas 1.8K Mar 27 20:42 special_tokens_map.json
-rw-rw-r-- 1 stas stas 774K Mar 27 20:42 spiece.model
-rw-rw-r-- 1 stas stas 1.9K Mar 27 20:42 tokenizer_config.json
-rw-rw-r-- 1 stas stas 339 Mar 27 20:42 trainer_state.json
-rw-rw-r-- 1 stas stas 2.3K Mar 27 20:42 training_args.bin
-rwxrw-r-- 1 stas stas 5.5K Mar 27 13:16 zero_to_fp32.py*<!-- HTML_TAG_END --></pre></div>
<p>In this example there is just one DeepSpeed checkpoint sub-folder <em>global_step1</em>. Therefore to reconstruct the fp32
weights just run:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python zero_to_fp32.py . pytorch_model.bin<!-- HTML_TAG_END --></pre></div>
<p>This is it. <code>pytorch_model.bin</code> will now contain the full fp32 model weights consolidated from multiple GPUs.</p>
<p>The script will automatically be able to handle either a ZeRO-2 or ZeRO-3 checkpoint.</p>
<p><code>python zero_to_fp32.py -h</code> will give you usage details.</p>
<p>The script will auto-discover the deepspeed sub-folder using the contents of the file <code>latest</code>, which in the current
example will contain <code>global_step1</code>.</p>
<p>Note: currently the script requires 2x general RAM of the final fp32 model weights.</p>
<h3 class="relative group"><a id="zero3-and-infinity-nuances" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero3-and-infinity-nuances"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO-3 and Infinity Nuances
</span></h3>
<p>ZeRO-3 is quite different from ZeRO-2 because of its param sharding feature.</p>
<p>ZeRO-Infinity further extends ZeRO-3 to support NVMe memory and multiple other speed and scalability improvements.</p>
<p>While all the efforts were made for things to just work without needing any special changes to your models, in certain
circumstances you may find the following information to be needed.</p>
<h4 class="relative group"><a id="constructing-massive-models" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#constructing-massive-models"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Constructing Massive Models
</span></h4>
<p>DeepSpeed/ZeRO-3 can handle models with Trillions of parameters which may not fit onto the existing RAM. In such cases,
but also if you want the initialization to happen much faster, initialize the model using <em>deepspeed.zero.Init()</em>
context manager (which is also a function decorator), like so:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> T5ForConditionalGeneration, T5Config
<span class="hljs-keyword">import</span> deepspeed
<span class="hljs-keyword">with</span> deepspeed.zero.Init():
config = T5Config.from_pretrained(<span class="hljs-string">"t5-small"</span>)
model = T5ForConditionalGeneration(config)<!-- HTML_TAG_END --></pre></div>
<p>As you can see this gives you a randomly initialized model.</p>
<p>If you want to use a pretrained model, <code>model_class.from_pretrained</code> will activate this feature as long as
<code>is_deepspeed_zero3_enabled()</code> returns <code>True</code>, which currently is setup by the
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> object if the passed DeepSpeed configuration file contains ZeRO-3 config
section. Thus you must create the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> object <strong>before</strong> calling
<code>from_pretrained</code>. Here is an example of a possible sequence:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel, Trainer, TrainingArguments
training_args = TrainingArguments(..., deepspeed=ds_config)
model = AutoModel.from_pretrained(<span class="hljs-string">"t5-small"</span>)
trainer = Trainer(model=model, args=training_args, ...)<!-- HTML_TAG_END --></pre></div>
<p>If you’re using the official example scripts and your command line arguments include <code>--deepspeed ds_config.json</code>
with ZeRO-3 config enabled, then everything is already done for you, since this is how example scripts are written.</p>
<p>Note: If the fp16 weights of the model can’t fit onto the memory of a single GPU this feature must be used.</p>
<p>For full details on this method and other related features please refer to <a href="https://deepspeed.readthedocs.io/en/latest/zero3.html#constructing-massive-models" rel="nofollow">Constructing Massive Models</a>.</p>
<p>Also when loading fp16-pretrained models, you will want to tell <code>from_pretrained</code> to use
<code>torch_dtype=torch.float16</code>. For details, please, see <a href="#from_pretrained-torch-dtype">from_pretrained-torch-dtype</a>.</p>
<h4 class="relative group"><a id="gathering-parameters" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#gathering-parameters"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Gathering Parameters
</span></h4>
<p>Under ZeRO-3 on multiple GPUs no single GPU has all the parameters unless it’s the parameters for the currently
executing layer. So if you need to access all parameters from all layers at once there is a specific method to do it.
Most likely you won’t need it, but if you do please refer to <a href="https://deepspeed.readthedocs.io/en/latest/zero3.html#manual-parameter-coordination" rel="nofollow">Gathering Parameters</a></p>
<p>We do however use it internally in several places, one such example is when loading pretrained model weights in
<code>from_pretrained</code>. We load one layer at a time and immediately partition it to all participating GPUs, as for very
large models it won’t be possible to load it on one GPU and then spread it out to multiple GPUs, due to memory
limitations.</p>
<p>Also under ZeRO-3, if you write your own code and run into a model parameter weight that looks like:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->tensor([<span class="hljs-number">1.0</span>], device=<span class="hljs-string">"cuda:0"</span>, dtype=torch.float16, requires_grad=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>stress on <code>tensor([1.])</code>, or if you get an error where it says the parameter is of size <code>1</code>, instead of some much
larger multi-dimensional shape, this means that the parameter is partitioned and what you see is a ZeRO-3 placeholder.</p>
<a id="deepspeed-zero-inference"></a>
<h3 class="relative group"><a id="zero-inference" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#zero-inference"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ZeRO Inference
</span></h3>
<p>ZeRO Inference uses the same config as ZeRO-3 Training. You just don’t need the optimizer and scheduler sections. In
fact you can leave these in the config file if you want to share the same one with the training. They will just be
ignored.</p>
<p>Otherwise you just need to pass the usual <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> arguments. For example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json<!-- HTML_TAG_END --></pre></div>
<p>The only important thing is that you need to use a ZeRO-3 configuration, since ZeRO-2 provides no benefit whatsoever
for the inference as only ZeRO-3 performs sharding of parameters, whereas ZeRO-1 shards gradients and optimizer states.</p>
<p>Here is an example of running <code>run_translation.py</code> under DeepSpeed deploying all available GPUs:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->deepspeed examples/pytorch/translation/run_translation.py \
--deepspeed tests/deepspeed/ds_config_zero3.json \
--model_name_or_path t5-small --output_dir output_dir \
--do_eval --max_eval_samples 50 --warmup_steps 50 \
--max_source_length 128 --val_max_target_length 128 \
--overwrite_output_dir --per_device_eval_batch_size 4 \
--predict_with_generate --dataset_config <span class="hljs-string">"ro-en"</span> --fp16 \
--source_lang en --target_lang ro --dataset_name wmt16 \
--source_prefix <span class="hljs-string">"translate English to Romanian: "</span><!-- HTML_TAG_END --></pre></div>
<p>Since for inference there is no need for additional large memory used by the optimizer states and the gradients you
should be able to fit much larger batches and/or sequence length onto the same hardware.</p>
<p>Additionally DeepSpeed is currently developing a related product called Deepspeed-Inference which has no relationship
to the ZeRO technology, but instead uses tensor parallelism to scale models that can’t fit onto a single GPU. This is a
work in progress and we will provide the integration once that product is complete.</p>
<h3 class="relative group"><a id="memory-requirements" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#memory-requirements"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Memory Requirements
</span></h3>
<p>Since Deepspeed ZeRO can offload memory to CPU (and NVMe) the framework provides utils that allow one to tell how much CPU and GPU memory will be needed depending on the number of GPUs being used.</p>
<p>Let’s estimate how much memory is needed to finetune “bigscience/T0_3B” on a single GPU:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->$ python -c <span class="hljs-string">'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained("bigscience/T0_3B"); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=1, num_nodes=1)'</span>
[...]
Estimated memory needed <span class="hljs-keyword">for</span> params, optim states and gradients <span class="hljs-keyword">for</span> a:
HW: Setup with 1 node, 1 GPU per node.
SW: Model with 2783M total params, 65M largest layer params.
per CPU | per GPU | Options
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=1
62.23GB | 5.43GB | offload_param=none, offload_optimizer=cpu , zero_init=0
0.37GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=1
15.56GB | 46.91GB | offload_param=none, offload_optimizer=none, zero_init=0<!-- HTML_TAG_END --></pre></div>
<p>So you can fit it on a single 80GB GPU and no CPU offload, or a tiny 8GB GPU but then need ~60GB of CPU memory. (Remember this is just the memory for params, optimizer states and gradients - you will need a bit more memory for cuda kernels, activations and temps.)</p>
<p>Then it’s a tradeoff of cost vs speed. It’ll be cheaper to buy/rent a smaller GPU (or less GPUs since you can use multiple GPUs with Deepspeed ZeRO. But then it’ll be slower, so even if you don’t care about how fast something will be done, the slowdown has a direct impact on the duration of using the GPU and thus bigger cost. So experiment and compare which works the best.</p>
<p>If you have enough GPU memory make sure to disable the CPU/NVMe offload as it’ll make everything faster.</p>
<p>For example, let’s repeat the same for 2 GPUs:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->$ python -c <span class="hljs-string">'from transformers import AutoModel; \
from deepspeed.runtime.zero.stage3 import estimate_zero3_model_states_mem_needs_all_live; \
model = AutoModel.from_pretrained("bigscience/T0_3B"); \
estimate_zero3_model_states_mem_needs_all_live(model, num_gpus_per_node=2, num_nodes=1)'</span>
[...]
Estimated memory needed <span class="hljs-keyword">for</span> params, optim states and gradients <span class="hljs-keyword">for</span> a:
HW: Setup with 1 node, 2 GPUs per node.
SW: Model with 2783M total params, 65M largest layer params.
per CPU | per GPU | Options
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=1
70.00GB | 0.25GB | offload_param=cpu , offload_optimizer=cpu , zero_init=0
62.23GB | 2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=1
62.23GB | 2.84GB | offload_param=none, offload_optimizer=cpu , zero_init=0
0.74GB | 23.58GB | offload_param=none, offload_optimizer=none, zero_init=1
31.11GB | 23.58GB | offload_param=none, offload_optimizer=none, zero_init=0
<!-- HTML_TAG_END --></pre></div>
<p>So here you’d want 2x 32GB GPUs or higher without offloading to CPU.</p>
<p>For full information please see <a href="https://deepspeed.readthedocs.io/en/latest/memory.html" rel="nofollow">memory estimators</a>.</p>
<h3 class="relative group"><a id="filing-issues" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#filing-issues"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Filing Issues
</span></h3>
<p>Here is how to file an issue so that we could quickly get to the bottom of the issue and help you to unblock your work.</p>
<p>In your report please always include:</p>
<ol><li><p>the full Deepspeed config file in the report</p></li>
<li><p>either the command line arguments if you were using the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> or
<a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> arguments if you were scripting the Trainer setup yourself. Please do not
dump the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> as it has dozens of entries that are irrelevant.</p></li>
<li><p>Output of:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->python -c <span class="hljs-string">'import torch; print(f"torch: {torch.__version__}")'</span>
python -c <span class="hljs-string">'import transformers; print(f"transformers: {transformers.__version__}")'</span>
python -c <span class="hljs-string">'import deepspeed; print(f"deepspeed: {deepspeed.__version__}")'</span><!-- HTML_TAG_END --></pre></div></li>
<li><p>If possible include a link to a Google Colab notebook that we can reproduce the problem with. You can use this
<a href="https://github.com/stas00/porting/blob/master/transformers/deepspeed/DeepSpeed_on_colab_CLI.ipynb" rel="nofollow">notebook</a> as
a starting point.</p></li>
<li><p>Unless it’s impossible please always use a standard dataset that we can use and not something custom.</p></li>
<li><p>If possible try to use one of the existing <a href="https://github.com/huggingface/transformers/tree/master/examples/pytorch" rel="nofollow">examples</a> to reproduce the problem with.</p></li></ol>
<p>Things to consider:</p>
<ul><li><p>Deepspeed is often not the cause of the problem.</p>
<p>Some of the filed issues proved to be Deepspeed-unrelated. That is once Deepspeed was removed from the setup, the
problem was still there.</p>
<p>Therefore, if it’s not absolutely obvious it’s a DeepSpeed-related problem, as in you can see that there is an
exception and you can see that DeepSpeed modules are involved, first re-test your setup without DeepSpeed in it.
And only if the problem persists then do mentioned Deepspeed and supply all the required details.</p></li>
<li><p>If it’s clear to you that the issue is in the DeepSpeed core and not the integration part, please file the Issue
directly with <a href="https://github.com/microsoft/DeepSpeed/" rel="nofollow">Deepspeed</a>. If you aren’t sure, please do not worry,
either Issue tracker will do, we will figure it out once you posted it and redirect you to another Issue tracker if
need be.</p></li></ul>
<h3 class="relative group"><a id="troubleshooting" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#troubleshooting"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Troubleshooting
</span></h3>
<h4 class="relative group"><a id="the-deepspeed-process-gets-killed-at-startup-without-a-traceback" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#the-deepspeed-process-gets-killed-at-startup-without-a-traceback"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>the <code>deepspeed</code> process gets killed at startup without a traceback
</span></h4>
<p>If the <code>deepspeed</code> process gets killed at launch time without a traceback, that usually means that the program tried
to allocate more CPU memory than your system has or your process is allowed to allocate and the OS kernel killed that
process. This is because your configuration file most likely has either <code>offload_optimizer</code> or <code>offload_param</code> or
both configured to offload to <code>cpu</code>. If you have NVMe, experiment with offloading to NVMe if you’re running under
ZeRO-3. Here is how you can <a href="https://deepspeed.readthedocs.io/en/latest/memory.html" rel="nofollow">estimate how much memory is needed for a specific model</a>.</p>
<h4 class="relative group"><a id="training-andor-evalpredict-loss-is-nan" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training-andor-evalpredict-loss-is-nan"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>training and/or eval/predict loss is <code>NaN</code></span></h4>
<p>This often happens when one takes a model pre-trained in bf16 mixed precision mode and tries to use it under fp16 (with or without mixed precision). Most models trained on TPU and often the ones released by Google are in this category (e.g. almost all t5-based models). Here the solution is to either use fp32 or bf16 if your hardware supports it (TPU, Ampere GPUs or newer).</p>
<p>The other problem may have to do with using fp16. When you configure this section:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span>
<span class="hljs-attr">"fp16"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>
<span class="hljs-attr">"enabled"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"auto"</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">0</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"loss_scale_window"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1000</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"initial_scale_power"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">16</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"hysteresis"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">2</span><span class="hljs-punctuation">,</span>
<span class="hljs-attr">"min_loss_scale"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span>
<span class="hljs-punctuation">}</span>
<span class="hljs-punctuation">}</span><!-- HTML_TAG_END --></pre></div>
<p>and you see in your log that Deepspeed reports <code>OVERFLOW!</code> as follows:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-number">0</span>%| | <span class="hljs-number">0</span>/<span class="hljs-number">189</span> [<span class="hljs-number">00</span>:<span class="hljs-number">00</span><?, ?it/s]
[deepscale] OVERFLOW! <span class="hljs-built_in">Rank</span> <span class="hljs-number">0</span> Skipping <span class="hljs-built_in">step</span>. Attempted loss scale: <span class="hljs-number">262144</span>, reducing <span class="hljs-keyword">to</span> <span class="hljs-number">262144</span>
<span class="hljs-number">1</span>%|▌ | <span class="hljs-number">1</span>/<span class="hljs-number">189</span> [<span class="hljs-number">00</span>:<span class="hljs-number">00</span><<span class="hljs-number">01</span>:<span class="hljs-number">26</span>, <span class="hljs-number">2.17</span>it/s]
[deepscale] OVERFLOW! <span class="hljs-built_in">Rank</span> <span class="hljs-number">0</span> Skipping <span class="hljs-built_in">step</span>. Attempted loss scale: <span class="hljs-number">262144</span>, reducing <span class="hljs-keyword">to</span> <span class="hljs-number">131072.0</span>
<span class="hljs-number">1</span>%|█▏
[...]
[deepscale] OVERFLOW! <span class="hljs-built_in">Rank</span> <span class="hljs-number">0</span> Skipping <span class="hljs-built_in">step</span>. Attempted loss scale: <span class="hljs-number">1</span>, reducing <span class="hljs-keyword">to</span> <span class="hljs-number">1</span>
<span class="hljs-number">14</span>%|████████████████▌ | <span class="hljs-number">27</span>/<span class="hljs-number">189</span> [<span class="hljs-number">00</span>:<span class="hljs-number">14</span><<span class="hljs-number">01</span>:<span class="hljs-number">13</span>, <span class="hljs-number">2.21</span>it/s]
[deepscale] OVERFLOW! <span class="hljs-built_in">Rank</span> <span class="hljs-number">0</span> Skipping <span class="hljs-built_in">step</span>. Attempted loss scale: <span class="hljs-number">1</span>, reducing <span class="hljs-keyword">to</span> <span class="hljs-number">1</span>
<span class="hljs-number">15</span>%|█████████████████▏ | <span class="hljs-number">28</span>/<span class="hljs-number">189</span> [<span class="hljs-number">00</span>:<span class="hljs-number">14</span><<span class="hljs-number">01</span>:<span class="hljs-number">13</span>, <span class="hljs-number">2.18</span>it/s]
[deepscale] OVERFLOW! <span class="hljs-built_in">Rank</span> <span class="hljs-number">0</span> Skipping <span class="hljs-built_in">step</span>. Attempted loss scale: <span class="hljs-number">1</span>, reducing <span class="hljs-keyword">to</span> <span class="hljs-number">1</span>
<span class="hljs-number">15</span>%|█████████████████▊ | <span class="hljs-number">29</span>/<span class="hljs-number">189</span> [<span class="hljs-number">00</span>:<span class="hljs-number">15</span><<span class="hljs-number">01</span>:<span class="hljs-number">13</span>, <span class="hljs-number">2.18</span>it/s]
[deepscale] OVERFLOW! <span class="hljs-built_in">Rank</span> <span class="hljs-number">0</span> Skipping <span class="hljs-built_in">step</span>. Attempted loss scale: <span class="hljs-number">1</span>, reducing <span class="hljs-keyword">to</span> <span class="hljs-number">1</span>
[...]<!-- HTML_TAG_END --></pre></div>
<p>that means that the Deepspeed loss scaler can’t figure out a scaling co-efficient that overcomes loss overflow.</p>
<p>(the log was massaged to be more readable here.)</p>
<p>In this case you usually need to raise the value of <code>initial_scale_power</code>. Setting it to <code>"initial_scale_power": 32</code> will typically resolve the problem.</p>
<h3 class="relative group"><a id="notes" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#notes"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Notes
</span></h3>
<ul><li>DeepSpeed works with the PyTorch <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> but not TF <code>TFTrainer</code></li>
<li>While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from <a href="https://github.com/microsoft/deepspeed#installation" rel="nofollow">source</a> to best match your hardware and also if you need to enable
certain features, like 1-bit Adam, which aren’t available in the pypi distribution.</li>
<li>You don’t have to use the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> to use DeepSpeed with 🤗 Transformers - you can use any model
with your own trainer, and you will have to adapt the latter according to <a href="https://www.deepspeed.ai/getting-started/#writing-deepspeed-models" rel="nofollow">the DeepSpeed integration instructions</a>.</li></ul>
<a id="deepspeed-non-trainer-integration"></a>
<h2 class="relative group"><a id="nontrainer-deepspeed-integration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#nontrainer-deepspeed-integration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Non-Trainer Deepspeed Integration
</span></h2>
<p>The <a href="/docs/transformers/pr_16143/en/main_classes/deepspeed#transformers.deepspeed.HfDeepSpeedConfig">HfDeepSpeedConfig</a> is used to integrate Deepspeed into the 🤗 Transformers core
functionality, when <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> is not used. The only thing that it does is handling Deepspeed ZeRO 3 param gathering and automatically splitting the model onto multiple gpus during <code>from_pretrained</code> call. Everything else you have to do by yourself.</p>
<p>When using <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> everything is automatically taken care of.</p>
<p>When not using <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, to efficiently deploy DeepSpeed stage 3, you must instantiate the
<a href="/docs/transformers/pr_16143/en/main_classes/deepspeed#transformers.deepspeed.HfDeepSpeedConfig">HfDeepSpeedConfig</a> object before instantiating the model.</p>
<p>For example for a pretrained model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.deepspeed <span class="hljs-keyword">import</span> HfDeepSpeedConfig
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
<span class="hljs-keyword">import</span> deepspeed
ds_config = {...} <span class="hljs-comment"># deepspeed config object or path to the file</span>
<span class="hljs-comment"># must run before instantiating the model to detect zero 3</span>
dschf = HfDeepSpeedConfig(ds_config) <span class="hljs-comment"># keep this object alive</span>
model = AutoModel.from_pretrained(<span class="hljs-string">"gpt2"</span>)
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)<!-- HTML_TAG_END --></pre></div>
<p>or for non-pretrained model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.deepspeed <span class="hljs-keyword">import</span> HfDeepSpeedConfig
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel, AutoConfig
<span class="hljs-keyword">import</span> deepspeed
ds_config = {...} <span class="hljs-comment"># deepspeed config object or path to the file</span>
<span class="hljs-comment"># must run before instantiating the model to detect zero 3</span>
dschf = HfDeepSpeedConfig(ds_config) <span class="hljs-comment"># keep this object alive</span>
config = AutoConfig.from_pretrained(<span class="hljs-string">"gpt2"</span>)
model = AutoModel.from_config(config)
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)<!-- HTML_TAG_END --></pre></div>
<p>Please note that if you’re not using the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> integration, you’re completely on your own. Basically follow the documentation on the <a href="https://www.deepspeed.ai/" rel="nofollow">Deepspeed</a> website. Also you have to configure explicitly the config file - you can’t use <code>"auto"</code> values and you will have to put real values instead.</p>
<h2 class="relative group"><a id="transformers.deepspeed.HfDeepSpeedConfig" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.deepspeed.HfDeepSpeedConfig"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>HfDeepSpeedConfig
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.deepspeed.HfDeepSpeedConfig"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.deepspeed.</span><span class="font-semibold">HfDeepSpeedConfig</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.deepspeed.HfDeepSpeedConfig" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.deepspeed.HfDeepSpeedConfig"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/deepspeed.py#L40" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config_file_or_dict<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.deepspeed.HfDeepSpeedConfig.config_file_or_dict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.deepspeed.HfDeepSpeedConfig.config_file_or_dict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config_file_or_dict</strong> (<code>Union[str, Dict]</code>) — path to DeepSpeed config file or dict.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.</p>
<p>A <code>weakref</code> of this object is stored in the module’s globals to be able to access the config from areas where
things like the Trainer object is not available (e.g. <code>from_pretrained</code> and <code>_get_resized_embeddings</code>). Therefore
it’s important that this object remains alive while the program is still running.</p>
<p><a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> uses the <code>HfTrainerDeepSpeedConfig</code> subclass instead. That subclass has logic to sync the configuration
with values of <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a> by replacing special placeholder values: <code>"auto"</code>. Without this special logic
the DeepSpeed configuration is not modified in any way.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.deepspeed.HfDeepSpeedConfig.del_config_sub_tree"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>del_config_sub_tree</span></h4><!-- HTML_TAG_END -->
<a id="transformers.deepspeed.HfDeepSpeedConfig.del_config_sub_tree" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.deepspeed.HfDeepSpeedConfig.del_config_sub_tree"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/deepspeed.py#L114" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ds_key_long<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">must_exist<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Deletes a sub-section of the config file if it’s found.</p>
<p>Unless <code>must_exist</code> is <code>True</code> the section doesn’t have to exist.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.deepspeed.HfDeepSpeedConfig.get_value"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_value</span></h4><!-- HTML_TAG_END -->
<a id="transformers.deepspeed.HfDeepSpeedConfig.get_value" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.deepspeed.HfDeepSpeedConfig.get_value"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/deepspeed.py#L105" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ds_key_long<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">default<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns the set value or <code>default</code> if no value is set</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.deepspeed.HfDeepSpeedConfig.is_false"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>is_false</span></h4><!-- HTML_TAG_END -->
<a id="transformers.deepspeed.HfDeepSpeedConfig.is_false" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.deepspeed.HfDeepSpeedConfig.is_false"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/deepspeed.py#L146" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ds_key_long<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns <code>True</code>/<code>False` only if the value is set, always `False` otherwise. So use this method to ask the very specific question of whether the value is set to `False` (and it's not set to `True</code> or isn’t set).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.deepspeed.HfDeepSpeedConfig.is_true"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>is_true</span></h4><!-- HTML_TAG_END -->
<a id="transformers.deepspeed.HfDeepSpeedConfig.is_true" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.deepspeed.HfDeepSpeedConfig.is_true"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/deepspeed.py#L137" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ds_key_long<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns <code>True</code>/<code>False` only if the value is set, always `False` otherwise. So use this method to ask the very specific question of whether the value is set to `True` (and it's not set to `False</code> or isn’t set).</p></div></div>
<h3 class="relative group"><a id="custom-deepspeed-zero-inference" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#custom-deepspeed-zero-inference"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Custom DeepSpeed ZeRO Inference
</span></h3>
<p>Here is an example of how one could do DeepSpeed ZeRO Inference without using <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> when one can’t fit a model onto a single GPU. The solution includes using additional GPUs or/and offloading GPU memory to CPU memory.</p>
<p>The important nuance to understand here is that the way ZeRO is designed you can process different inputs on different GPUs in parallel.</p>
<p>The example has copious notes and is self-documenting.</p>
<p>Make sure to:</p>
<ol><li>disable CPU offload if you have enough GPU memory (since it slows things down)</li>
<li>enable bf16 if you own an Ampere or a newer GPU to make things faster. If you don’t have that hardware you may enable fp16 as long as you don’t use any model that was pre-trained in bf16 mixed precision (such as most t5 models). These usually overflow in fp16 and you will see garbage as output.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment">#!/usr/bin/env python</span>
<span class="hljs-comment"># This script demonstrates how to use Deepspeed ZeRO in an inference mode when one can't fit a model</span>
<span class="hljs-comment"># into a single GPU</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># 1. Use 1 GPU with CPU offload</span>
<span class="hljs-comment"># 2. Or use multiple GPUs instead</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># First you need to install deepspeed: pip install deepspeed</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># Here we use a 3B "bigscience/T0_3B" model which needs about 15GB GPU RAM - so 1 largish or 2</span>
<span class="hljs-comment"># small GPUs can handle it. or 1 small GPU and a lot of CPU memory.</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># To use a larger model like "bigscience/T0" which needs about 50GB, unless you have an 80GB GPU -</span>
<span class="hljs-comment"># you will need 2-4 gpus. And then you can adapt the script to handle more gpus if you want to</span>
<span class="hljs-comment"># process multiple inputs at once.</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># The provided deepspeed config also activates CPU memory offloading, so chances are that if you</span>
<span class="hljs-comment"># have a lot of available CPU memory and you don't mind a slowdown you should be able to load a</span>
<span class="hljs-comment"># model that doesn't normally fit into a single GPU. If you have enough GPU memory the program will</span>
<span class="hljs-comment"># run faster if you don't want offload to CPU - so disable that section then.</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># To deploy on 1 gpu:</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># deepspeed --num_gpus 1 t0.py</span>
<span class="hljs-comment"># or:</span>
<span class="hljs-comment"># python -m torch.distributed.run --nproc_per_node=1 t0.py</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># To deploy on 2 gpus:</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># deepspeed --num_gpus 2 t0.py</span>
<span class="hljs-comment"># or:</span>
<span class="hljs-comment"># python -m torch.distributed.run --nproc_per_node=2 t0.py</span>
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM
<span class="hljs-keyword">from</span> transformers.deepspeed <span class="hljs-keyword">import</span> HfDeepSpeedConfig
<span class="hljs-keyword">import</span> deepspeed
<span class="hljs-keyword">import</span> os
<span class="hljs-keyword">import</span> torch
os.environ[<span class="hljs-string">"TOKENIZERS_PARALLELISM"</span>] = <span class="hljs-string">"false"</span> <span class="hljs-comment"># To avoid warnings about parallelism in tokenizers</span>
<span class="hljs-comment"># distributed setup</span>
local_rank = <span class="hljs-built_in">int</span>(os.getenv(<span class="hljs-string">"LOCAL_RANK"</span>, <span class="hljs-string">"0"</span>))
world_size = <span class="hljs-built_in">int</span>(os.getenv(<span class="hljs-string">"WORLD_SIZE"</span>, <span class="hljs-string">"1"</span>))
torch.cuda.set_device(local_rank)
deepspeed.init_distributed()
model_name = <span class="hljs-string">"bigscience/T0_3B"</span>
config = AutoConfig.from_pretrained(model_name)
model_hidden_size = config.d_model
<span class="hljs-comment"># batch size has to be divisible by world_size, but can be bigger than world_size</span>
train_batch_size = <span class="hljs-number">1</span> * world_size
<span class="hljs-comment"># ds_config notes</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># - enable bf16 if you use Ampere or higher GPU - this will run in mixed precision and will be</span>
<span class="hljs-comment"># faster.</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># - for older GPUs you can enable fp16, but it'll only work for non-bf16 pretrained models - e.g.</span>
<span class="hljs-comment"># all official t5 models are bf16-pretrained</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># - set offload_param.device to "none" or completely remove the `offload_param` section if you don't</span>
<span class="hljs-comment"># - want CPU offload</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># - if using `offload_param` you can manually finetune stage3_param_persistence_threshold to control</span>
<span class="hljs-comment"># - which params should remain on gpus - the larger the value the smaller the offload size</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># For indepth info on Deepspeed config see</span>
<span class="hljs-comment"># https://huggingface.co/docs/transformers/master/main_classes/deepspeed</span>
<span class="hljs-comment"># keeping the same format as json for consistency, except it uses lower case for true/false</span>
<span class="hljs-comment"># fmt: off</span>
ds_config = {
<span class="hljs-string">"fp16"</span>: {
<span class="hljs-string">"enabled"</span>: <span class="hljs-literal">False</span>
},
<span class="hljs-string">"bf16"</span>: {
<span class="hljs-string">"enabled"</span>: <span class="hljs-literal">False</span>
},
<span class="hljs-string">"zero_optimization"</span>: {
<span class="hljs-string">"stage"</span>: <span class="hljs-number">3</span>,
<span class="hljs-string">"offload_param"</span>: {
<span class="hljs-string">"device"</span>: <span class="hljs-string">"cpu"</span>,
<span class="hljs-string">"pin_memory"</span>: <span class="hljs-literal">True</span>
},
<span class="hljs-string">"overlap_comm"</span>: <span class="hljs-literal">True</span>,
<span class="hljs-string">"contiguous_gradients"</span>: <span class="hljs-literal">True</span>,
<span class="hljs-string">"reduce_bucket_size"</span>: model_hidden_size * model_hidden_size,
<span class="hljs-string">"stage3_prefetch_bucket_size"</span>: <span class="hljs-number">0.9</span> * model_hidden_size * model_hidden_size,
<span class="hljs-string">"stage3_param_persistence_threshold"</span>: <span class="hljs-number">10</span> * model_hidden_size
},
<span class="hljs-string">"steps_per_print"</span>: <span class="hljs-number">2000</span>,
<span class="hljs-string">"train_batch_size"</span>: train_batch_size,
<span class="hljs-string">"train_micro_batch_size_per_gpu"</span>: <span class="hljs-number">1</span>,
<span class="hljs-string">"wall_clock_breakdown"</span>: <span class="hljs-literal">False</span>
}
<span class="hljs-comment"># fmt: on</span>
<span class="hljs-comment"># next line instructs transformers to partition the model directly over multiple gpus using</span>
<span class="hljs-comment"># deepspeed.zero.Init when model's `from_pretrained` method is called.</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># **it has to be run before loading the model AutoModelForSeq2SeqLM.from_pretrained(model_name)**</span>
<span class="hljs-comment">#</span>
<span class="hljs-comment"># otherwise the model will first be loaded normally and only partitioned at forward time which is</span>
<span class="hljs-comment"># less efficient and when there is little CPU RAM may fail</span>
dschf = HfDeepSpeedConfig(ds_config) <span class="hljs-comment"># keep this object alive</span>
<span class="hljs-comment"># now a model can be loaded.</span>
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
<span class="hljs-comment"># initialise Deepspeed ZeRO and store only the engine object</span>
ds_engine = deepspeed.initialize(model=model, config_params=ds_config)[<span class="hljs-number">0</span>]
ds_engine.module.<span class="hljs-built_in">eval</span>() <span class="hljs-comment"># inference</span>
<span class="hljs-comment"># Deepspeed ZeRO can process unrelated inputs on each GPU. So for 2 gpus you process 2 inputs at once.</span>
<span class="hljs-comment"># If you use more GPUs adjust for more.</span>
<span class="hljs-comment"># And of course if you have just one input to process you then need to pass the same string to both gpus</span>
<span class="hljs-comment"># If you use only one GPU, then you will have only rank 0.</span>
rank = torch.distributed.get_rank()
<span class="hljs-keyword">if</span> rank == <span class="hljs-number">0</span>:
text_in = <span class="hljs-string">"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"</span>
<span class="hljs-keyword">elif</span> rank == <span class="hljs-number">1</span>:
text_in = <span class="hljs-string">"Is this review positive or negative? Review: this is the worst restaurant ever"</span>
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer.encode(text_in, return_tensors=<span class="hljs-string">"pt"</span>).to(device=local_rank)
<span class="hljs-keyword">with</span> torch.no_grad():
outputs = ds_engine.module.generate(inputs, synced_gpus=<span class="hljs-literal">True</span>)
text_out = tokenizer.decode(outputs[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"rank<span class="hljs-subst">{rank}</span>:\n in=<span class="hljs-subst">{text_in}</span>\n out=<span class="hljs-subst">{text_out}</span>"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Let’s save it as <code>t0.py</code> and run it:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START -->$ deepspeed --num_gpus <span class="hljs-number">2</span> t0.py
rank0:
<span class="hljs-keyword">in</span>=Is <span class="hljs-keyword">this</span> review positive or negative? Review: <span class="hljs-keyword">this</span> <span class="hljs-keyword">is</span> the best cast iron skillet you will ever buy
<span class="hljs-keyword">out</span>=Positive
rank1:
<span class="hljs-keyword">in</span>=Is <span class="hljs-keyword">this</span> review positive or negative? Review: <span class="hljs-keyword">this</span> <span class="hljs-keyword">is</span> the worst restaurant ever
<span class="hljs-keyword">out</span>=negative<!-- HTML_TAG_END --></pre></div>
<p>This was a very basic example and you will want to adapt it to your needs.</p>
<h2 class="relative group"><a id="main-deepspeed-resources" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#main-deepspeed-resources"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Main DeepSpeed Resources
</span></h2>
<ul><li><a href="https://github.com/microsoft/deepspeed" rel="nofollow">Project’s github</a></li>
<li><a href="https://www.deepspeed.ai/getting-started/" rel="nofollow">Usage docs</a></li>
<li><a href="https://deepspeed.readthedocs.io/en/latest/index.html" rel="nofollow">API docs</a></li>
<li><a href="https://www.microsoft.com/en-us/research/search/?q=deepspeed" rel="nofollow">Blog posts</a></li></ul>
<p>Papers:</p>
<ul><li><a href="https://arxiv.org/abs/1910.02054" rel="nofollow">ZeRO: Memory Optimizations Toward Training Trillion Parameter Models</a></li>
<li><a href="https://arxiv.org/abs/2101.06840" rel="nofollow">ZeRO-Offload: Democratizing Billion-Scale Model Training</a></li>
<li><a href="https://arxiv.org/abs/2104.07857" rel="nofollow">ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning</a></li></ul>
<p>Finally, please, remember that, HuggingFace <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> only integrates DeepSpeed, therefore if you
have any problems or questions with regards to DeepSpeed usage, please, file an issue with <a href="https://github.com/microsoft/DeepSpeed/issues" rel="nofollow">DeepSpeed GitHub</a>.</p>
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content="{"local":"model-outputs","sections":[{"local":"transformers.file_utils.ModelOutput","title":"ModelOutput"},{"local":"transformers.modeling_outputs.BaseModelOutput","title":"BaseModelOutput"},{"local":"transformers.modeling_outputs.BaseModelOutputWithPooling","title":"BaseModelOutputWithPooling"},{"local":"transformers.modeling_outputs.BaseModelOutputWithCrossAttentions","title":"BaseModelOutputWithCrossAttentions"},{"local":"transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions","title":"BaseModelOutputWithPoolingAndCrossAttentions"},{"local":"transformers.modeling_outputs.BaseModelOutputWithPast","title":"BaseModelOutputWithPast"},{"local":"transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions","title":"BaseModelOutputWithPastAndCrossAttentions"},{"local":"transformers.modeling_outputs.Seq2SeqModelOutput","title":"Seq2SeqModelOutput"},{"local":"transformers.modeling_outputs.CausalLMOutput","title":"CausalLMOutput"},{"local":"transformers.modeling_outputs.CausalLMOutputWithCrossAttentions","title":"CausalLMOutputWithCrossAttentions"},{"local":"transformers.modeling_outputs.CausalLMOutputWithPast","title":"CausalLMOutputWithPast"},{"local":"transformers.modeling_outputs.MaskedLMOutput","title":"MaskedLMOutput"},{"local":"transformers.modeling_outputs.Seq2SeqLMOutput","title":"Seq2SeqLMOutput"},{"local":"transformers.modeling_outputs.NextSentencePredictorOutput","title":"NextSentencePredictorOutput"},{"local":"transformers.modeling_outputs.SequenceClassifierOutput","title":"SequenceClassifierOutput"},{"local":"transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput","title":"Seq2SeqSequenceClassifierOutput"},{"local":"transformers.modeling_outputs.MultipleChoiceModelOutput","title":"MultipleChoiceModelOutput"},{"local":"transformers.modeling_outputs.TokenClassifierOutput","title":"TokenClassifierOutput"},{"local":"transformers.modeling_outputs.QuestionAnsweringModelOutput","title":"QuestionAnsweringModelOutput"},{"local":"transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput","title":"Seq2SeqQuestionAnsweringModelOutput"},{"local":"transformers.modeling_tf_outputs.TFBaseModelOutput","title":"TFBaseModelOutput"},{"local":"transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling","title":"TFBaseModelOutputWithPooling"},{"local":"transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions","title":"TFBaseModelOutputWithPoolingAndCrossAttentions"},{"local":"transformers.modeling_tf_outputs.TFBaseModelOutputWithPast","title":"TFBaseModelOutputWithPast"},{"local":"transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions","title":"TFBaseModelOutputWithPastAndCrossAttentions"},{"local":"transformers.modeling_tf_outputs.TFSeq2SeqModelOutput","title":"TFSeq2SeqModelOutput"},{"local":"transformers.modeling_tf_outputs.TFCausalLMOutput","title":"TFCausalLMOutput"},{"local":"transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions","title":"TFCausalLMOutputWithCrossAttentions"},{"local":"transformers.modeling_tf_outputs.TFCausalLMOutputWithPast","title":"TFCausalLMOutputWithPast"},{"local":"transformers.modeling_tf_outputs.TFMaskedLMOutput","title":"TFMaskedLMOutput"},{"local":"transformers.modeling_tf_outputs.TFSeq2SeqLMOutput","title":"TFSeq2SeqLMOutput"},{"local":"transformers.modeling_tf_outputs.TFNextSentencePredictorOutput","title":"TFNextSentencePredictorOutput"},{"local":"transformers.modeling_tf_outputs.TFSequenceClassifierOutput","title":"TFSequenceClassifierOutput"},{"local":"transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput","title":"TFSeq2SeqSequenceClassifierOutput"},{"local":"transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput","title":"TFMultipleChoiceModelOutput"},{"local":"transformers.modeling_tf_outputs.TFTokenClassifierOutput","title":"TFTokenClassifierOutput"},{"local":"transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput","title":"TFQuestionAnsweringModelOutput"},{"local":"transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput","title":"TFSeq2SeqQuestionAnsweringModelOutput"},{"local":"transformers.modeling_flax_outputs.FlaxBaseModelOutput","title":"FlaxBaseModelOutput"},{"local":"transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast","title":"FlaxBaseModelOutputWithPast"},{"local":"transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling","title":"FlaxBaseModelOutputWithPooling"},{"local":"transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions","title":"FlaxBaseModelOutputWithPastAndCrossAttentions"},{"local":"transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput","title":"FlaxSeq2SeqModelOutput"},{"local":"transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions","title":"FlaxCausalLMOutputWithCrossAttentions"},{"local":"transformers.modeling_flax_outputs.FlaxMaskedLMOutput","title":"FlaxMaskedLMOutput"},{"local":"transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput","title":"FlaxSeq2SeqLMOutput"},{"local":"transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput","title":"FlaxNextSentencePredictorOutput"},{"local":"transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput","title":"FlaxSequenceClassifierOutput"},{"local":"transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput","title":"FlaxSeq2SeqSequenceClassifierOutput"},{"local":"transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput","title":"FlaxMultipleChoiceModelOutput"},{"local":"transformers.modeling_flax_outputs.FlaxTokenClassifierOutput","title":"FlaxTokenClassifierOutput"},{"local":"transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput","title":"FlaxQuestionAnsweringModelOutput"},{"local":"transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput","title":"FlaxSeq2SeqQuestionAnsweringModelOutput"}],"title":"Model outputs"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="model-outputs" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#model-outputs"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Model outputs
</span></h1>
<p>All models have outputs that are instances of subclasses of <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a>. Those are
data structures containing all the information returned by the model, but that can also be used as tuples or
dictionaries.</p>
<p>Let’s see of this looks on an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BertTokenizer, BertForSequenceClassification
<span class="hljs-keyword">import</span> torch
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
model = BertForSequenceClassification.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
inputs = tokenizer(<span class="hljs-string">"Hello, my dog is cute"</span>, return_tensors=<span class="hljs-string">"pt"</span>)
labels = torch.tensor([<span class="hljs-number">1</span>]).unsqueeze(<span class="hljs-number">0</span>) <span class="hljs-comment"># Batch size 1</span>
outputs = model(**inputs, labels=labels)<!-- HTML_TAG_END --></pre></div>
<p>The <code>outputs</code> object is a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput">SequenceClassifierOutput</a>, as we can see in the
documentation of that class below, it means it has an optional <code>loss</code>, a <code>logits</code> an optional <code>hidden_states</code> and
an optional <code>attentions</code> attribute. Here we have the <code>loss</code> since we passed along <code>labels</code>, but we don’t have
<code>hidden_states</code> and <code>attentions</code> because we didn’t pass <code>output_hidden_states=True</code> or
<code>output_attentions=True</code>.</p>
<p>You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get <code>None</code>. Here for instance <code>outputs.loss</code> is the loss computed by the model, and <code>outputs.attentions</code> is
<code>None</code>.</p>
<p>When considering our <code>outputs</code> object as tuple, it only considers the attributes that don’t have <code>None</code> values.
Here for instance, it has two elements, <code>loss</code> then <code>logits</code>, so</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->outputs[:<span class="hljs-number">2</span>]<!-- HTML_TAG_END --></pre></div>
<p>will return the tuple <code>(outputs.loss, outputs.logits)</code> for instance.</p>
<p>When considering our <code>outputs</code> object as dictionary, it only considers the attributes that don’t have <code>None</code>
values. Here for instance, it has two keys that are <code>loss</code> and <code>logits</code>.</p>
<p>We document here the generic model outputs that are used by more than one model type. Specific output types are
documented on their corresponding model page.</p>
<h2 class="relative group"><a id="transformers.file_utils.ModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.ModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.ModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.file_utils.</span><span class="font-semibold">ModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.file_utils.ModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.ModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2614" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Base class for all model outputs as dataclass. Has a <code>__getitem__</code> that allows indexing by integer or slice (like a
tuple) or strings (like a dictionary) that will ignore the <code>None</code> attributes. Otherwise behaves like a regular
python dictionary.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>You can’t unpack a <code>ModelOutput</code> directly. Use the <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput.to_tuple">to_tuple()</a> method to convert it to a
tuple before.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.ModelOutput.to_tuple"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>to_tuple</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.ModelOutput.to_tuple" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.ModelOutput.to_tuple"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2703" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Convert self to a tuple containing all the attributes/keys that are not <code>None</code>.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.BaseModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BaseModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.BaseModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">BaseModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.BaseModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.BaseModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L24" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutput.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutput.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs, with potential hidden states and attentions.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.BaseModelOutputWithPooling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPooling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BaseModelOutputWithPooling
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.BaseModelOutputWithPooling"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">BaseModelOutputWithPooling</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.BaseModelOutputWithPooling" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.BaseModelOutputWithPooling"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L50" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pooler_output<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPooling.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPooling.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPooling.pooler_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPooling.pooler_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) —
Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPooling.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPooling.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPooling.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPooling.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that also contains a pooling of the last hidden states.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.BaseModelOutputWithCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BaseModelOutputWithCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.BaseModelOutputWithCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">BaseModelOutputWithCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.BaseModelOutputWithCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L121" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs, with potential hidden states and attentions.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BaseModelOutputWithPoolingAndCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">BaseModelOutputWithPoolingAndCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L154" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pooler_output<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.pooler_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.pooler_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) —
Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that also contains a pooling of the last hidden states.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.BaseModelOutputWithPast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BaseModelOutputWithPast
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.BaseModelOutputWithPast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">BaseModelOutputWithPast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.BaseModelOutputWithPast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.BaseModelOutputWithPast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L82" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPast.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPast.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPast.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPast.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPast.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPast.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPast.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPast.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BaseModelOutputWithPastAndCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">BaseModelOutputWithPastAndCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L203" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.Seq2SeqModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Seq2SeqModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.Seq2SeqModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">Seq2SeqModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.Seq2SeqModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.Seq2SeqModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L249" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqModelOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqModelOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.CausalLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>CausalLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.CausalLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">CausalLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.CausalLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.CausalLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L310" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Language modeling loss (for next-token prediction).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for causal language model (or autoregressive) outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>CausalLMOutputWithCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">CausalLMOutputWithCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L375" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Language modeling loss (for next-token prediction).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for causal language model (or autoregressive) outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.CausalLMOutputWithPast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithPast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>CausalLMOutputWithPast
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.CausalLMOutputWithPast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">CausalLMOutputWithPast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.CausalLMOutputWithPast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.CausalLMOutputWithPast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L339" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithPast.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithPast.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Language modeling loss (for next-token prediction).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithPast.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithPast.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithPast.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithPast.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>)</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithPast.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithPast.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.CausalLMOutputWithPast.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.CausalLMOutputWithPast.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for causal language model (or autoregressive) outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.MaskedLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MaskedLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>MaskedLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.MaskedLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">MaskedLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.MaskedLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.MaskedLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L455" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MaskedLMOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MaskedLMOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Masked language modeling (MLM) loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MaskedLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MaskedLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MaskedLMOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MaskedLMOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MaskedLMOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MaskedLMOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for masked language models outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.Seq2SeqLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Seq2SeqLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.Seq2SeqLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">Seq2SeqLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.Seq2SeqLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.Seq2SeqLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L484" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Language modeling loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqLMOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqLMOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for sequence-to-sequence language models outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.NextSentencePredictorOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.NextSentencePredictorOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>NextSentencePredictorOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.NextSentencePredictorOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">NextSentencePredictorOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.NextSentencePredictorOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.NextSentencePredictorOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L544" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.NextSentencePredictorOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.NextSentencePredictorOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>next_sentence_label</code> is provided) —
Next sequence prediction (classification) loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.NextSentencePredictorOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.NextSentencePredictorOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) —
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.NextSentencePredictorOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.NextSentencePredictorOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.NextSentencePredictorOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.NextSentencePredictorOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of models predicting if two sentences are consecutive or not.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.SequenceClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.SequenceClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>SequenceClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.SequenceClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">SequenceClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.SequenceClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.SequenceClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L574" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.SequenceClassifierOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.SequenceClassifierOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Classification (or regression if config.num_labels==1) loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.SequenceClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.SequenceClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.SequenceClassifierOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.SequenceClassifierOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.SequenceClassifierOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.SequenceClassifierOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sentence classification models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Seq2SeqSequenceClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">Seq2SeqSequenceClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L603" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>label</code> is provided) —
Classification (or regression if config.num_labels==1) loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sequence-to-sequence sentence classification models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.MultipleChoiceModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MultipleChoiceModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>MultipleChoiceModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.MultipleChoiceModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">MultipleChoiceModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.MultipleChoiceModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.MultipleChoiceModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L663" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MultipleChoiceModelOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MultipleChoiceModelOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <em>(1,)</em>, <em>optional</em>, returned when <code>labels</code> is provided) —
Classification loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MultipleChoiceModelOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MultipleChoiceModelOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>) —
<em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MultipleChoiceModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MultipleChoiceModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.MultipleChoiceModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.MultipleChoiceModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of multiple choice models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.TokenClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.TokenClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TokenClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.TokenClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">TokenClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.TokenClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.TokenClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L694" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.TokenClassifierOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.TokenClassifierOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Classification loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.TokenClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.TokenClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) —
Classification scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.TokenClassifierOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.TokenClassifierOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.TokenClassifierOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.TokenClassifierOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of token classification models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.QuestionAnsweringModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.QuestionAnsweringModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>QuestionAnsweringModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.QuestionAnsweringModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">QuestionAnsweringModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.QuestionAnsweringModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.QuestionAnsweringModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L723" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.QuestionAnsweringModelOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.QuestionAnsweringModelOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.QuestionAnsweringModelOutput.start_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.QuestionAnsweringModelOutput.start_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-start scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.QuestionAnsweringModelOutput.end_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.QuestionAnsweringModelOutput.end_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-end scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.QuestionAnsweringModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.QuestionAnsweringModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.QuestionAnsweringModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.QuestionAnsweringModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of question answering models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Seq2SeqQuestionAnsweringModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_outputs.</span><span class="font-semibold">Seq2SeqQuestionAnsweringModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_outputs.py#L755" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_logits<span class="opacity-60">: FloatTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.start_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.start_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-start scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.end_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.end_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-end scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sequence-to-sequence question answering models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFBaseModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFBaseModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFBaseModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFBaseModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFBaseModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFBaseModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L24" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutput.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutput.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs, with potential hidden states and attentions.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFBaseModelOutputWithPooling
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFBaseModelOutputWithPooling</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L50" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pooler_output<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.pooler_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.pooler_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) —
Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that also contains a pooling of the last hidden states.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFBaseModelOutputWithPoolingAndCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFBaseModelOutputWithPoolingAndCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L84" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pooler_output<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.pooler_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.pooler_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) —
Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that also contains a pooling of the last hidden states.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFBaseModelOutputWithPast
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFBaseModelOutputWithPast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L132" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPast.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFBaseModelOutputWithPastAndCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFBaseModelOutputWithPastAndCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L201" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFSeq2SeqModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFSeq2SeqModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L244" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFCausalLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFCausalLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFCausalLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFCausalLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFCausalLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFCausalLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L304" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) —
Language modeling loss (for next-token prediction).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for causal language model (or autoregressive) outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFCausalLMOutputWithCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFCausalLMOutputWithCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L369" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) —
Language modeling loss (for next-token prediction).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for causal language model (or autoregressive) outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithPast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFCausalLMOutputWithPast
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFCausalLMOutputWithPast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithPast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L333" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) —
Language modeling loss (for next-token prediction).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFCausalLMOutputWithPast.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for causal language model (or autoregressive) outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFMaskedLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMaskedLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFMaskedLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFMaskedLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFMaskedLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFMaskedLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFMaskedLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L412" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMaskedLMOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMaskedLMOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) —
Masked language modeling (MLM) loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMaskedLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMaskedLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMaskedLMOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMaskedLMOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMaskedLMOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMaskedLMOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for masked language models outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFSeq2SeqLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFSeq2SeqLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L441" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) —
Language modeling loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for sequence-to-sequence language models outputs.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFNextSentencePredictorOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFNextSentencePredictorOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFNextSentencePredictorOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFNextSentencePredictorOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFNextSentencePredictorOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFNextSentencePredictorOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFNextSentencePredictorOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L500" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>next_sentence_label</code> is provided) —
Next sentence prediction loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, 2)</code>) —
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFNextSentencePredictorOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of models predicting if two sentences are consecutive or not.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFSequenceClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFSequenceClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFSequenceClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFSequenceClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFSequenceClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L530" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSequenceClassifierOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSequenceClassifierOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Classification (or regression if config.num_labels==1) loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSequenceClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSequenceClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSequenceClassifierOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSequenceClassifierOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSequenceClassifierOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSequenceClassifierOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sentence classification models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFSeq2SeqSequenceClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFSeq2SeqSequenceClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L559" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>label</code> is provided) —
Classification (or regression if config.num_labels==1) loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sequence-to-sequence sentence classification models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFMultipleChoiceModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFMultipleChoiceModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L611" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <em>(batch_size, )</em>, <em>optional</em>, returned when <code>labels</code> is provided) —
Classification loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices)</code>) —
<em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of multiple choice models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFTokenClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFTokenClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFTokenClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFTokenClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFTokenClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFTokenClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFTokenClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L642" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFTokenClassifierOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFTokenClassifierOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of unmasked labels, returned when <code>labels</code> is provided) —
Classification loss.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFTokenClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFTokenClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) —
Classification scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFTokenClassifierOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFTokenClassifierOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFTokenClassifierOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFTokenClassifierOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of token classification models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFQuestionAnsweringModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFQuestionAnsweringModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L671" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>start_positions</code> and <code>end_positions</code> are provided) —
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.start_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.start_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-start scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.end_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.end_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-end scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of question answering models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TFSeq2SeqQuestionAnsweringModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_outputs.</span><span class="font-semibold">TFSeq2SeqQuestionAnsweringModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_outputs.py#L703" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_logits<span class="opacity-60">: Tensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[tensorflow.python.framework.ops.Tensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) —
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.start_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.start_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-start scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.end_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.end_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-end scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sequence-to-sequence question answering models.</p></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxBaseModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxBaseModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxBaseModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxBaseModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L23" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutput.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutput.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs, with potential hidden states and attentions.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxBaseModelOutputWithPast
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxBaseModelOutputWithPast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L49" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Union[typing.Dict[str, jax._src.numpy.lax_numpy.ndarray], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>Dict[str, jnp.ndarray]</code>) —
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape <em>[batch_size, max_length]</em>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs, with potential hidden states and attentions.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxBaseModelOutputWithPooling
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxBaseModelOutputWithPooling</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L79" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pooler_output<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.pooler_output" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.pooler_output"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) —
Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that also contains a pooling of the last hidden states.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxBaseModelOutputWithPastAndCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxBaseModelOutputWithPastAndCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L110" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model’s outputs that may also contain a past key/values (to speed up sequential decoding).</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxSeq2SeqModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxSeq2SeqModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L156" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">last_hidden_state<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) —
Sequence of hidden-states at the output of the last layer of the decoder of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxCausalLMOutputWithCrossAttentions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxCausalLMOutputWithCrossAttentions</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L217" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>jnp.ndarray</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key, value
states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for causal language model (or autoregressive) outputs.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxMaskedLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxMaskedLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxMaskedLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxMaskedLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxMaskedLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L258" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxMaskedLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMaskedLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxMaskedLMOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMaskedLMOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxMaskedLMOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMaskedLMOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for masked language models outputs.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxSeq2SeqLMOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxSeq2SeqLMOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L287" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for sequence-to-sequence language models outputs.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxNextSentencePredictorOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxNextSentencePredictorOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L344" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, 2)</code>) —
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of models predicting if two sentences are consecutive or not.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxSequenceClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxSequenceClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L371" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.num_labels)</code>) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sentence classification models.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxSeq2SeqSequenceClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxSeq2SeqSequenceClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L397" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.num_labels)</code>) —
Classification (or regression if config.num_labels==1) scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sequence-to-sequence sentence classification models.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxMultipleChoiceModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxMultipleChoiceModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L454" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, num_choices)</code>) —
<em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of multiple choice models.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxTokenClassifierOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxTokenClassifierOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxTokenClassifierOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxTokenClassifierOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L482" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxTokenClassifierOutput.logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput.logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) —
Classification scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxTokenClassifierOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxTokenClassifierOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of token classification models.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxQuestionAnsweringModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxQuestionAnsweringModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L508" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.start_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.start_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-start scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.end_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.end_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-end scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of question answering models.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FlaxSeq2SeqQuestionAnsweringModelOutput
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_flax_outputs.</span><span class="font-semibold">FlaxSeq2SeqQuestionAnsweringModelOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_flax_outputs.py#L537" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_logits<span class="opacity-60">: ndarray = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">past_key_values<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_last_hidden_state<span class="opacity-60">: typing.Optional[jax._src.numpy.lax_numpy.ndarray] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[jax._src.numpy.lax_numpy.ndarray]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.start_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.start_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-start scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.end_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.end_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Span-end scores (before SoftMax).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.past_key_values" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.past_key_values"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) —
Tuple of <code>tuple(jnp.ndarray)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.encoder_last_hidden_state" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.encoder_last_hidden_state"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) —
Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of sequence-to-sequence question answering models.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
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</span>
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</p>
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<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
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<h1 class="relative group"><a id="logging" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#logging"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Logging
</span></h1>
<p>🤗 Transformers has a centralized logging system, so that you can setup the verbosity of the library easily.</p>
<p>Currently the default verbosity of the library is <code>WARNING</code>.</p>
<p>To change the level of verbosity, just use one of the direct setters. For instance, here is how to change the verbosity
to the INFO level.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> transformers
transformers.logging.set_verbosity_info()<!-- HTML_TAG_END --></pre></div>
<p>You can also use the environment variable <code>TRANSFORMERS_VERBOSITY</code> to override the default verbosity. You can set it
to one of the following: <code>debug</code>, <code>info</code>, <code>warning</code>, <code>error</code>, <code>critical</code>. For example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->TRANSFORMERS_VERBOSITY=error ./myprogram.py<!-- HTML_TAG_END --></pre></div>
<p>Additionally, some <code>warnings</code> can be disabled by setting the environment variable
<code>TRANSFORMERS_NO_ADVISORY_WARNINGS</code> to a true value, like <em>1</em>. This will disable any warning that is logged using
<code>logger.warning_advice()</code> For example:</p>
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<pre><!-- HTML_TAG_START -->TRANSFORMERS_NO_ADVISORY_WARNINGS=1 ./myprogram.py<!-- HTML_TAG_END --></pre></div>
<p>Here is an example of how to use <code>logging</code> in a module:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.utils <span class="hljs-keyword">import</span> logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
logger.info(<span class="hljs-string">"INFO"</span>)
logger.warning(<span class="hljs-string">"WARN"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Above, a <code>logger</code> instance is created from <code>logging.get_logger(__name__)</code>. If you want to use <code>logging</code> in a script, you shouldn’t pass <code>__name__</code> to <code>logging.get_logger</code>. For example:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.utils <span class="hljs-keyword">import</span> logging
<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">"__main__"</span>:
logging.set_verbosity_info()
<span class="hljs-comment"># leave it empy or use a string</span>
logger = logging.get_logger()
logger.info(<span class="hljs-string">"INFO"</span>)
logger.warning(<span class="hljs-string">"WARN"</span>)<!-- HTML_TAG_END --></pre></div>
<p>All the methods of this logging module are documented below, the main ones are
<a href="/docs/transformers/pr_16143/en/main_classes/logging#transformers.utils.logging.get_verbosity">logging.get_verbosity()</a> to get the current level of verbosity in the logger and
<a href="/docs/transformers/pr_16143/en/main_classes/logging#transformers.utils.logging.set_verbosity">logging.set_verbosity()</a> to set the verbosity to the level of your choice. In order (from the least
verbose to the most verbose), those levels (with their corresponding int values in parenthesis) are:</p>
<ul><li><code>transformers.logging.CRITICAL</code> or <code>transformers.logging.FATAL</code> (int value, 50): only report the most
critical errors.</li>
<li><code>transformers.logging.ERROR</code> (int value, 40): only report errors.</li>
<li><code>transformers.logging.WARNING</code> or <code>transformers.logging.WARN</code> (int value, 30): only reports error and
warnings. This the default level used by the library.</li>
<li><code>transformers.logging.INFO</code> (int value, 20): reports error, warnings and basic information.</li>
<li><code>transformers.logging.DEBUG</code> (int value, 10): report all information.</li></ul>
<p>By default, <code>tqdm</code> progress bars will be displayed during model download. <a href="/docs/transformers/pr_16143/en/main_classes/logging#transformers.utils.logging.disable_progress_bar">logging.disable_progress_bar()</a> and <a href="/docs/transformers/pr_16143/en/main_classes/logging#transformers.utils.logging.enable_progress_bar">logging.enable_progress_bar()</a> can be used to suppress or unsuppress this behavior.</p>
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<span>Base setters
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.set_verbosity_error"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.set_verbosity_error</span></h4><!-- HTML_TAG_END -->
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<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L184" target="_blank"><span><</span>
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<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Set the verbosity to the <code>ERROR</code> level.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.set_verbosity_warning"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.set_verbosity_warning</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.set_verbosity_warning" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.set_verbosity_warning"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L174" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Set the verbosity to the <code>WARNING</code> level.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.set_verbosity_info"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.set_verbosity_info</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.set_verbosity_info" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.set_verbosity_info"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L169" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Set the verbosity to the <code>INFO</code> level.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.set_verbosity_debug"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.set_verbosity_debug</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.set_verbosity_debug" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.set_verbosity_debug"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L179" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Set the verbosity to the <code>DEBUG</code> level.</p></div>
<h2 class="relative group"><a id="transformers.utils.logging.get_verbosity" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.utils.logging.get_verbosity"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Other functions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.get_verbosity"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.get_verbosity</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.get_verbosity" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.get_verbosity"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L127" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.utils.logging.get_verbosity.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The logging level.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Return the current level for the 🤗 Transformers’s root logger as an int.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>🤗 Transformers has following logging levels:</p>
<ul><li>50: <code>transformers.logging.CRITICAL</code> or <code>transformers.logging.FATAL</code></li>
<li>40: <code>transformers.logging.ERROR</code></li>
<li>30: <code>transformers.logging.WARNING</code> or <code>transformers.logging.WARN</code></li>
<li>20: <code>transformers.logging.INFO</code></li>
<li>10: <code>transformers.logging.DEBUG</code></li></ul></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.set_verbosity"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.set_verbosity</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.set_verbosity" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.set_verbosity"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L150" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbosity<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.utils.logging.set_verbosity.verbosity" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.utils.logging.set_verbosity.verbosity"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbosity</strong> (<code>int</code>) —
Logging level, e.g., one of:</p>
<ul>
<li><code>transformers.logging.CRITICAL</code> or <code>transformers.logging.FATAL</code></li>
<li><code>transformers.logging.ERROR</code></li>
<li><code>transformers.logging.WARNING</code> or <code>transformers.logging.WARN</code></li>
<li><code>transformers.logging.INFO</code></li>
<li><code>transformers.logging.DEBUG</code></li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Set the verbosity level for the 🤗 Transformers’s root logger.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.get_logger"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.get_logger</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.get_logger" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.get_logger"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L113" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Return a logger with the specified name.</p>
<p>This function is not supposed to be directly accessed unless you are writing a custom transformers module.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.enable_default_handler"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.enable_default_handler</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.enable_default_handler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.enable_default_handler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L198" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Enable the default handler of the HuggingFace Transformers’s root logger.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.disable_default_handler"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.disable_default_handler</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.disable_default_handler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.disable_default_handler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L189" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Disable the default handler of the HuggingFace Transformers’s root logger.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.enable_explicit_format"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.enable_explicit_format</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.enable_explicit_format" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.enable_explicit_format"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L244" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Enable explicit formatting for every HuggingFace Transformers’s logger. The explicit formatter is as follows:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --> [LEVELNAME|<span class="hljs-type">FILENAME</span>|<span class="hljs-type">LINE</span> NUMBER] TIME >> MESSAGE<!-- HTML_TAG_END --></pre></div>
<p>All handlers currently bound to the root logger are affected by this method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.reset_format"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.reset_format</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.reset_format" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.reset_format"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L259" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Resets the formatting for HuggingFace Transformers’s loggers.</p>
<p>All handlers currently bound to the root logger are affected by this method.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.enable_progress_bar"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.enable_progress_bar</span></h4><!-- HTML_TAG_END -->
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<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L335" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Enable tqdm progress bar.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.utils.logging.disable_progress_bar"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.utils.logging.disable_progress_bar</span></h4><!-- HTML_TAG_END -->
<a id="transformers.utils.logging.disable_progress_bar" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.utils.logging.disable_progress_bar"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/utils/logging.py#L341" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Enable tqdm progress bar.</p></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_16143/en | hf_public_repos/doc-build-dev/transformers/pr_16143/en/main_classes/onnx.html | <meta charset="utf-8" /><meta http-equiv="content-security-policy" content=""><meta name="hf:doc:metadata" content="{"local":"exporting-transformers-models-to-onnx","sections":[{"local":"onnx-configurations","sections":[{"local":"transformers.onnx.OnnxConfig","title":"OnnxConfig"},{"local":"transformers.onnx.OnnxConfigWithPast","title":"OnnxConfigWithPast"},{"local":"transformers.onnx.OnnxSeq2SeqConfigWithPast","title":"OnnxSeq2SeqConfigWithPast"}],"title":"ONNX Configurations"},{"local":"onnx-features","sections":[{"local":"transformers.onnx.FeaturesManager","title":"FeaturesManager"}],"title":"ONNX Features"}],"title":"Exporting 🤗 Transformers models to ONNX"}" data-svelte="svelte-1phssyn">
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<h1 class="relative group"><a id="exporting-transformers-models-to-onnx" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#exporting-transformers-models-to-onnx"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Exporting 🤗 Transformers models to ONNX
</span></h1>
<p>🤗 Transformers provides a <code>transformers.onnx</code> package that enables you to
convert model checkpoints to an ONNX graph by leveraging configuration objects.</p>
<p>See the <a href="../serialization">guide</a> on exporting 🤗 Transformers models for more
details.</p>
<h2 class="relative group"><a id="onnx-configurations" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#onnx-configurations"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ONNX Configurations
</span></h2>
<p>We provide three abstract classes that you should inherit from, depending on the
type of model architecture you wish to export:</p>
<ul><li>Encoder-based models inherit from <a href="/docs/transformers/pr_16143/en/main_classes/onnx#transformers.onnx.OnnxConfig">OnnxConfig</a></li>
<li>Decoder-based models inherit from <a href="/docs/transformers/pr_16143/en/main_classes/onnx#transformers.onnx.OnnxConfigWithPast">OnnxConfigWithPast</a></li>
<li>Encoder-decoder models inherit from <a href="/docs/transformers/pr_16143/en/main_classes/onnx#transformers.onnx.OnnxSeq2SeqConfigWithPast">OnnxSeq2SeqConfigWithPast</a></li></ul>
<h3 class="relative group"><a id="transformers.onnx.OnnxConfig" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>OnnxConfig
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfig"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.onnx.</span><span class="font-semibold">OnnxConfig</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfig" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfig"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L68" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = 'default'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">patching_specs<span class="opacity-60">: typing.List[transformers.onnx.config.PatchingSpec] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfig.flatten_output_collection_property"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>flatten_output_collection_property</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfig.flatten_output_collection_property" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfig.flatten_output_collection_property"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L320" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">name<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">field<span class="opacity-60">: typing.Iterable[typing.Any]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>(Dict[str, Any])</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.onnx.OnnxConfig.flatten_output_collection_property.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>(Dict[str, Any])</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Outputs with flattened structure and key mapping this new structure.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Flatten any potential nested structure expanding the name of the field with the index of the element within the
structure.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfig.from_model_config"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_model_config</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfig.from_model_config" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfig.from_model_config"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L109" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = 'default'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Instantiate a OnnxConfig for a specific model</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfig.generate_dummy_inputs"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>generate_dummy_inputs</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfig.generate_dummy_inputs" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L239" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">preprocessor<span class="opacity-60">: typing.Union[ForwardRef('PreTrainedTokenizerBase'), ForwardRef('FeatureExtractionMixin')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_size<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">seq_length<span class="opacity-60">: int = -1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_pair<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: typing.Optional[transformers.file_utils.TensorType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_channels<span class="opacity-60">: int = 3</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">image_width<span class="opacity-60">: int = 40</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">image_height<span class="opacity-60">: int = 40</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60">: PreTrainedTokenizerBase = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.OnnxConfig.generate_dummy_inputs.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The batch size to export the model for (-1 means dynamic axis).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.OnnxConfig.generate_dummy_inputs.seq_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs.seq_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>seq_length</strong> (<code>int</code>, <em>optional</em>, defaults to -1) —
The sequence length to export the model for (-1 means dynamic axis).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.OnnxConfig.generate_dummy_inputs.is_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs.is_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_pair</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Indicate if the input is a pair (sentence 1, sentence 2)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.OnnxConfig.generate_dummy_inputs.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>TensorType</code>, <em>optional</em>, defaults to <code>None</code>) —
The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.OnnxConfig.generate_dummy_inputs.num_channels" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs.num_channels"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) —
The number of channels of the generated images.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.OnnxConfig.generate_dummy_inputs.image_width" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs.image_width"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>image_width</strong> (<code>int</code>, <em>optional</em>, defaults to 40) —
The width of the generated images.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.OnnxConfig.generate_dummy_inputs.image_height" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfig.generate_dummy_inputs.image_height"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>image_height</strong> (<code>int</code>, <em>optional</em>, defaults to 40) —
The height of the generated images.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Generate inputs to provide to the ONNX exporter for the specific framework</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfig.use_external_data_format"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>use_external_data_format</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfig.use_external_data_format" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfig.use_external_data_format"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L213" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_parameters<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Flag indicating if the model requires using external data format</p></div></div>
<h3 class="relative group"><a id="transformers.onnx.OnnxConfigWithPast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxConfigWithPast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>OnnxConfigWithPast
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfigWithPast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.onnx.</span><span class="font-semibold">OnnxConfigWithPast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfigWithPast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfigWithPast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L339" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = 'default'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">patching_specs<span class="opacity-60">: typing.List[transformers.onnx.config.PatchingSpec] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_past<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>fill_with_past_key_values_</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfigWithPast.fill_with_past_key_values_"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L441" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs_or_outputs<span class="opacity-60">: typing.Mapping[str, typing.Mapping[int, str]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">direction<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Fill the input_or_ouputs mapping with past_key_values dynamic axes considering.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxConfigWithPast.with_past"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>with_past</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxConfigWithPast.with_past" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxConfigWithPast.with_past"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L350" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = 'default'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Instantiate a OnnxConfig with <code>use_past</code> attribute set to True</p></div></div>
<h3 class="relative group"><a id="transformers.onnx.OnnxSeq2SeqConfigWithPast" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.OnnxSeq2SeqConfigWithPast"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>OnnxSeq2SeqConfigWithPast
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.OnnxSeq2SeqConfigWithPast"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.onnx.</span><span class="font-semibold">OnnxSeq2SeqConfigWithPast</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.onnx.OnnxSeq2SeqConfigWithPast" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.OnnxSeq2SeqConfigWithPast"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/config.py#L474" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str = 'default'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">patching_specs<span class="opacity-60">: typing.List[transformers.onnx.config.PatchingSpec] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_past<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div>
<h2 class="relative group"><a id="onnx-features" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#onnx-features"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>ONNX Features
</span></h2>
<p>Each ONNX configuration is associated with a set of <em>features</em> that enable you
to export models for different types of topologies or tasks.</p>
<h3 class="relative group"><a id="transformers.onnx.FeaturesManager" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>FeaturesManager
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.FeaturesManager"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.onnx.</span><span class="font-semibold">FeaturesManager</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.onnx.FeaturesManager" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.FeaturesManager"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/features.py#L84" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.FeaturesManager.check_supported_model_or_raise"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>check_supported_model_or_raise</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.FeaturesManager.check_supported_model_or_raise" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.FeaturesManager.check_supported_model_or_raise"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/features.py#L355" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: typing.Union[transformers.modeling_utils.PreTrainedModel, transformers.modeling_tf_utils.TFPreTrainedModel]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature<span class="opacity-60">: str = 'default'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Check whether or not the model has the requested features.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.FeaturesManager.get_model_class_for_feature"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_model_class_for_feature</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.FeaturesManager.get_model_class_for_feature" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.FeaturesManager.get_model_class_for_feature"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/features.py#L300" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.FeaturesManager.get_model_class_for_feature.feature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager.get_model_class_for_feature.feature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>feature</strong> (<code>str</code>) —
The feature required.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.FeaturesManager.get_model_class_for_feature.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager.get_model_class_for_feature.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"pt"</code>) —
The framework to use for the export.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Attempts to retrieve an AutoModel class from a feature name.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.FeaturesManager.get_model_from_feature"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_model_from_feature</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.FeaturesManager.get_model_from_feature" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.FeaturesManager.get_model_from_feature"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/features.py#L327" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">feature<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">framework<span class="opacity-60">: str = 'pt'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.FeaturesManager.get_model_from_feature.feature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager.get_model_from_feature.feature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>feature</strong> (<code>str</code>) —
The feature required.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.FeaturesManager.get_model_from_feature.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager.get_model_from_feature.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<code>str</code>) —
The name of the model to export.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.FeaturesManager.get_model_from_feature.framework" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager.get_model_from_feature.framework"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>framework</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"pt"</code>) —
The framework to use for the export.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Attempts to retrieve a model from a model’s name and the feature to be enabled.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.onnx.FeaturesManager.get_supported_features_for_model_type"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_supported_features_for_model_type</span></h4><!-- HTML_TAG_END -->
<a id="transformers.onnx.FeaturesManager.get_supported_features_for_model_type" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.onnx.FeaturesManager.get_supported_features_for_model_type"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/onnx/features.py#L255" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model_type<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.FeaturesManager.get_supported_features_for_model_type.model_type" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager.get_supported_features_for_model_type.model_type"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_type</strong> (<code>str</code>) —
The model type to retrieve the supported features for.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.onnx.FeaturesManager.get_supported_features_for_model_type.model_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.onnx.FeaturesManager.get_supported_features_for_model_type.model_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_name</strong> (<code>str</code>, <em>optional</em>) —
The name attribute of the model object, only used for the exception message.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Tries to retrieve the feature -> OnnxConfig constructor map from the model type.</p></div></div>
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<h1 class="relative group"><a id="language-modeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#language-modeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Language modeling
</span></h1>
<p>Language modeling predicts words in a sentence. There are two forms of language modeling.</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/Vpjb1lu0MDk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on the left.</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/mqElG5QJWUg" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally.</p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/distilgpt2" rel="nofollow">DistilGPT2</a> for causal language modeling and <a href="https://huggingface.co/distilroberta-base" rel="nofollow">DistilRoBERTa</a> for masked language modeling on the <a href="https://www.reddit.com/r/askscience/" rel="nofollow">r/askscience</a> subset of the <a href="https://huggingface.co/datasets/eli5" rel="nofollow">ELI5</a> dataset.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>You can fine-tune other architectures for language modeling such as <a href="https://huggingface.co/EleutherAI/gpt-neo-125M" rel="nofollow">GPT-Neo</a>, <a href="https://huggingface.co/EleutherAI/gpt-j-6B" rel="nofollow">GPT-J</a>, and <a href="https://huggingface.co/bert-base-uncased" rel="nofollow">BERT</a>, following the same steps presented in this guide!</p>
<p>See the text generation <a href="https://huggingface.co/tasks/text-generation" rel="nofollow">task page</a> and fill mask <a href="https://huggingface.co/tasks/fill-mask" rel="nofollow">task page</a> for more information about their associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-eli5-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-eli5-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load ELI5 dataset
</span></h2>
<p>Load only the first 5000 rows of the ELI5 dataset from the 🤗 Datasets library since it is pretty large:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>eli5 = load_dataset(<span class="hljs-string">"eli5"</span>, split=<span class="hljs-string">"train_asks[:5000]"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Split this dataset into a train and test set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->eli5 = eli5.train_test_split(test_size=<span class="hljs-number">0.2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>eli5[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'answers'</span>: {<span class="hljs-string">'a_id'</span>: [<span class="hljs-string">'c3d1aib'</span>, <span class="hljs-string">'c3d4lya'</span>],
<span class="hljs-string">'score'</span>: [<span class="hljs-number">6</span>, <span class="hljs-number">3</span>],
<span class="hljs-string">'text'</span>: [<span class="hljs-string">"The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."</span>,
<span class="hljs-string">"Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"</span>]},
<span class="hljs-string">'answers_urls'</span>: {<span class="hljs-string">'url'</span>: []},
<span class="hljs-string">'document'</span>: <span class="hljs-string">''</span>,
<span class="hljs-string">'q_id'</span>: <span class="hljs-string">'nyxfp'</span>,
<span class="hljs-string">'selftext'</span>: <span class="hljs-string">'_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?'</span>,
<span class="hljs-string">'selftext_urls'</span>: {<span class="hljs-string">'url'</span>: [<span class="hljs-string">'http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'</span>]},
<span class="hljs-string">'subreddit'</span>: <span class="hljs-string">'askscience'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'Few questions about this space walk photograph.'</span>,
<span class="hljs-string">'title_urls'</span>: {<span class="hljs-string">'url'</span>: []}}<!-- HTML_TAG_END --></pre></div>
<p>Notice <code>text</code> is a subfield nested inside the <code>answers</code> dictionary. When you preprocess the dataset, you will need to extract the <code>text</code> subfield into a separate column.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/ma1TrR7gE7I" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>For causal language modeling, load the DistilGPT2 tokenizer to process the <code>text</code> subfield:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)<!-- HTML_TAG_END --></pre></div>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/8PmhEIXhBvI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>For masked language modeling, load the DistilRoBERTa tokenizer instead:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilroberta-base"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Extract the <code>text</code> subfield from its nested structure with the <a href="https://huggingface.co/docs/datasets/process.html#flatten" rel="nofollow"><code>flatten</code></a> method:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>eli5 = eli5.flatten()
<span class="hljs-meta">>>> </span>eli5[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'answers.a_id'</span>: [<span class="hljs-string">'c3d1aib'</span>, <span class="hljs-string">'c3d4lya'</span>],
<span class="hljs-string">'answers.score'</span>: [<span class="hljs-number">6</span>, <span class="hljs-number">3</span>],
<span class="hljs-string">'answers.text'</span>: [<span class="hljs-string">"The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up."</span>,
<span class="hljs-string">"Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"</span>],
<span class="hljs-string">'answers_urls.url'</span>: [],
<span class="hljs-string">'document'</span>: <span class="hljs-string">''</span>,
<span class="hljs-string">'q_id'</span>: <span class="hljs-string">'nyxfp'</span>,
<span class="hljs-string">'selftext'</span>: <span class="hljs-string">'_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?'</span>,
<span class="hljs-string">'selftext_urls.url'</span>: [<span class="hljs-string">'http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'</span>],
<span class="hljs-string">'subreddit'</span>: <span class="hljs-string">'askscience'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'Few questions about this space walk photograph.'</span>,
<span class="hljs-string">'title_urls.url'</span>: []}<!-- HTML_TAG_END --></pre></div>
<p>Each subfield is now a separate column as indicated by the <code>answers</code> prefix. Notice that <code>answers.text</code> is a list. Instead of tokenizing each sentence separately, convert the list to a string to jointly tokenize them.</p>
<p>Here is how you can create a preprocessing function to convert the list to a string and truncate sequences to be no longer than DistilGPT2’s maximum input length:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenizer([<span class="hljs-string">" "</span>.join(x) <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"answers.text"</span>]], truncation=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once and increasing the number of processes with <code>num_proc</code>. Remove the columns you don’t need:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_eli5 = eli5.<span class="hljs-built_in">map</span>(
<span class="hljs-meta">... </span> preprocess_function,
<span class="hljs-meta">... </span> batched=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> num_proc=<span class="hljs-number">4</span>,
<span class="hljs-meta">... </span> remove_columns=eli5[<span class="hljs-string">"train"</span>].column_names,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Now you need a second preprocessing function to capture text truncated from any lengthy examples to prevent loss of information. This preprocessing function should:</p>
<ul><li>Concatenate all the text.</li>
<li>Split the concatenated text into smaller chunks defined by <code>block_size</code>.</li></ul>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>block_size = <span class="hljs-number">128</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">group_texts</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> concatenated_examples = {k: <span class="hljs-built_in">sum</span>(examples[k], []) <span class="hljs-keyword">for</span> k <span class="hljs-keyword">in</span> examples.keys()}
<span class="hljs-meta">... </span> total_length = <span class="hljs-built_in">len</span>(concatenated_examples[<span class="hljs-built_in">list</span>(examples.keys())[<span class="hljs-number">0</span>]])
<span class="hljs-meta">... </span> result = {
<span class="hljs-meta">... </span> k: [t[i : i + block_size] <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, total_length, block_size)]
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> k, t <span class="hljs-keyword">in</span> concatenated_examples.items()
<span class="hljs-meta">... </span> }
<span class="hljs-meta">... </span> result[<span class="hljs-string">"labels"</span>] = result[<span class="hljs-string">"input_ids"</span>].copy()
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> result<!-- HTML_TAG_END --></pre></div>
<p>Apply the <code>group_texts</code> function over the entire dataset:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>lm_dataset = tokenized_eli5.<span class="hljs-built_in">map</span>(group_texts, batched=<span class="hljs-literal">True</span>, num_proc=<span class="hljs-number">4</span>)<!-- HTML_TAG_END --></pre></div>
<p>For causal language modeling, use <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorForLanguageModeling">DataCollatorForLanguageModeling</a> to create a batch of examples. It will also <em>dynamically pad</em> your text to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the <code>tokenizer</code> function by setting <code>padding=True</code>, dynamic padding is more efficient. </p>
<p>You can use the end of sequence token as the padding token, and set <code>mlm=False</code>. This will use the inputs as labels shifted to the right by one element:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="Copy code excerpt to clipboard" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
</button><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-r text-gray-500 filter grayscale"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForLanguageModeling
<span class="hljs-meta">>>> </span>tokenizer.pad_token = tokenizer.eos_token
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=<span class="hljs-literal">False</span>)<!-- HTML_TAG_END --></pre></div>
<p>For masked language modeling, use the same <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorForLanguageModeling">DataCollatorForLanguageModeling</a> except you should specify <code>mlm_probability</code> to randomly mask tokens each time you iterate over the data.</p>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
</button><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-r text-gray-500 filter grayscale"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForLanguageModeling
<span class="hljs-meta">>>> </span>tokenizer.pad_token = tokenizer.eos_token
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=<span class="hljs-number">0.15</span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="causal-language-modeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#causal-language-modeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Causal language modeling
</span></h2>
<p>Causal language modeling is frequently used for text generation. This section shows you how to fine-tune <a href="https://huggingface.co/distilgpt2" rel="nofollow">DistilGPT2</a> to generate new text.</p>
<h3 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h3>
<p>Load DistilGPT2 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForCausalLM">AutoModelForCausalLM</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, datasets, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=lm_dataset[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=lm_dataset[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h3>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs and labels in <code>columns</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = lm_dataset[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = lm_dataset[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate, and some training hyperparameters:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)<!-- HTML_TAG_END --></pre></div>
<p>Load DistilGPT2 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForCausalLM">TFAutoModelForCausalLM</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForCausalLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="masked-language-modeling" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#masked-language-modeling"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Masked language modeling
</span></h2>
<p>Masked language modeling is also known as a fill-mask task because it predicts a masked token in a sequence. Models for masked language modeling require a good contextual understanding of an entire sequence instead of only the left context. This section shows you how to fine-tune <a href="https://huggingface.co/distilroberta-base" rel="nofollow">DistilRoBERTa</a> to predict a masked word.</p>
<h3 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h3>
<p>Load DistilRoBERTa with <code>AutoModelForMaskedlM</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM
<span class="hljs-meta">>>> </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">"distilroberta-base"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, datasets, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=lm_dataset[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=lm_dataset[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h3>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs and labels in <code>columns</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = lm_dataset[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = lm_dataset[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate, and some training hyperparameters:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)<!-- HTML_TAG_END --></pre></div>
<p>Load DistilRoBERTa with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForMaskedLM">TFAutoModelForMaskedLM</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMaskedLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilroberta-base"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for causal language modeling, take a look at the corresponding
<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling.ipynb" rel="nofollow">PyTorch notebook</a>
or <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/language_modeling-tf.ipynb" rel="nofollow">TensorFlow notebook</a>.</p></div>
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<h1 class="relative group"><a id="text-classification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#text-classification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Text classification
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/leNG9fN9FQU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Text classification is a common NLP task that assigns a label or class to text. There are many practical applications of text classification widely used in production by some of today’s largest companies. One of the most popular forms of text classification is sentiment analysis, which assigns a label like positive, negative, or neutral to a sequence of text. </p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">DistilBERT</a> on the <a href="https://huggingface.co/datasets/imdb" rel="nofollow">IMDb</a> dataset to determine whether a movie review is positive or negative.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the text classification <a href="https://huggingface.co/tasks/text-classification" rel="nofollow">task page</a> for more information about other forms of text classification and their associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-imdb-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-imdb-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load IMDb dataset
</span></h2>
<p>Load the IMDb dataset from the 🤗 Datasets library:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>imdb = load_dataset(<span class="hljs-string">"imdb"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>imdb[<span class="hljs-string">"test"</span>][<span class="hljs-number">0</span>]
{
<span class="hljs-string">"label"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"text"</span>: <span class="hljs-string">"I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \"Gene Roddenberry's Earth...\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again."</span>,
}<!-- HTML_TAG_END --></pre></div>
<p>There are two fields in this dataset: </p>
<ul><li><code>text</code>: a string containing the text of the movie review.</li>
<li><code>label</code>: a value that can either be <code>0</code> for a negative review or <code>1</code> for a positive review.</li></ul>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<p>Load the DistilBERT tokenizer to process the <code>text</code> field:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Create a preprocessing function to tokenize <code>text</code> and truncate sequences to be no longer than DistilBERT’s maximum input length:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenizer(examples[<span class="hljs-string">"text"</span>], truncation=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->tokenized_imdb = imdb.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorWithPadding">DataCollatorWithPadding</a> to create a batch of examples. It will also <em>dynamically pad</em> your text to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the <code>tokenizer</code> function by setting <code>padding=True</code>, dynamic padding is more efficient.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="Copy code excerpt to clipboard" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
</button><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-r text-gray-500 filter grayscale"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding
<span class="hljs-meta">>>> </span>data_collator = DataCollatorWithPadding(tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load DistilBERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForSequenceClassification">AutoModelForSequenceClassification</a> along with the number of expected labels:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, dataset, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">5</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_imdb[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_imdb[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p><a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> will apply dynamic padding by default when you pass <code>tokenizer</code> to it. In this case, you don’t need to specify a data collator explicitly.</p></div>
<h2 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h2>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs and labels in <code>columns</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_dataset = tokenized_imdb[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_dataset = tokenized_imdb[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate schedule, and some training hyperparameters:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">5</span>
<span class="hljs-meta">>>> </span>batches_per_epoch = <span class="hljs-built_in">len</span>(tokenized_imdb[<span class="hljs-string">"train"</span>]) // batch_size
<span class="hljs-meta">>>> </span>total_train_steps = <span class="hljs-built_in">int</span>(batches_per_epoch * num_epochs)
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(init_lr=<span class="hljs-number">2e-5</span>, num_warmup_steps=<span class="hljs-number">0</span>, num_train_steps=total_train_steps)<!-- HTML_TAG_END --></pre></div>
<p>Load DistilBERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForSequenceClassification">TFAutoModelForSequenceClassification</a> along with the number of expected labels:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for text classification, take a look at the corresponding
<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb" rel="nofollow">PyTorch notebook</a>
or <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb" rel="nofollow">TensorFlow notebook</a>.</p></div>
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<h1 class="relative group"><a id="translation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#translation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Translation
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/1JvfrvZgi6c" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Translation converts a sequence of text from one language to another. It is one of several tasks you can formulate as a sequence-to-sequence problem, a powerful framework that extends to vision and audio tasks. </p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/t5-small" rel="nofollow">T5</a> on the English-French subset of the <a href="https://huggingface.co/datasets/opus_books" rel="nofollow">OPUS Books</a> dataset to translate English text to French.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the translation <a href="https://huggingface.co/tasks/translation" rel="nofollow">task page</a> for more information about its associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-opus-books-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-opus-books-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load OPUS Books dataset
</span></h2>
<p>Load the OPUS Books dataset from the 🤗 Datasets library:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>books = load_dataset(<span class="hljs-string">"opus_books"</span>, <span class="hljs-string">"en-fr"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Split this dataset into a train and test set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->books = books[<span class="hljs-string">"train"</span>].train_test_split(test_size=<span class="hljs-number">0.2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>books[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'id'</span>: <span class="hljs-string">'90560'</span>,
<span class="hljs-string">'translation'</span>: {<span class="hljs-string">'en'</span>: <span class="hljs-string">'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.'</span>,
<span class="hljs-string">'fr'</span>: <span class="hljs-string">'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'</span>}}<!-- HTML_TAG_END --></pre></div>
<p>The <code>translation</code> field is a dictionary containing the English and French translations of the text.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/XAR8jnZZuUs" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Load the T5 tokenizer to process the language pairs:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-small"</span>)<!-- HTML_TAG_END --></pre></div>
<p>The preprocessing function needs to:</p>
<ol><li>Prefix the input with a prompt so T5 knows this is a translation task. Some models capable of multiple NLP tasks require prompting for specific tasks.</li>
<li>Tokenize the input (English) and target (French) separately. You can’t tokenize French text with a tokenizer pretrained on an English vocabulary. A context manager will help set the tokenizer to French first before tokenizing it.</li>
<li>Truncate sequences to be no longer than the maximum length set by the <code>max_length</code> parameter.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>source_lang = <span class="hljs-string">"en"</span>
<span class="hljs-meta">>>> </span>target_lang = <span class="hljs-string">"fr"</span>
<span class="hljs-meta">>>> </span>prefix = <span class="hljs-string">"translate English to French: "</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> inputs = [prefix + example[source_lang] <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"translation"</span>]]
<span class="hljs-meta">... </span> targets = [example[target_lang] <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"translation"</span>]]
<span class="hljs-meta">... </span> model_inputs = tokenizer(inputs, max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span> labels = tokenizer(targets, max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> model_inputs[<span class="hljs-string">"labels"</span>] = labels[<span class="hljs-string">"input_ids"</span>]
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> model_inputs<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_books = books.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorForSeq2Seq">DataCollatorForSeq2Seq</a> to create a batch of examples. It will also <em>dynamically pad</em> your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the <code>tokenizer</code> function by setting <code>padding=True</code>, dynamic padding is more efficient.</p>
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<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
</button><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-r text-gray-500 filter grayscale"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load T5 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForSeq2SeqLM">AutoModelForSeq2SeqLM</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-small"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Seq2SeqTrainingArguments">Seq2SeqTrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Seq2SeqTrainer">Seq2SeqTrainer</a> along with the model, dataset, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = Seq2SeqTrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Seq2SeqTrainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_books[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_books[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h2>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs and labels in <code>columns</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = tokenized_books[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = tokenized_books[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate schedule, and some training hyperparameters:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)<!-- HTML_TAG_END --></pre></div>
<p>Load T5 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForSeq2SeqLM">TFAutoModelForSeq2SeqLM</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-small"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for translation, take a look at the corresponding
<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation.ipynb" rel="nofollow">PyTorch notebook</a>
or <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/translation-tf.ipynb" rel="nofollow">TensorFlow notebook</a>.</p></div>
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<h1 class="relative group"><a id="audio-classification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#audio-classification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Audio classification
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/KWwzcmG98Ds" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Audio classification assigns a label or class to audio data. It is similar to text classification, except an audio input is continuous and must be discretized, whereas text can be split into tokens. Some practical applications of audio classification include identifying intent, speakers, and even animal species by their sounds.</p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/facebook/wav2vec2-base" rel="nofollow">Wav2Vec2</a> on the Keyword Spotting subset of the <a href="https://huggingface.co/datasets/superb" rel="nofollow">SUPERB</a> benchmark to classify utterances.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the audio classification <a href="https://huggingface.co/tasks/audio-classification" rel="nofollow">task page</a> for more information about its associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-superb-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-superb-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load SUPERB dataset
</span></h2>
<p>Load the SUPERB dataset from the 🤗 Datasets library:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>ks = load_dataset(<span class="hljs-string">"superb"</span>, <span class="hljs-string">"ks"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>ks[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'audio'</span>: {<span class="hljs-string">'array'</span>: array([ <span class="hljs-number">0.</span> , <span class="hljs-number">0.</span> , <span class="hljs-number">0.</span> , ..., -<span class="hljs-number">0.00592041</span>, -<span class="hljs-number">0.00405884</span>, -<span class="hljs-number">0.00253296</span>], dtype=float32), <span class="hljs-string">'path'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/05734a36d88019a09725c20cc024e1c4e7982e37d7d55c0c1ca1742ea1cdd47f/_background_noise_/doing_the_dishes.wav'</span>, <span class="hljs-string">'sampling_rate'</span>: <span class="hljs-number">16000</span>}, <span class="hljs-string">'file'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/05734a36d88019a09725c20cc024e1c4e7982e37d7d55c0c1ca1742ea1cdd47f/_background_noise_/doing_the_dishes.wav'</span>, <span class="hljs-string">'label'</span>: <span class="hljs-number">10</span>}<!-- HTML_TAG_END --></pre></div>
<p>The <code>audio</code> column contains a 1-dimensional <code>array</code> of the speech signal that must be called to load and resample the audio file. The <code>label</code> column is an integer that represents the utterance class. Create a dictionary that maps a label name to an integer and vice versa. The mapping will help the model recover the label name from the label number:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>labels = ks[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">"label"</span>].names
<span class="hljs-meta">>>> </span>label2id, id2label = <span class="hljs-built_in">dict</span>(), <span class="hljs-built_in">dict</span>()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(labels):
<span class="hljs-meta">... </span> label2id[label] = <span class="hljs-built_in">str</span>(i)
<span class="hljs-meta">... </span> id2label[<span class="hljs-built_in">str</span>(i)] = label<!-- HTML_TAG_END --></pre></div>
<p>Now you can convert the label number to a label name for more information:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>id2label[<span class="hljs-built_in">str</span>(<span class="hljs-number">10</span>)]
<span class="hljs-string">'_silence_'</span><!-- HTML_TAG_END --></pre></div>
<p>Each keyword - or label - corresponds to a number; <code>10</code> indicates <code>silence</code> in the example above.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<p>Load the Wav2Vec2 feature extractor to process the audio signal:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">>>> </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">"facebook/wav2vec2-base"</span>)<!-- HTML_TAG_END --></pre></div>
<p>The preprocessing function needs to:</p>
<ol><li>Call the <code>audio</code> column to load and if necessary resample the audio file.</li>
<li>Check the sampling rate of the audio file matches the sampling rate of the audio data a model was pretrained with. You can find this information on the Wav2Vec2 <a href="(https://huggingface.co/facebook/wav2vec2-base)">model card</a>.</li>
<li>Set a maximum input length so longer inputs are batched without being truncated.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> audio_arrays = [x[<span class="hljs-string">"array"</span>] <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"audio"</span>]]
<span class="hljs-meta">... </span> inputs = feature_extractor(
<span class="hljs-meta">... </span> audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=<span class="hljs-number">16000</span>, truncation=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> inputs<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once. Remove the columns you don’t need:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>encoded_ks = ks.<span class="hljs-built_in">map</span>(preprocess_function, remove_columns=[<span class="hljs-string">"audio"</span>, <span class="hljs-string">"file"</span>], batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load Wav2Vec2 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForAudioClassification">AutoModelForAudioClassification</a>. Specify the number of labels, and pass the model the mapping between label number and label class:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForAudioClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>num_labels = <span class="hljs-built_in">len</span>(id2label)
<span class="hljs-meta">>>> </span>model = AutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"facebook/wav2vec2-base"</span>, num_labels=num_labels, label2id=label2id, id2label=id2label
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, datasets, and feature extractor.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> save_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">3e-5</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">5</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=encoded_ks[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=encoded_ks[<span class="hljs-string">"validation"</span>],
<span class="hljs-meta">... </span> tokenizer=feature_extractor,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for audio classification, take a look at the corresponding <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/audio_classification.ipynb" rel="nofollow">PyTorch notebook</a>.</p></div>
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<h1 class="relative group"><a id="image-classification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#image-classification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Image classification
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/tjAIM7BOYhw" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Image classification assigns a label or class to an image. Unlike text or audio classification, the inputs are the pixel values that represent an image. There are many uses for image classification, like detecting damage after a disaster, monitoring crop health, or helping screen medical images for signs of disease.</p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/docs/transformers/v4.16.2/en/model_doc/vit" rel="nofollow">ViT</a> on the <a href="https://huggingface.co/datasets/food101" rel="nofollow">Food-101</a> dataset to classify a food item in an image.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the image classification <a href="https://huggingface.co/tasks/audio-classification" rel="nofollow">task page</a> for more information about its associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-food101-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-food101-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load Food-101 dataset
</span></h2>
<p>Load only the first 5000 images of the Food-101 dataset from the 🤗 Datasets library since it is pretty large:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>food = load_dataset(<span class="hljs-string">"food101"</span>, split=<span class="hljs-string">"train[:5000]"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Split this dataset into a train and test set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>food = food.train_test_split(test_size=<span class="hljs-number">0.2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>food[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'image'</span>: <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at <span class="hljs-number">0x7F52AFC8AC50</span>>,
<span class="hljs-string">'label'</span>: <span class="hljs-number">79</span>}<!-- HTML_TAG_END --></pre></div>
<p>The <code>image</code> field contains a PIL image, and each <code>label</code> is an integer that represents a class. Create a dictionary that maps a label name to an integer and vice versa. The mapping will help the model recover the label name from the label number:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>labels = food[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">"label"</span>].names
<span class="hljs-meta">>>> </span>label2id, id2label = <span class="hljs-built_in">dict</span>(), <span class="hljs-built_in">dict</span>()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(labels):
<span class="hljs-meta">... </span> label2id[label] = <span class="hljs-built_in">str</span>(i)
<span class="hljs-meta">... </span> id2label[<span class="hljs-built_in">str</span>(i)] = label<!-- HTML_TAG_END --></pre></div>
<p>Now you can convert the label number to a label name for more information:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>id2label[<span class="hljs-built_in">str</span>(<span class="hljs-number">79</span>)]
<span class="hljs-string">'prime_rib'</span><!-- HTML_TAG_END --></pre></div>
<p>Each food class - or label - corresponds to a number; <code>79</code> indicates a prime rib in the example above.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<p>Load the ViT feature extractor to process the image into a tensor:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">>>> </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">"google/vit-base-patch16-224-in21k"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Apply several image transformations to the dataset to make the model more robust against overfitting. Here you’ll use torchvision’s <a href="https://pytorch.org/vision/stable/transforms.html" rel="nofollow"><code>transforms</code></a> module. Crop a random part of the image, resize it, and normalize it with the image mean and standard deviation:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torchvision.transforms <span class="hljs-keyword">import</span> RandomResizedCrop, Compose, Normalize, ToTensor
<span class="hljs-meta">>>> </span>normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
<span class="hljs-meta">>>> </span>_transforms = Compose([RandomResizedCrop(feature_extractor.size), ToTensor(), normalize])<!-- HTML_TAG_END --></pre></div>
<p>Create a preprocessing function that will apply the transforms and return the <code>pixel_values</code> - the inputs to the model - of the image:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">transforms</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> examples[<span class="hljs-string">"pixel_values"</span>] = [_transforms(img.convert(<span class="hljs-string">"RGB"</span>)) <span class="hljs-keyword">for</span> img <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"image"</span>]]
<span class="hljs-meta">... </span> <span class="hljs-keyword">del</span> examples[<span class="hljs-string">"image"</span>]
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> examples<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Dataset’s <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html?#datasets.Dataset.with_transform" rel="nofollow"><code>with_transform</code></a> method to apply the transforms over the entire dataset. The transforms are applied on-the-fly when you load an element of the dataset:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>food = food.with_transform(transforms)<!-- HTML_TAG_END --></pre></div>
<p>Use <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DefaultDataCollator">DefaultDataCollator</a> to create a batch of examples. Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing such as padding.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator
<span class="hljs-meta">>>> </span>data_collator = DefaultDataCollator()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load ViT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForImageClassification">AutoModelForImageClassification</a>. Specify the number of labels, and pass the model the mapping between label number and label class:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForImageClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"google/vit-base-patch16-224-in21k"</span>,
<span class="hljs-meta">... </span> num_labels=<span class="hljs-built_in">len</span>(labels),
<span class="hljs-meta">... </span> id2label=id2label,
<span class="hljs-meta">... </span> label2id=label2id,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>. It is important you don’t remove unused columns because this will drop the <code>image</code> column. Without the <code>image</code> column, you can’t create <code>pixel_values</code>. Set <code>remove_unused_columns=False</code> to prevent this behavior!</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, datasets, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"steps"</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">4</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> save_steps=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span> eval_steps=<span class="hljs-number">100</span>,
<span class="hljs-meta">... </span> logging_steps=<span class="hljs-number">10</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-4</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span> remove_unused_columns=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span> train_dataset=food[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=food[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=feature_extractor,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for image classification, take a look at the corresponding <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/image_classification.ipynb" rel="nofollow">PyTorch notebook</a>.</p></div>
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<h1 class="relative group"><a id="summarization" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#summarization"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Summarization
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/yHnr5Dk2zCI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Summarization creates a shorter version of a document or an article that captures all the important information. Along with translation, it is another example of a task that can be formulated as a sequence-to-sequence task. Summarization can be:</p>
<ul><li>Extractive: extract the most relevant information from a document.</li>
<li>Abstractive: generate new text that captures the most relevant information. </li></ul>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/t5-small" rel="nofollow">T5</a> on the California state bill subset of the <a href="https://huggingface.co/datasets/billsum" rel="nofollow">BillSum</a> dataset for abstractive summarization.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the summarization <a href="https://huggingface.co/tasks/summarization" rel="nofollow">task page</a> for more information about its associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-billsum-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-billsum-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load BillSum dataset
</span></h2>
<p>Load the BillSum dataset from the 🤗 Datasets library:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>billsum = load_dataset(<span class="hljs-string">"billsum"</span>, split=<span class="hljs-string">"ca_test"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Split this dataset into a train and test set:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>billsum = billsum.train_test_split(test_size=<span class="hljs-number">0.2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>billsum[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'summary'</span>: <span class="hljs-string">'Existing law authorizes state agencies to enter into contracts for the acquisition of goods or services upon approval by the Department of General Services. Existing law sets forth various requirements and prohibitions for those contracts, including, but not limited to, a prohibition on entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between spouses and domestic partners or same-sex and different-sex couples in the provision of benefits. Existing law provides that a contract entered into in violation of those requirements and prohibitions is void and authorizes the state or any person acting on behalf of the state to bring a civil action seeking a determination that a contract is in violation and therefore void. Under existing law, a willful violation of those requirements and prohibitions is a misdemeanor.\nThis bill would also prohibit a state agency from entering into contracts for the acquisition of goods or services of $100,000 or more with a contractor that discriminates between employees on the basis of gender identity in the provision of benefits, as specified. By expanding the scope of a crime, this bill would impose a state-mandated local program.\nThe California Constitution requires the state to reimburse local agencies and school districts for certain costs mandated by the state. Statutory provisions establish procedures for making that reimbursement.\nThis bill would provide that no reimbursement is required by this act for a specified reason.'</span>,
<span class="hljs-string">'text'</span>: <span class="hljs-string">'The people of the State of California do enact as follows:\n\n\nSECTION 1.\nSection 10295.35 is added to the Public Contract Code, to read:\n10295.35.\n(a) (1) Notwithstanding any other law, a state agency shall not enter into any contract for the acquisition of goods or services in the amount of one hundred thousand dollars ($100,000) or more with a contractor that, in the provision of benefits, discriminates between employees on the basis of an employee’s or dependent’s actual or perceived gender identity, including, but not limited to, the employee’s or dependent’s identification as transgender.\n(2) For purposes of this section, “contract” includes contracts with a cumulative amount of one hundred thousand dollars ($100,000) or more per contractor in each fiscal year.\n(3) For purposes of this section, an employee health plan is discriminatory if the plan is not consistent with Section 1365.5 of the Health and Safety Code and Section 10140 of the Insurance Code.\n(4) The requirements of this section shall apply only to those portions of a contractor’s operations that occur under any of the following conditions:\n(A) Within the state.\n(B) On real property outside the state if the property is owned by the state or if the state has a right to occupy the property, and if the contractor’s presence at that location is connected to a contract with the state.\n(C) Elsewhere in the United States where work related to a state contract is being performed.\n(b) Contractors shall treat as confidential, to the maximum extent allowed by law or by the requirement of the contractor’s insurance provider, any request by an employee or applicant for employment benefits or any documentation of eligibility for benefits submitted by an employee or applicant for employment.\n(c) After taking all reasonable measures to find a contractor that complies with this section, as determined by the state agency, the requirements of this section may be waived under any of the following circumstances:\n(1) There is only one prospective contractor willing to enter into a specific contract with the state agency.\n(2) The contract is necessary to respond to an emergency, as determined by the state agency, that endangers the public health, welfare, or safety, or the contract is necessary for the provision of essential services, and no entity that complies with the requirements of this section capable of responding to the emergency is immediately available.\n(3) The requirements of this section violate, or are inconsistent with, the terms or conditions of a grant, subvention, or agreement, if the agency has made a good faith attempt to change the terms or conditions of any grant, subvention, or agreement to authorize application of this section.\n(4) The contractor is providing wholesale or bulk water, power, or natural gas, the conveyance or transmission of the same, or ancillary services, as required for ensuring reliable services in accordance with good utility practice, if the purchase of the same cannot practically be accomplished through the standard competitive bidding procedures and the contractor is not providing direct retail services to end users.\n(d) (1) A contractor shall not be deemed to discriminate in the provision of benefits if the contractor, in providing the benefits, pays the actual costs incurred in obtaining the benefit.\n(2) If a contractor is unable to provide a certain benefit, despite taking reasonable measures to do so, the contractor shall not be deemed to discriminate in the provision of benefits.\n(e) (1) Every contract subject to this chapter shall contain a statement by which the contractor certifies that the contractor is in compliance with this section.\n(2) The department or other contracting agency shall enforce this section pursuant to its existing enforcement powers.\n(3) (A) If a contractor falsely certifies that it is in compliance with this section, the contract with that contractor shall be subject to Article 9 (commencing with Section 10420), unless, within a time period specified by the department or other contracting agency, the contractor provides to the department or agency proof that it has complied, or is in the process of complying, with this section.\n(B) The application of the remedies or penalties contained in Article 9 (commencing with Section 10420) to a contract subject to this chapter shall not preclude the application of any existing remedies otherwise available to the department or other contracting agency under its existing enforcement powers.\n(f) Nothing in this section is intended to regulate the contracting practices of any local jurisdiction.\n(g) This section shall be construed so as not to conflict with applicable federal laws, rules, or regulations. In the event that a court or agency of competent jurisdiction holds that federal law, rule, or regulation invalidates any clause, sentence, paragraph, or section of this code or the application thereof to any person or circumstances, it is the intent of the state that the court or agency sever that clause, sentence, paragraph, or section so that the remainder of this section shall remain in effect.\nSEC. 2.\nSection 10295.35 of the Public Contract Code shall not be construed to create any new enforcement authority or responsibility in the Department of General Services or any other contracting agency.\nSEC. 3.\nNo reimbursement is required by this act pursuant to Section 6 of Article XIII\u2009B of the California Constitution because the only costs that may be incurred by a local agency or school district will be incurred because this act creates a new crime or infraction, eliminates a crime or infraction, or changes the penalty for a crime or infraction, within the meaning of Section 17556 of the Government Code, or changes the definition of a crime within the meaning of Section 6 of Article XIII\u2009B of the California Constitution.'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'An act to add Section 10295.35 to the Public Contract Code, relating to public contracts.'</span>}<!-- HTML_TAG_END --></pre></div>
<p>The <code>text</code> field is the input and the <code>summary</code> field is the target.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<p>Load the T5 tokenizer to process <code>text</code> and <code>summary</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"t5-small"</span>)<!-- HTML_TAG_END --></pre></div>
<p>The preprocessing function needs to:</p>
<ol><li>Prefix the input with a prompt so T5 knows this is a summarization task. Some models capable of multiple NLP tasks require prompting for specific tasks.</li>
<li>Use a context manager with the <code>as_target_tokenizer()</code> function to parallelize tokenization of inputs and labels.</li>
<li>Truncate sequences to be no longer than the maximum length set by the <code>max_length</code> parameter.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>prefix = <span class="hljs-string">"summarize: "</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> inputs = [prefix + doc <span class="hljs-keyword">for</span> doc <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"text"</span>]]
<span class="hljs-meta">... </span> model_inputs = tokenizer(inputs, max_length=<span class="hljs-number">1024</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> tokenizer.as_target_tokenizer():
<span class="hljs-meta">... </span> labels = tokenizer(examples[<span class="hljs-string">"summary"</span>], max_length=<span class="hljs-number">128</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> model_inputs[<span class="hljs-string">"labels"</span>] = labels[<span class="hljs-string">"input_ids"</span>]
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> model_inputs<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_billsum = billsum.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorForSeq2Seq">DataCollatorForSeq2Seq</a> to create a batch of examples. It will also <em>dynamically pad</em> your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the <code>tokenizer</code> function by setting <code>padding=True</code>, dynamic padding is more efficient.</p>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
</button><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-r text-gray-500 filter grayscale"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForSeq2Seq
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load T5 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForSeq2SeqLM">AutoModelForSeq2SeqLM</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-small"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Seq2SeqTrainingArguments">Seq2SeqTrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Seq2SeqTrainer">Seq2SeqTrainer</a> along with the model, dataset, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = Seq2SeqTrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">1</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Seq2SeqTrainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_billsum[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_billsum[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h2>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs and labels in <code>columns</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = tokenized_billsum[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_test_set = tokenized_billsum[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate schedule, and some training hyperparameters:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer, AdamWeightDecay
<span class="hljs-meta">>>> </span>optimizer = AdamWeightDecay(learning_rate=<span class="hljs-number">2e-5</span>, weight_decay_rate=<span class="hljs-number">0.01</span>)<!-- HTML_TAG_END --></pre></div>
<p>Load T5 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForSeq2SeqLM">TFAutoModelForSeq2SeqLM</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"t5-small"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for summarization, take a look at the corresponding
<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb" rel="nofollow">PyTorch notebook</a>
or <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization-tf.ipynb" rel="nofollow">TensorFlow notebook</a>.</p></div>
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<h1 class="relative group"><a id="automatic-speech-recognition" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#automatic-speech-recognition"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Automatic speech recognition
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/TksaY_FDgnk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Automatic speech recognition (ASR) converts a speech signal to text. It is an example of a sequence-to-sequence task, going from a sequence of audio inputs to textual outputs. Voice assistants like Siri and Alexa utilize ASR models to assist users.</p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/facebook/wav2vec2-base" rel="nofollow">Wav2Vec2</a> on the <a href="https://huggingface.co/datasets/timit_asr" rel="nofollow">TIMIT</a> dataset to transcribe audio to text.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the automatic speech recognition <a href="https://huggingface.co/tasks/automatic-speech-recognition" rel="nofollow">task page</a> for more information about its associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-timit-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-timit-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load TIMIT dataset
</span></h2>
<p>Load the TIMIT dataset from the 🤗 Datasets library:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>timit = load_dataset(<span class="hljs-string">"timit_asr"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>timit
DatasetDict({
train: Dataset({
features: [<span class="hljs-string">'file'</span>, <span class="hljs-string">'audio'</span>, <span class="hljs-string">'text'</span>, <span class="hljs-string">'phonetic_detail'</span>, <span class="hljs-string">'word_detail'</span>, <span class="hljs-string">'dialect_region'</span>, <span class="hljs-string">'sentence_type'</span>, <span class="hljs-string">'speaker_id'</span>, <span class="hljs-string">'id'</span>],
num_rows: <span class="hljs-number">4620</span>
})
test: Dataset({
features: [<span class="hljs-string">'file'</span>, <span class="hljs-string">'audio'</span>, <span class="hljs-string">'text'</span>, <span class="hljs-string">'phonetic_detail'</span>, <span class="hljs-string">'word_detail'</span>, <span class="hljs-string">'dialect_region'</span>, <span class="hljs-string">'sentence_type'</span>, <span class="hljs-string">'speaker_id'</span>, <span class="hljs-string">'id'</span>],
num_rows: <span class="hljs-number">1680</span>
})
})<!-- HTML_TAG_END --></pre></div>
<p>While the dataset contains a lot of helpful information, like <code>dialect_region</code> and <code>sentence_type</code>, you will focus on the <code>audio</code> and <code>text</code> fields in this guide. Remove the other columns:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>timit = timit.remove_columns(
<span class="hljs-meta">... </span> [<span class="hljs-string">"phonetic_detail"</span>, <span class="hljs-string">"word_detail"</span>, <span class="hljs-string">"dialect_region"</span>, <span class="hljs-string">"id"</span>, <span class="hljs-string">"sentence_type"</span>, <span class="hljs-string">"speaker_id"</span>]
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Take a look at the example again:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>timit[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'audio'</span>: {<span class="hljs-string">'array'</span>: array([-<span class="hljs-number">2.1362305e-04</span>, <span class="hljs-number">6.1035156e-05</span>, <span class="hljs-number">3.0517578e-05</span>, ...,
-<span class="hljs-number">3.0517578e-05</span>, -<span class="hljs-number">9.1552734e-05</span>, -<span class="hljs-number">6.1035156e-05</span>], dtype=float32),
<span class="hljs-string">'path'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'</span>,
<span class="hljs-string">'sampling_rate'</span>: <span class="hljs-number">16000</span>},
<span class="hljs-string">'file'</span>: <span class="hljs-string">'/root/.cache/huggingface/datasets/downloads/extracted/404950a46da14eac65eb4e2a8317b1372fb3971d980d91d5d5b221275b1fd7e0/data/TRAIN/DR4/MMDM0/SI681.WAV'</span>,
<span class="hljs-string">'text'</span>: <span class="hljs-string">'Would such an act of refusal be useful?'</span>}<!-- HTML_TAG_END --></pre></div>
<p>The <code>audio</code> column contains a 1-dimensional <code>array</code> of the speech signal that must be called to load and resample the audio file.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<p>Load the Wav2Vec2 processor to process the audio signal and transcribed text:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
<span class="hljs-meta">>>> </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">"facebook/wav2vec2-base"</span>)<!-- HTML_TAG_END --></pre></div>
<p>The preprocessing function needs to:</p>
<ol><li>Call the <code>audio</code> column to load and resample the audio file.</li>
<li>Extract the <code>input_values</code> from the audio file.</li>
<li>Typically, when you call the processor, you call the feature extractor. Since you also want to tokenize text, instruct the processor to call the tokenizer instead with a context manager.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">prepare_dataset</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span> audio = batch[<span class="hljs-string">"audio"</span>]
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"input_values"</span>] = processor(audio[<span class="hljs-string">"array"</span>], sampling_rate=audio[<span class="hljs-string">"sampling_rate"</span>]).input_values[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"input_length"</span>] = <span class="hljs-built_in">len</span>(batch[<span class="hljs-string">"input_values"</span>])
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> processor.as_target_processor():
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"labels"</span>] = processor(batch[<span class="hljs-string">"text"</span>]).input_ids
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> batch<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the map function by increasing the number of processes with <code>num_proc</code>. Remove the columns you don’t need:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>timit = timit.<span class="hljs-built_in">map</span>(prepare_dataset, remove_columns=timit.column_names[<span class="hljs-string">"train"</span>], num_proc=<span class="hljs-number">4</span>)<!-- HTML_TAG_END --></pre></div>
<p>🤗 Transformers doesn’t have a data collator for automatic speech recognition, so you will need to create one. You can adapt the <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorWithPadding">DataCollatorWithPadding</a> to create a batch of examples for automatic speech recognition. It will also dynamically pad your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the <code>tokenizer</code> function by setting <code>padding=True</code>, dynamic padding is more efficient.</p>
<p>Unlike other data collators, this specific data collator needs to apply a different padding method to <code>input_values</code> and <code>labels</code>. You can apply a different padding method with a context manager:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass, field
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">Any</span>, <span class="hljs-type">Dict</span>, <span class="hljs-type">List</span>, <span class="hljs-type">Optional</span>, <span class="hljs-type">Union</span>
<span class="hljs-meta">>>> </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">DataCollatorCTCWithPadding</span>:
<span class="hljs-meta">... </span> processor: AutoProcessor
<span class="hljs-meta">... </span> padding: <span class="hljs-type">Union</span>[<span class="hljs-built_in">bool</span>, <span class="hljs-built_in">str</span>] = <span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, features: <span class="hljs-type">List</span>[<span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, <span class="hljs-type">Union</span>[<span class="hljs-type">List</span>[<span class="hljs-built_in">int</span>], torch.Tensor]]]</span>) -> <span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, torch.Tensor]:
<span class="hljs-meta">... </span> <span class="hljs-comment"># split inputs and labels since they have to be of different lengths and need</span>
<span class="hljs-meta">... </span> <span class="hljs-comment"># different padding methods</span>
<span class="hljs-meta">... </span> input_features = [{<span class="hljs-string">"input_values"</span>: feature[<span class="hljs-string">"input_values"</span>]} <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span> label_features = [{<span class="hljs-string">"input_ids"</span>: feature[<span class="hljs-string">"labels"</span>]} <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span> batch = self.processor.pad(
<span class="hljs-meta">... </span> input_features,
<span class="hljs-meta">... </span> padding=self.padding,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> self.processor.as_target_processor():
<span class="hljs-meta">... </span> labels_batch = self.processor.pad(
<span class="hljs-meta">... </span> label_features,
<span class="hljs-meta">... </span> padding=self.padding,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> <span class="hljs-comment"># replace padding with -100 to ignore loss correctly</span>
<span class="hljs-meta">... </span> labels = labels_batch[<span class="hljs-string">"input_ids"</span>].masked_fill(labels_batch.attention_mask.ne(<span class="hljs-number">1</span>), -<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"labels"</span>] = labels
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> batch<!-- HTML_TAG_END --></pre></div>
<p>Create a batch of examples and dynamically pad them with <code>DataCollatorForCTCWithPadding</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>data_collator = DataCollatorCTCWithPadding(processor=processor, padding=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load Wav2Vec2 with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForCTC">AutoModelForCTC</a>. For <code>ctc_loss_reduction</code>, it is often better to use the average instead of the default summation:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCTC, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForCTC.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"facebook/wav2vec-base"</span>,
<span class="hljs-meta">... </span> ctc_loss_reduction=<span class="hljs-string">"mean"</span>,
<span class="hljs-meta">... </span> pad_token_id=processor.tokenizer.pad_token_id,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, datasets, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> group_by_length=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"steps"</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> gradient_checkpointing=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">1e-4</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.005</span>,
<span class="hljs-meta">... </span> save_total_limit=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=timit[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=timit[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=processor.feature_extractor,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for automatic speech recognition, take a look at this blog <a href="https://huggingface.co/blog/fine-tune-wav2vec2-english" rel="nofollow">post</a> for English ASR and this <a href="https://huggingface.co/blog/fine-tune-xlsr-wav2vec2" rel="nofollow">post</a> for multilingual ASR.</p></div>
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<h1 class="relative group"><a id="token-classification" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#token-classification"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Token classification
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/wVHdVlPScxA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization. </p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">DistilBERT</a> on the <a href="https://huggingface.co/datasets/wnut_17" rel="nofollow">WNUT 17</a> dataset to detect new entities.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the token classification <a href="https://huggingface.co/tasks/token-classification" rel="nofollow">task page</a> for more information about other forms of token classification and their associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-wnut-17-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-wnut-17-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load WNUT 17 dataset
</span></h2>
<p>Load the WNUT 17 dataset from the 🤗 Datasets library:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>wnut = load_dataset(<span class="hljs-string">"wnut_17"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>wnut[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'id'</span>: <span class="hljs-string">'0'</span>,
<span class="hljs-string">'ner_tags'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'tokens'</span>: [<span class="hljs-string">'@paulwalk'</span>, <span class="hljs-string">'It'</span>, <span class="hljs-string">"'s"</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'I'</span>, <span class="hljs-string">"'m"</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Empire'</span>, <span class="hljs-string">'State'</span>, <span class="hljs-string">'Building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'ESB'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>]
}<!-- HTML_TAG_END --></pre></div>
<p>Each number in <code>ner_tags</code> represents an entity. Convert the number to a label name for more information:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>label_list = wnut[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">f"ner_tags"</span>].feature.names
<span class="hljs-meta">>>> </span>label_list
[
<span class="hljs-string">"O"</span>,
<span class="hljs-string">"B-corporation"</span>,
<span class="hljs-string">"I-corporation"</span>,
<span class="hljs-string">"B-creative-work"</span>,
<span class="hljs-string">"I-creative-work"</span>,
<span class="hljs-string">"B-group"</span>,
<span class="hljs-string">"I-group"</span>,
<span class="hljs-string">"B-location"</span>,
<span class="hljs-string">"I-location"</span>,
<span class="hljs-string">"B-person"</span>,
<span class="hljs-string">"I-person"</span>,
<span class="hljs-string">"B-product"</span>,
<span class="hljs-string">"I-product"</span>,
]<!-- HTML_TAG_END --></pre></div>
<p>The <code>ner_tag</code> describes an entity, such as a corporation, location, or person. The letter that prefixes each <code>ner_tag</code> indicates the token position of the entity:</p>
<ul><li><code>B-</code> indicates the beginning of an entity.</li>
<li><code>I-</code> indicates a token is contained inside the same entity (e.g., the <code>State</code> token is a part of an entity like
<code>Empire State Building</code>).</li>
<li><code>0</code> indicates the token doesn’t correspond to any entity.</li></ul>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/iY2AZYdZAr0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Load the DistilBERT tokenizer to process the <code>tokens</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Since the input has already been split into words, set <code>is_split_into_words=True</code> to tokenize the words into subwords:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_input = tokenizer(example[<span class="hljs-string">"tokens"</span>], is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokens = tokenizer.convert_ids_to_tokens(tokenized_input[<span class="hljs-string">"input_ids"</span>])
<span class="hljs-meta">>>> </span>tokens
[<span class="hljs-string">'[CLS]'</span>, <span class="hljs-string">'@'</span>, <span class="hljs-string">'paul'</span>, <span class="hljs-string">'##walk'</span>, <span class="hljs-string">'it'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'s'</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'i'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'m'</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'empire'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'es'</span>, <span class="hljs-string">'##b'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'[SEP]'</span>]<!-- HTML_TAG_END --></pre></div>
<p>Adding the special tokens <code>[CLS]</code> and <code>[SEP]</code> and subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may be split into two subwords. You will need to realign the tokens and labels by:</p>
<ol><li>Mapping all tokens to their corresponding word with the <a href="https://huggingface.co/docs/tokenizers/python/latest/api/reference.html#tokenizers.Encoding.word_ids" rel="nofollow"><code>word_ids</code></a> method.</li>
<li>Assigning the label <code>-100</code> to the special tokens <code>[CLS]</code> and <code>[SEP]</code> so the PyTorch loss function ignores
them.</li>
<li>Only labeling the first token of a given word. Assign <code>-100</code> to other subtokens from the same word.</li></ol>
<p>Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT’s maximum input length::</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_and_align_labels</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> tokenized_inputs = tokenizer(examples[<span class="hljs-string">"tokens"</span>], truncation=<span class="hljs-literal">True</span>, is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> labels = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(examples[<span class="hljs-string">f"ner_tags"</span>]):
<span class="hljs-meta">... </span> word_ids = tokenized_inputs.word_ids(batch_index=i) <span class="hljs-comment"># Map tokens to their respective word.</span>
<span class="hljs-meta">... </span> previous_word_idx = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> label_ids = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> word_idx <span class="hljs-keyword">in</span> word_ids: <span class="hljs-comment"># Set the special tokens to -100.</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> word_idx <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">elif</span> word_idx != previous_word_idx: <span class="hljs-comment"># Only label the first token of a given word.</span>
<span class="hljs-meta">... </span> label_ids.append(label[word_idx])
<span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> previous_word_idx = word_idx
<span class="hljs-meta">... </span> labels.append(label_ids)
<span class="hljs-meta">... </span> tokenized_inputs[<span class="hljs-string">"labels"</span>] = labels
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenized_inputs<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to tokenize and align the labels over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_wnut = wnut.<span class="hljs-built_in">map</span>(tokenize_and_align_labels, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorForTokenClassification">DataCollatorForTokenClassification</a> to create a batch of examples. It will also <em>dynamically pad</em> your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the <code>tokenizer</code> function by setting <code>padding=True</code>, dynamic padding is more efficient.</p>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
</button><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-r text-gray-500 filter grayscale"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForTokenClassification
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load DistilBERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForTokenClassification">AutoModelForTokenClassification</a> along with the number of expected labels:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">14</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, dataset, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_wnut[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_wnut[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h2>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs and labels in <code>columns</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = tokenized_wnut[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_wnut[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate schedule, and some training hyperparameters:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_train_epochs = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span>num_train_steps = (<span class="hljs-built_in">len</span>(tokenized_wnut[<span class="hljs-string">"train"</span>]) // batch_size) * num_train_epochs
<span class="hljs-meta">>>> </span>optimizer, lr_schedule = create_optimizer(
<span class="hljs-meta">... </span> init_lr=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_train_steps=num_train_steps,
<span class="hljs-meta">... </span> weight_decay_rate=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> num_warmup_steps=<span class="hljs-number">0</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Load DistilBERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForTokenClassification">TFAutoModelForTokenClassification</a> along with the number of expected labels:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb" rel="nofollow">PyTorch notebook</a>
or <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb" rel="nofollow">TensorFlow notebook</a>.</p></div>
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<h1 class="relative group"><a id="multiple-choice" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#multiple-choice"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Multiple choice
</span></h1>
<p>A multiple choice task is similar to question answering, except several candidate answers are provided along with a context. The model is trained to select the correct answer from multiple inputs given a context.</p>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/bert-base-uncased" rel="nofollow">BERT</a> on the <code>regular</code> configuration of the <a href="https://huggingface.co/datasets/swag" rel="nofollow">SWAG</a> dataset to select the best answer given multiple options and some context.</p>
<h2 class="relative group"><a id="load-swag-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-swag-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load SWAG dataset
</span></h2>
<p>Load the SWAG dataset from the 🤗 Datasets library:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>swag = load_dataset(<span class="hljs-string">"swag"</span>, <span class="hljs-string">"regular"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>swag[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'ending0'</span>: <span class="hljs-string">'passes by walking down the street playing their instruments.'</span>,
<span class="hljs-string">'ending1'</span>: <span class="hljs-string">'has heard approaching them.'</span>,
<span class="hljs-string">'ending2'</span>: <span class="hljs-string">"arrives and they're outside dancing and asleep."</span>,
<span class="hljs-string">'ending3'</span>: <span class="hljs-string">'turns the lead singer watches the performance.'</span>,
<span class="hljs-string">'fold-ind'</span>: <span class="hljs-string">'3416'</span>,
<span class="hljs-string">'gold-source'</span>: <span class="hljs-string">'gold'</span>,
<span class="hljs-string">'label'</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">'sent1'</span>: <span class="hljs-string">'Members of the procession walk down the street holding small horn brass instruments.'</span>,
<span class="hljs-string">'sent2'</span>: <span class="hljs-string">'A drum line'</span>,
<span class="hljs-string">'startphrase'</span>: <span class="hljs-string">'Members of the procession walk down the street holding small horn brass instruments. A drum line'</span>,
<span class="hljs-string">'video-id'</span>: <span class="hljs-string">'anetv_jkn6uvmqwh4'</span>}<!-- HTML_TAG_END --></pre></div>
<p>The <code>sent1</code> and <code>sent2</code> fields show how a sentence begins, and each <code>ending</code> field shows how a sentence could end. Given the sentence beginning, the model must pick the correct sentence ending as indicated by the <code>label</code> field.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<p>Load the BERT tokenizer to process the start of each sentence and the four possible endings:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>The preprocessing function needs to do:</p>
<ol><li>Make four copies of the <code>sent1</code> field so you can combine each of them with <code>sent2</code> to recreate how a sentence starts.</li>
<li>Combine <code>sent2</code> with each of the four possible sentence endings.</li>
<li>Flatten these two lists so you can tokenize them, and then unflatten them afterward so each example has a corresponding <code>input_ids</code>, <code>attention_mask</code>, and <code>labels</code> field.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>ending_names = [<span class="hljs-string">"ending0"</span>, <span class="hljs-string">"ending1"</span>, <span class="hljs-string">"ending2"</span>, <span class="hljs-string">"ending3"</span>]
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> first_sentences = [[context] * <span class="hljs-number">4</span> <span class="hljs-keyword">for</span> context <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"sent1"</span>]]
<span class="hljs-meta">... </span> question_headers = examples[<span class="hljs-string">"sent2"</span>]
<span class="hljs-meta">... </span> second_sentences = [
<span class="hljs-meta">... </span> [<span class="hljs-string">f"<span class="hljs-subst">{header}</span> <span class="hljs-subst">{examples[end][i]}</span>"</span> <span class="hljs-keyword">for</span> end <span class="hljs-keyword">in</span> ending_names] <span class="hljs-keyword">for</span> i, header <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(question_headers)
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span> first_sentences = <span class="hljs-built_in">sum</span>(first_sentences, [])
<span class="hljs-meta">... </span> second_sentences = <span class="hljs-built_in">sum</span>(second_sentences, [])
<span class="hljs-meta">... </span> tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> {k: [v[i : i + <span class="hljs-number">4</span>] <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(v), <span class="hljs-number">4</span>)] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> tokenized_examples.items()}<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once:</p>
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<pre><!-- HTML_TAG_START -->tokenized_swag = swag.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>🤗 Transformers doesn’t have a data collator for multiple choice, so you will need to create one. You can adapt the <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DataCollatorWithPadding">DataCollatorWithPadding</a> to create a batch of examples for multiple choice. It will also <em>dynamically pad</em> your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the <code>tokenizer</code> function by setting <code>padding=True</code>, dynamic padding is more efficient.</p>
<p><code>DataCollatorForMultipleChoice</code> will flatten all the model inputs, apply padding, and then unflatten the results:</p>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
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<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.tokenization_utils_base <span class="hljs-keyword">import</span> PreTrainedTokenizerBase, PaddingStrategy
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">Optional</span>, <span class="hljs-type">Union</span>
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">DataCollatorForMultipleChoice</span>:
<span class="hljs-meta">... </span> <span class="hljs-string">"""
<span class="hljs-meta">... </span> Data collator that will dynamically pad the inputs for multiple choice received.
<span class="hljs-meta">... </span> """</span>
<span class="hljs-meta">... </span> tokenizer: PreTrainedTokenizerBase
<span class="hljs-meta">... </span> padding: <span class="hljs-type">Union</span>[<span class="hljs-built_in">bool</span>, <span class="hljs-built_in">str</span>, PaddingStrategy] = <span class="hljs-literal">True</span>
<span class="hljs-meta">... </span> max_length: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">int</span>] = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> pad_to_multiple_of: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">int</span>] = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, features</span>):
<span class="hljs-meta">... </span> label_name = <span class="hljs-string">"label"</span> <span class="hljs-keyword">if</span> <span class="hljs-string">"label"</span> <span class="hljs-keyword">in</span> features[<span class="hljs-number">0</span>].keys() <span class="hljs-keyword">else</span> <span class="hljs-string">"labels"</span>
<span class="hljs-meta">... </span> labels = [feature.pop(label_name) <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features]
<span class="hljs-meta">... </span> batch_size = <span class="hljs-built_in">len</span>(features)
<span class="hljs-meta">... </span> num_choices = <span class="hljs-built_in">len</span>(features[<span class="hljs-number">0</span>][<span class="hljs-string">"input_ids"</span>])
<span class="hljs-meta">... </span> flattened_features = [
<span class="hljs-meta">... </span> [{k: v[i] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> feature.items()} <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_choices)] <span class="hljs-keyword">for</span> feature <span class="hljs-keyword">in</span> features
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span> flattened_features = <span class="hljs-built_in">sum</span>(flattened_features, [])
<span class="hljs-meta">... </span> batch = self.tokenizer.pad(
<span class="hljs-meta">... </span> flattened_features,
<span class="hljs-meta">... </span> padding=self.padding,
<span class="hljs-meta">... </span> max_length=self.max_length,
<span class="hljs-meta">... </span> pad_to_multiple_of=self.pad_to_multiple_of,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> batch = {k: v.view(batch_size, num_choices, -<span class="hljs-number">1</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
<span class="hljs-meta">... </span> batch[<span class="hljs-string">"labels"</span>] = torch.tensor(labels, dtype=torch.int64)
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> batch<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load BERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForMultipleChoice">AutoModelForMultipleChoice</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMultipleChoice, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Trainer, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, dataset, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">5e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_swag[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_swag[<span class="hljs-string">"validation"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h2>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs in <code>columns</code>, targets in <code>label_cols</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
<span class="hljs-meta">>>> </span>tf_train_set = tokenized_swag[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=batch_size,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_swag[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=batch_size,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate schedule, and some training hyperparameters:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_train_epochs = <span class="hljs-number">2</span>
<span class="hljs-meta">>>> </span>total_train_steps = (<span class="hljs-built_in">len</span>(tokenized_swag[<span class="hljs-string">"train"</span>]) // batch_size) * num_train_epochs
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(init_lr=<span class="hljs-number">5e-5</span>, num_warmup_steps=<span class="hljs-number">0</span>, num_train_steps=total_train_steps)<!-- HTML_TAG_END --></pre></div>
<p>Load BERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForMultipleChoice">TFAutoModelForMultipleChoice</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMultipleChoice
<span class="hljs-meta">>>> </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(
<span class="hljs-meta">... </span> optimizer=optimizer,
<span class="hljs-meta">... </span> loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=<span class="hljs-literal">True</span>),
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
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<h1 class="relative group"><a id="question-answering" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#question-answering"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Question answering
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/ajPx5LwJD-I" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Question answering tasks return an answer given a question. There are two common forms of question answering:</p>
<ul><li>Extractive: extract the answer from the given context.</li>
<li>Abstractive: generate an answer from the context that correctly answers the question.</li></ul>
<p>This guide will show you how to fine-tune <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">DistilBERT</a> on the <a href="https://huggingface.co/datasets/squad" rel="nofollow">SQuAD</a> dataset for extractive question answering.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>See the question answering <a href="https://huggingface.co/tasks/question-answering" rel="nofollow">task page</a> for more information about other forms of question answering and their associated models, datasets, and metrics.</p></div>
<h2 class="relative group"><a id="load-squad-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#load-squad-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Load SQuAD dataset
</span></h2>
<p>Load the SQuAD dataset from the 🤗 Datasets library:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>squad = load_dataset(<span class="hljs-string">"squad"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Then take a look at an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>squad[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'answers'</span>: {<span class="hljs-string">'answer_start'</span>: [<span class="hljs-number">515</span>], <span class="hljs-string">'text'</span>: [<span class="hljs-string">'Saint Bernadette Soubirous'</span>]},
<span class="hljs-string">'context'</span>: <span class="hljs-string">'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'</span>,
<span class="hljs-string">'id'</span>: <span class="hljs-string">'5733be284776f41900661182'</span>,
<span class="hljs-string">'question'</span>: <span class="hljs-string">'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'</span>,
<span class="hljs-string">'title'</span>: <span class="hljs-string">'University_of_Notre_Dame'</span>
}<!-- HTML_TAG_END --></pre></div>
<p>The <code>answers</code> field is a dictionary containing the starting position of the answer and the <code>text</code> of the answer.</p>
<h2 class="relative group"><a id="preprocess" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocess"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preprocess
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/qgaM0weJHpA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Load the DistilBERT tokenizer to process the <code>question</code> and <code>context</code> fields:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>There are a few preprocessing steps particular to question answering that you should be aware of:</p>
<ol><li>Some examples in a dataset may have a very long <code>context</code> that exceeds the maximum input length of the model. Truncate only the <code>context</code> by setting <code>truncation="only_second"</code>.</li>
<li>Next, map the start and end positions of the answer to the original <code>context</code> by setting
<code>return_offset_mapping=True</code>.</li>
<li>With the mapping in hand, you can find the start and end tokens of the answer. Use the <a href="https://huggingface.co/docs/tokenizers/python/latest/api/reference.html#tokenizers.Encoding.sequence_ids" rel="nofollow"><code>sequence_ids</code></a> method to
find which part of the offset corresponds to the <code>question</code> and which corresponds to the <code>context</code>.</li></ol>
<p>Here is how you can create a function to truncate and map the start and end tokens of the answer to the <code>context</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> questions = [q.strip() <span class="hljs-keyword">for</span> q <span class="hljs-keyword">in</span> examples[<span class="hljs-string">"question"</span>]]
<span class="hljs-meta">... </span> inputs = tokenizer(
<span class="hljs-meta">... </span> questions,
<span class="hljs-meta">... </span> examples[<span class="hljs-string">"context"</span>],
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">384</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-string">"only_second"</span>,
<span class="hljs-meta">... </span> return_offsets_mapping=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> padding=<span class="hljs-string">"max_length"</span>,
<span class="hljs-meta">... </span> )
<span class="hljs-meta">... </span> offset_mapping = inputs.pop(<span class="hljs-string">"offset_mapping"</span>)
<span class="hljs-meta">... </span> answers = examples[<span class="hljs-string">"answers"</span>]
<span class="hljs-meta">... </span> start_positions = []
<span class="hljs-meta">... </span> end_positions = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> i, offset <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(offset_mapping):
<span class="hljs-meta">... </span> answer = answers[i]
<span class="hljs-meta">... </span> start_char = answer[<span class="hljs-string">"answer_start"</span>][<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span> end_char = answer[<span class="hljs-string">"answer_start"</span>][<span class="hljs-number">0</span>] + <span class="hljs-built_in">len</span>(answer[<span class="hljs-string">"text"</span>][<span class="hljs-number">0</span>])
<span class="hljs-meta">... </span> sequence_ids = inputs.sequence_ids(i)
<span class="hljs-meta">... </span> <span class="hljs-comment"># Find the start and end of the context</span>
<span class="hljs-meta">... </span> idx = <span class="hljs-number">0</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> sequence_ids[idx] != <span class="hljs-number">1</span>:
<span class="hljs-meta">... </span> idx += <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> context_start = idx
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> sequence_ids[idx] == <span class="hljs-number">1</span>:
<span class="hljs-meta">... </span> idx += <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> context_end = idx - <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> <span class="hljs-comment"># If the answer is not fully inside the context, label it (0, 0)</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> offset[context_start][<span class="hljs-number">0</span>] > end_char <span class="hljs-keyword">or</span> offset[context_end][<span class="hljs-number">1</span>] < start_char:
<span class="hljs-meta">... </span> start_positions.append(<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span> end_positions.append(<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span> <span class="hljs-comment"># Otherwise it's the start and end token positions</span>
<span class="hljs-meta">... </span> idx = context_start
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> idx <= context_end <span class="hljs-keyword">and</span> offset[idx][<span class="hljs-number">0</span>] <= start_char:
<span class="hljs-meta">... </span> idx += <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> start_positions.append(idx - <span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> idx = context_end
<span class="hljs-meta">... </span> <span class="hljs-keyword">while</span> idx >= context_start <span class="hljs-keyword">and</span> offset[idx][<span class="hljs-number">1</span>] >= end_char:
<span class="hljs-meta">... </span> idx -= <span class="hljs-number">1</span>
<span class="hljs-meta">... </span> end_positions.append(idx + <span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> inputs[<span class="hljs-string">"start_positions"</span>] = start_positions
<span class="hljs-meta">... </span> inputs[<span class="hljs-string">"end_positions"</span>] = end_positions
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> inputs<!-- HTML_TAG_END --></pre></div>
<p>Use 🤗 Datasets <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> function to apply the preprocessing function over the entire dataset. You can speed up the <code>map</code> function by setting <code>batched=True</code> to process multiple elements of the dataset at once. Remove the columns you don’t need:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_squad = squad.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>, remove_columns=squad[<span class="hljs-string">"train"</span>].column_names)<!-- HTML_TAG_END --></pre></div>
<p>Use <a href="/docs/transformers/pr_16143/en/main_classes/data_collator#transformers.DefaultDataCollator">DefaultDataCollator</a> to create a batch of examples. Unlike other data collators in 🤗 Transformers, the <code>DefaultDataCollator</code> does not apply additional preprocessing such as padding.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="Copy code excerpt to clipboard" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><div><div class="bg-white leading-none border border-gray-100 rounded-lg inline-flex p-0.5 text-sm mb-4 select-none"><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-l false"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<p class="!m-0 ">Pytorch</p>
</button><button class="flex justify-center py-1.5 px-2.5 focus:outline-none rounded-r text-gray-500 filter grayscale"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<p class="!m-0 ">TensorFlow</p>
</button></div></div><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator
<span class="hljs-meta">>>> </span>data_collator = DefaultDataCollator()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with Trainer
</span></h2>
<p>Load DistilBERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.AutoModelForQuestionAnswering">AutoModelForQuestionAnswering</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForQuestionAnswering, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with the <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>, take a look at the basic tutorial <a href="training#finetune-with-trainer">here</a>!</p></div>
<p>At this point, only three steps remain:</p>
<ol><li>Define your training hyperparameters in <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.TrainingArguments">TrainingArguments</a>.</li>
<li>Pass the training arguments to <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a> along with the model, dataset, tokenizer, and data collator.</li>
<li>Call <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer.train">train()</a> to fine-tune your model.</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_squad[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_squad[<span class="hljs-string">"validation"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="finetune-with-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-with-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune with TensorFlow
</span></h2>
<p>To fine-tune a model in TensorFlow is just as easy, with only a few differences.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If you aren’t familiar with fine-tuning a model with Keras, take a look at the basic tutorial <a href="training#finetune-with-keras">here</a>!</p></div>
<p>Convert your datasets to the <code>tf.data.Dataset</code> format with <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Specify inputs and the start and end positions of an answer in <code>columns</code>, whether to shuffle the dataset order, batch size, and the data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = tokenized_squad[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"start_positions"</span>, <span class="hljs-string">"end_positions"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_squad[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"start_positions"</span>, <span class="hljs-string">"end_positions"</span>],
<span class="hljs-meta">... </span> dummy_labels=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Set up an optimizer function, learning rate schedule, and some training hyperparameters:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">2</span>
<span class="hljs-meta">>>> </span>total_train_steps = (<span class="hljs-built_in">len</span>(tokenized_squad[<span class="hljs-string">"train"</span>]) // batch_size) * num_epochs
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(
<span class="hljs-meta">... </span> init_lr=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_warmup_steps=<span class="hljs-number">0</span>,
<span class="hljs-meta">... </span> num_train_steps=total_train_steps,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Load DistilBERT with <a href="/docs/transformers/pr_16143/en/model_doc/auto#transformers.TFAutoModelForQuestionAnswering">TFAutoModelForQuestionAnswering</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForQuestionAnswering
<span class="hljs-meta">>>> </span>model = TFAutoModelForQuestionAnswering(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure the model for training with <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Call <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> to fine-tune the model:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding
<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb" rel="nofollow">PyTorch notebook</a>
or <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb" rel="nofollow">TensorFlow notebook</a>.</p></div>
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<h1 class="relative group"><a id="utilities-for-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#utilities-for-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Utilities for Trainer
</span></h1>
<p>This page lists all the utility functions used by <a href="/docs/transformers/pr_16143/en/main_classes/trainer#transformers.Trainer">Trainer</a>.</p>
<p>Most of those are only useful if you are studying the code of the Trainer in the library.</p>
<h2 class="relative group"><a id="transformers.EvalPrediction" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.EvalPrediction"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Utilities
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.EvalPrediction"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">EvalPrediction</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.EvalPrediction" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.EvalPrediction"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_utils.py#L67" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">predictions<span class="opacity-60">: typing.Union[numpy.ndarray, typing.Tuple[numpy.ndarray]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">label_ids<span class="opacity-60">: typing.Union[numpy.ndarray, typing.Tuple[numpy.ndarray]]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.EvalPrediction.predictions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.EvalPrediction.predictions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>predictions</strong> (<code>np.ndarray</code>) — Predictions of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.EvalPrediction.label_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.EvalPrediction.label_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>label_ids</strong> (<code>np.ndarray</code>) — Targets to be matched.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Evaluation output (always contains labels), to be used to compute metrics.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.IntervalStrategy"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">IntervalStrategy</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.IntervalStrategy" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.IntervalStrategy"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_utils.py#L115" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">names<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">qualname<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60"> = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>An enumeration.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.set_seed"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.set_seed</span></h4><!-- HTML_TAG_END -->
<a id="transformers.set_seed" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.set_seed"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_utils.py#L50" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">seed<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.set_seed.seed" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.set_seed.seed"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>seed</strong> (<code>int</code>) — The seed to set.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Helper function for reproducible behavior to set the seed in <code>random</code>, <code>numpy</code>, <code>torch</code> and/or <code>tf</code> (if installed).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.torch_distributed_zero_first"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.torch_distributed_zero_first</span></h4><!-- HTML_TAG_END -->
<a id="transformers.torch_distributed_zero_first" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.torch_distributed_zero_first"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L212" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">local_rank<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.torch_distributed_zero_first.local_rank" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.torch_distributed_zero_first.local_rank"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>local_rank</strong> (<code>int</code>) — The rank of the local process.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Decorator to make all processes in distributed training wait for each local_master to do something.</p></div>
<h2 class="relative group"><a id="transformers.trainer_callback.CallbackHandler" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_callback.CallbackHandler"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Callbacks internals
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_callback.CallbackHandler"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.trainer_callback.</span><span class="font-semibold">CallbackHandler</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.trainer_callback.CallbackHandler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_callback.CallbackHandler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_callback.py#L284" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">callbacks<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokenizer<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">optimizer<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">lr_scheduler<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Internal class that just calls the list of callbacks in order.</p></div>
<h2 class="relative group"><a id="transformers.trainer_pt_utils.DistributedTensorGatherer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.DistributedTensorGatherer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Distributed Evaluation
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_pt_utils.DistributedTensorGatherer"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.trainer_pt_utils.</span><span class="font-semibold">DistributedTensorGatherer</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.trainer_pt_utils.DistributedTensorGatherer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_pt_utils.DistributedTensorGatherer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L338" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">world_size<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_samples<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">make_multiple_of<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding_index<span class="opacity-60"> = -100</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.DistributedTensorGatherer.world_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.DistributedTensorGatherer.world_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>world_size</strong> (<code>int</code>) —
The number of processes used in the distributed training.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.DistributedTensorGatherer.num_samples" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.DistributedTensorGatherer.num_samples"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_samples</strong> (<code>int</code>) —
The number of samples in our dataset.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.DistributedTensorGatherer.make_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.DistributedTensorGatherer.make_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>make_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If passed, the class assumes the datasets passed to each process are made to be a multiple of this argument
(by adding samples).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.trainer_pt_utils.DistributedTensorGatherer.padding_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.trainer_pt_utils.DistributedTensorGatherer.padding_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding_index</strong> (<code>int</code>, <em>optional</em>, defaults to -100) —
The padding index to use if the arrays don’t all have the same sequence length.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A class responsible for properly gathering tensors (or nested list/tuple of tensors) on the CPU by chunks.</p>
<p>If our dataset has 16 samples with a batch size of 2 on 3 processes and we gather then transfer on CPU at every
step, our sampler will generate the following indices:</p>
<p><code>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 0, 1]</code></p>
<p>to get something of size a multiple of 3 (so that each process gets the same dataset length). Then process 0, 1 and
2 will be responsible of making predictions for the following samples:</p>
<ul><li>P0: <code>[0, 1, 2, 3, 4, 5]</code></li>
<li>P1: <code>[6, 7, 8, 9, 10, 11]</code></li>
<li>P2: <code>[12, 13, 14, 15, 0, 1]</code></li></ul>
<p>The first batch treated on each process will be</p>
<ul><li>P0: <code>[0, 1]</code></li>
<li>P1: <code>[6, 7]</code></li>
<li>P2: <code>[12, 13]</code></li></ul>
<p>So if we gather at the end of the first batch, we will get a tensor (nested list/tuple of tensor) corresponding to
the following indices:</p>
<p><code>[0, 1, 6, 7, 12, 13]</code></p>
<p>If we directly concatenate our results without taking any precautions, the user will then get the predictions for
the indices in this order at the end of the prediction loop:</p>
<p><code>[0, 1, 6, 7, 12, 13, 2, 3, 8, 9, 14, 15, 4, 5, 10, 11, 0, 1]</code></p>
<p>For some reason, that’s not going to roll their boat. This class is there to solve that problem.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_pt_utils.DistributedTensorGatherer.add_arrays"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>add_arrays</span></h4><!-- HTML_TAG_END -->
<a id="transformers.trainer_pt_utils.DistributedTensorGatherer.add_arrays" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_pt_utils.DistributedTensorGatherer.add_arrays"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L399" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">arrays<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Add <code>arrays</code> to the internal storage, Will initialize the storage to the full size at the first arrays passed
so that if we’re bound to get an OOM, it happens at the beginning.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.trainer_pt_utils.DistributedTensorGatherer.finalize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>finalize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.trainer_pt_utils.DistributedTensorGatherer.finalize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.trainer_pt_utils.DistributedTensorGatherer.finalize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/trainer_pt_utils.py#L435" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Return the properly gathered arrays and truncate to the number of samples (since the sampler added some extras
to get each process a dataset of the same length).</p></div></div>
<h2 class="relative group"><a id="transformers.HfArgumentParser" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.HfArgumentParser"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Distributed Evaluation
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.HfArgumentParser"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">HfArgumentParser</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.HfArgumentParser" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.HfArgumentParser"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/hf_argparser.py#L44" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dataclass_types<span class="opacity-60">: typing.Union[DataClassType, typing.Iterable[DataClassType]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>This subclass of <code>argparse.ArgumentParser</code> uses type hints on dataclasses to generate arguments.</p>
<p>The class is designed to play well with the native argparse. In particular, you can add more (non-dataclass backed)
arguments to the parser after initialization and you’ll get the output back after parsing as an additional
namespace. Optional: To create sub argument groups use the <code>_argument_group_name</code> attribute in the dataclass.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.HfArgumentParser.parse_args_into_dataclasses"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>parse_args_into_dataclasses</span></h4><!-- HTML_TAG_END -->
<a id="transformers.HfArgumentParser.parse_args_into_dataclasses" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.HfArgumentParser.parse_args_into_dataclasses"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/hf_argparser.py#L161" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_remaining_strings<span class="opacity-60"> = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">look_for_args_file<span class="opacity-60"> = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args_filename<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>Tuple consisting of</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.HfArgumentParser.parse_args_into_dataclasses.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>Tuple consisting of</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<ul>
<li>the dataclass instances in the same order as they were passed to the initializer.abspath</li>
<li>if applicable, an additional namespace for more (non-dataclass backed) arguments added to the parser
after initialization.</li>
<li>The potential list of remaining argument strings. (same as argparse.ArgumentParser.parse_known_args)</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Parse command-line args into instances of the specified dataclass types.</p>
<p>This relies on argparse’s <code>ArgumentParser.parse_known_args</code>. See the doc at:
docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.parse_args</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.HfArgumentParser.parse_dict"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>parse_dict</span></h4><!-- HTML_TAG_END -->
<a id="transformers.HfArgumentParser.parse_dict" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.HfArgumentParser.parse_dict"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/hf_argparser.py#L234" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">args<span class="opacity-60">: dict</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Alternative helper method that does not use <code>argparse</code> at all, instead uses a dict and populating the dataclass
types.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.HfArgumentParser.parse_json_file"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>parse_json_file</span></h4><!-- HTML_TAG_END -->
<a id="transformers.HfArgumentParser.parse_json_file" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.HfArgumentParser.parse_json_file"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/hf_argparser.py#L220" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">json_file<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Alternative helper method that does not use <code>argparse</code> at all, instead loading a json file and populating the
dataclass types.</p></div></div>
<h2 class="relative group"><a id="transformers.debug_utils.DebugUnderflowOverflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.debug_utils.DebugUnderflowOverflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Debug Utilities
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.debug_utils.DebugUnderflowOverflow"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.debug_utils.</span><span class="font-semibold">DebugUnderflowOverflow</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.debug_utils.DebugUnderflowOverflow" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.debug_utils.DebugUnderflowOverflow"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/debug_utils.py#L28" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_frames_to_save<span class="opacity-60"> = 21</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">trace_batch_nums<span class="opacity-60"> = []</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">abort_after_batch_num<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.debug_utils.DebugUnderflowOverflow.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.debug_utils.DebugUnderflowOverflow.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<code>nn.Module</code>) —
The model to debug.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.debug_utils.DebugUnderflowOverflow.max_frames_to_save" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.debug_utils.DebugUnderflowOverflow.max_frames_to_save"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_frames_to_save</strong> (<code>int</code>, <em>optional</em>, defaults to 21) —
How many frames back to record<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.debug_utils.DebugUnderflowOverflow.trace_batch_nums(List[int]," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.debug_utils.DebugUnderflowOverflow.trace_batch_nums(List[int],"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>trace_batch_nums(<code>List[int]</code>,</strong> <em>optional</em>, defaults to <code>[]</code>) —
Which batch numbers to trace (turns detection off)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.debug_utils.DebugUnderflowOverflow.abort_after_batch_num" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.debug_utils.DebugUnderflowOverflow.abort_after_batch_num"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>abort_after_batch_num</strong> (`int“, <em>optional</em>) —
Whether to abort after a certain batch number has finished<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>This debug class helps detect and understand where the model starts getting very large or very small, and more
importantly <code>nan</code> or <code>inf</code> weight and activation elements.</p>
<p>There are 2 working modes:</p>
<ol><li>Underflow/overflow detection (default)</li>
<li>Specific batch absolute min/max tracing without detection</li></ol>
<p>Mode 1: Underflow/overflow detection</p>
<p>To activate the underflow/overflow detection, initialize the object with the model :</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->debug_overflow = DebugUnderflowOverflow(model)<!-- HTML_TAG_END --></pre></div>
<p>then run the training as normal and if <code>nan</code> or <code>inf</code> gets detected in at least one of the weight, input or output
elements this module will throw an exception and will print <code>max_frames_to_save</code> frames that lead to this event,
each frame reporting</p>
<ol><li>the fully qualified module name plus the class name whose <code>forward</code> was run</li>
<li>the absolute min and max value of all elements for each module weights, and the inputs and output</li></ol>
<p>For example, here is the header and the last few frames in detection report for <code>google/mt5-small</code> run in fp16
mixed precision :</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-attribute">Detected</span> inf/nan during batch_number=<span class="hljs-number">0</span>
<span class="hljs-attribute">Last</span> <span class="hljs-number">21</span> forward frames:
<span class="hljs-attribute">abs</span> min abs max metadata<span class="hljs-meta">
[...]</span>
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wi_0 Linear
<span class="hljs-attribute">2</span>.<span class="hljs-number">17</span>e-<span class="hljs-number">07</span> <span class="hljs-number">4</span>.<span class="hljs-number">50</span>e+<span class="hljs-number">00</span> weight
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">2</span>.<span class="hljs-number">68</span>e-<span class="hljs-number">06</span> <span class="hljs-number">3</span>.<span class="hljs-number">70</span>e+<span class="hljs-number">01</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wi_1 Linear
<span class="hljs-attribute">8</span>.<span class="hljs-number">08</span>e-<span class="hljs-number">07</span> <span class="hljs-number">2</span>.<span class="hljs-number">66</span>e+<span class="hljs-number">01</span> weight
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">1</span>.<span class="hljs-number">27</span>e-<span class="hljs-number">04</span> <span class="hljs-number">2</span>.<span class="hljs-number">37</span>e+<span class="hljs-number">02</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense.wo Linear
<span class="hljs-attribute">1</span>.<span class="hljs-number">01</span>e-<span class="hljs-number">06</span> <span class="hljs-number">6</span>.<span class="hljs-number">44</span>e+<span class="hljs-number">00</span> weight
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span> <span class="hljs-number">9</span>.<span class="hljs-number">74</span>e+<span class="hljs-number">03</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.DenseReluDense T5DenseGatedGeluDense
<span class="hljs-attribute">1</span>.<span class="hljs-number">79</span>e-<span class="hljs-number">06</span> <span class="hljs-number">4</span>.<span class="hljs-number">65</span>e+<span class="hljs-number">00</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> output
<span class="hljs-attribute">encoder</span>.block.<span class="hljs-number">2</span>.layer.<span class="hljs-number">1</span>.dropout Dropout
<span class="hljs-attribute">3</span>.<span class="hljs-number">18</span>e-<span class="hljs-number">04</span> <span class="hljs-number">6</span>.<span class="hljs-number">27</span>e+<span class="hljs-number">04</span> input[<span class="hljs-number">0</span>]
<span class="hljs-attribute">0</span>.<span class="hljs-number">00</span>e+<span class="hljs-number">00</span> inf output<!-- HTML_TAG_END --></pre></div>
<p>You can see here, that <code>T5DenseGatedGeluDense.forward</code> resulted in output activations, whose absolute max value was
around 62.7K, which is very close to fp16’s top limit of 64K. In the next frame we have <code>Dropout</code> which
renormalizes the weights, after it zeroed some of the elements, which pushes the absolute max value to more than
64K, and we get an overlow.</p>
<p>As you can see it’s the previous frames that we need to look into when the numbers start going into very large for
fp16 numbers.</p>
<p>The tracking is done in a forward hook, which gets invoked immediately after <code>forward</code> has completed.</p>
<p>By default the last 21 frames are printed. You can change the default to adjust for your needs. For example :</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=<span class="hljs-number">100</span>)<!-- HTML_TAG_END --></pre></div>
<p>To validate that you have set up this debugging feature correctly, and you intend to use it in a training that
may take hours to complete, first run it with normal tracing enabled for one of a few batches as explained in
the next section.</p>
<p>Mode 2. Specific batch absolute min/max tracing without detection</p>
<p>The second work mode is per-batch tracing with the underflow/overflow detection feature turned off.</p>
<p>Let’s say you want to watch the absolute min and max values for all the ingredients of each <code>forward</code> call of a
given batch, and only do that for batches 1 and 3. Then you instantiate this class as :</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>])<!-- HTML_TAG_END --></pre></div>
<p>And now full batches 1 and 3 will be traced using the same format as explained above. Batches are 0-indexed.</p>
<p>This is helpful if you know that the program starts misbehaving after a certain batch number, so you can
fast-forward right to that area.</p>
<p>Early stopping:</p>
<p>You can also specify the batch number after which to stop the training, with :</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>], abort_after_batch_num=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<p>This feature is mainly useful in the tracing mode, but you can use it for any mode.</p>
<p><strong>Performance</strong>:</p>
<p>As this module measures absolute <code>min</code>/`<code>max</code> of each weight of the model on every forward it’ll slow the training
down. Therefore remember to turn it off once the debugging needs have been met.</p></div>
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<h1 class="relative group"><a id="custom-layers-and-utilities" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#custom-layers-and-utilities"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Custom Layers and Utilities
</span></h1>
<p>This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling.</p>
<p>Most of those are only useful if you are studying the code of the models in the library.</p>
<h2 class="relative group"><a id="transformers.Conv1D" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conv1D"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pytorch custom modules
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Conv1D"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Conv1D</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Conv1D" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Conv1D"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1851" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">nf<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">nx<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conv1D.nf" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conv1D.nf"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>nf</strong> (<code>int</code>) — The number of output features.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.Conv1D.nx" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Conv1D.nx"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>nx</strong> (<code>int</code>) — The number of input features.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).</p>
<p>Basically works like a linear layer but the weights are transposed.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.PoolerStartLogits"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_utils.</span><span class="font-semibold">PoolerStartLogits</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.PoolerStartLogits" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.PoolerStartLogits"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1877" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerStartLogits.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerStartLogits.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) —
The config used by the model, will be used to grab the <code>hidden_size</code> of the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Compute SQuAD start logits from sequence hidden states.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.PoolerStartLogits.forward"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>forward</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.PoolerStartLogits.forward" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.PoolerStartLogits.forward"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1890" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">p_mask<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.FloatTensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerStartLogits.forward.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerStartLogits.forward.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) —
The final hidden states of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerStartLogits.forward.p_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerStartLogits.forward.p_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>p_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len)</code>, <em>optional</em>) —
Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
should be masked.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.PoolerStartLogits.forward.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.FloatTensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The start logits for SQuAD.</p>
<!-- HTML_TAG_END --></p></div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.PoolerEndLogits"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_utils.</span><span class="font-semibold">PoolerEndLogits</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.PoolerEndLogits" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.PoolerEndLogits"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1915" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerEndLogits.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerEndLogits.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) —
The config used by the model, will be used to grab the <code>hidden_size</code> of the model and the <code>layer_norm_eps</code>
to use.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Compute SQuAD end logits from sequence hidden states.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.PoolerEndLogits.forward"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>forward</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.PoolerEndLogits.forward" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.PoolerEndLogits.forward"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1932" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_states<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_positions<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">p_mask<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.FloatTensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerEndLogits.forward.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerEndLogits.forward.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) —
The final hidden states of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerEndLogits.forward.start_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerEndLogits.forward.start_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>, <em>optional</em>) —
The hidden states of the first tokens for the labeled span.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerEndLogits.forward.start_positions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerEndLogits.forward.start_positions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
The position of the first token for the labeled span.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerEndLogits.forward.p_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerEndLogits.forward.p_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>p_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len)</code>, <em>optional</em>) —
Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
should be masked.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.PoolerEndLogits.forward.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.FloatTensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The end logits for SQuAD.</p>
<!-- HTML_TAG_END --></p></div></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>One of <code>start_states</code> or <code>start_positions</code> should be not <code>None</code>. If both are set, <code>start_positions</code> overrides
<code>start_states</code>.</p></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.PoolerAnswerClass"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_utils.</span><span class="font-semibold">PoolerAnswerClass</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.PoolerAnswerClass" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.PoolerAnswerClass"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1984" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerAnswerClass.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerAnswerClass.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) —
The config used by the model, will be used to grab the <code>hidden_size</code> of the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Compute SQuAD 2.0 answer class from classification and start tokens hidden states.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.PoolerAnswerClass.forward"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>forward</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.PoolerAnswerClass.forward" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.PoolerAnswerClass.forward"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L1999" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_states<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_positions<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cls_index<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.FloatTensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerAnswerClass.forward.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerAnswerClass.forward.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) —
The final hidden states of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerAnswerClass.forward.start_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerAnswerClass.forward.start_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>, <em>optional</em>) —
The hidden states of the first tokens for the labeled span.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerAnswerClass.forward.start_positions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerAnswerClass.forward.start_positions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
The position of the first token for the labeled span.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.PoolerAnswerClass.forward.cls_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.PoolerAnswerClass.forward.cls_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
Position of the CLS token for each sentence in the batch. If <code>None</code>, takes the last token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.PoolerAnswerClass.forward.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.FloatTensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The SQuAD 2.0 answer class.</p>
<!-- HTML_TAG_END --></p></div></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>One of <code>start_states</code> or <code>start_positions</code> should be not <code>None</code>. If both are set, <code>start_positions</code> overrides
<code>start_states</code>.</p></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.SquadHeadOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_utils.</span><span class="font-semibold">SquadHeadOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.SquadHeadOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.SquadHeadOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2050" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">loss<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_top_log_probs<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_top_index<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_top_log_probs<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_top_index<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cls_logits<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SquadHeadOutput.loss" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SquadHeadOutput.loss"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned if both <code>start_positions</code> and <code>end_positions</code> are provided) —
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SquadHeadOutput.start_top_log_probs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SquadHeadOutput.start_top_log_probs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) —
Log probabilities for the top config.start_n_top start token possibilities (beam-search).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SquadHeadOutput.start_top_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SquadHeadOutput.start_top_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) —
Indices for the top config.start_n_top start token possibilities (beam-search).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SquadHeadOutput.end_top_log_probs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SquadHeadOutput.end_top_log_probs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) —
Log probabilities for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities
(beam-search).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SquadHeadOutput.end_top_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SquadHeadOutput.end_top_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) —
Indices for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities (beam-search).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SquadHeadOutput.cls_logits" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SquadHeadOutput.cls_logits"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) —
Log probabilities for the <code>is_impossible</code> label of the answers.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of question answering models using a <a href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.modeling_utils.SQuADHead">SQuADHead</a>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.SQuADHead"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_utils.</span><span class="font-semibold">SQuADHead</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.SQuADHead" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.SQuADHead"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2080" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) —
The config used by the model, will be used to grab the <code>hidden_size</code> of the model and the <code>layer_norm_eps</code>
to use.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A SQuAD head inspired by XLNet.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.SQuADHead.forward"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>forward</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.SQuADHead.forward" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.SQuADHead.forward"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2099" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start_positions<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end_positions<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cls_index<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_impossible<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">p_mask<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_dict<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.modeling_utils.SquadHeadOutput"
>transformers.modeling_utils.SquadHeadOutput</a> or <code>tuple(torch.FloatTensor)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.forward.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.forward.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len, hidden_size)</code>) —
Final hidden states of the model on the sequence tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.forward.start_positions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.forward.start_positions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
Positions of the first token for the labeled span.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.forward.end_positions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.forward.end_positions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
Positions of the last token for the labeled span.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.forward.cls_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.forward.cls_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
Position of the CLS token for each sentence in the batch. If <code>None</code>, takes the last token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.forward.is_impossible" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.forward.is_impossible"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_impossible</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
Whether the question has a possible answer in the paragraph or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.forward.p_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.forward.p_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>p_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, seq_len)</code>, <em>optional</em>) —
Mask for tokens at invalid position, such as query and special symbols (PAD, SEP, CLS). 1.0 means token
should be masked.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SQuADHead.forward.return_dict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SQuADHead.forward.return_dict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return a <a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a> instead of a plain tuple.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.SQuADHead.forward.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.modeling_utils.SquadHeadOutput"
>transformers.modeling_utils.SquadHeadOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.modeling_utils.SquadHeadOutput"
>transformers.modeling_utils.SquadHeadOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<code><class 'transformers.configuration_utils.PretrainedConfig'></code>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned if both <code>start_positions</code> and <code>end_positions</code> are provided) — Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.</li>
<li><strong>start_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) — Log probabilities for the top config.start_n_top start token possibilities (beam-search).</li>
<li><strong>start_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) — Indices for the top config.start_n_top start token possibilities (beam-search).</li>
<li><strong>end_top_log_probs</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) — Log probabilities for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities
(beam-search).</li>
<li><strong>end_top_index</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, config.start_n_top * config.end_n_top)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) — Indices for the top <code>config.start_n_top * config.end_n_top</code> end token possibilities (beam-search).</li>
<li><strong>cls_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>, returned if <code>start_positions</code> or <code>end_positions</code> is not provided) — Log probabilities for the <code>is_impossible</code> label of the answers.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.SequenceSummary"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_utils.</span><span class="font-semibold">SequenceSummary</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.SequenceSummary" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.SequenceSummary"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2197" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config<span class="opacity-60">: PretrainedConfig</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SequenceSummary.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SequenceSummary.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) —
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for the default values it uses):</p>
<ul>
<li>
<p><strong>summary_type</strong> (<code>str</code>) — The method to use to make this summary. Accepted values are:</p>
<ul>
<li><code>"last"</code> — Take the last token hidden state (like XLNet)</li>
<li><code>"first"</code> — Take the first token hidden state (like Bert)</li>
<li><code>"mean"</code> — Take the mean of all tokens hidden states</li>
<li><code>"cls_index"</code> — Supply a Tensor of classification token position (GPT/GPT-2)</li>
<li><code>"attn"</code> — Not implemented now, use multi-head attention</li>
</ul>
</li>
<li>
<p><strong>summary_use_proj</strong> (<code>bool</code>) — Add a projection after the vector extraction.</p>
</li>
<li>
<p><strong>summary_proj_to_labels</strong> (<code>bool</code>) — If <code>True</code>, the projection outputs to <code>config.num_labels</code> classes
(otherwise to <code>config.hidden_size</code>).</p>
</li>
<li>
<p><strong>summary_activation</strong> (<code>Optional[str]</code>) — Set to <code>"tanh"</code> to add a tanh activation to the output,
another string or <code>None</code> will add no activation.</p>
</li>
<li>
<p><strong>summary_first_dropout</strong> (<code>float</code>) — Optional dropout probability before the projection and activation.</p>
</li>
<li>
<p><strong>summary_last_dropout</strong> (<code>float</code>)— Optional dropout probability after the projection and activation.</p>
</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Compute a single vector summary of a sequence hidden states.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.SequenceSummary.forward"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>forward</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.SequenceSummary.forward" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.SequenceSummary.forward"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2252" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cls_index<span class="opacity-60">: typing.Optional[torch.LongTensor] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.FloatTensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SequenceSummary.forward.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SequenceSummary.forward.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>[batch_size, seq_len, hidden_size]</code>) —
The hidden states of the last layer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.SequenceSummary.forward.cls_index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.SequenceSummary.forward.cls_index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_index</strong> (<code>torch.LongTensor</code> of shape <code>[batch_size]</code> or <code>[batch_size, ...]</code> where … are optional leading dimensions of <code>hidden_states</code>, <em>optional</em>) —
Used if <code>summary_type == "cls_index"</code> and takes the last token of the sequence as classification token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.SequenceSummary.forward.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.FloatTensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The summary of the sequence hidden states.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Compute a single vector summary of a sequence hidden states.</p></div></div>
<h2 class="relative group"><a id="transformers.apply_chunking_to_forward" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.apply_chunking_to_forward"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>PyTorch Helper Functions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.apply_chunking_to_forward"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.apply_chunking_to_forward</span></h4><!-- HTML_TAG_END -->
<a id="transformers.apply_chunking_to_forward" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.apply_chunking_to_forward"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2401" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">forward_fn<span class="opacity-60">: typing.Callable[..., torch.Tensor]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">chunk_size<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">chunk_dim<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*input_tensors<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.Tensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.apply_chunking_to_forward.forward_fn" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.apply_chunking_to_forward.forward_fn"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>forward_fn</strong> (<code>Callable[..., torch.Tensor]</code>) —
The forward function of the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.apply_chunking_to_forward.chunk_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.apply_chunking_to_forward.chunk_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>chunk_size</strong> (<code>int</code>) —
The chunk size of a chunked tensor: <code>num_chunks = len(input_tensors[0]) / chunk_size</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.apply_chunking_to_forward.chunk_dim" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.apply_chunking_to_forward.chunk_dim"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>chunk_dim</strong> (<code>int</code>) —
The dimension over which the <code>input_tensors</code> should be chunked.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.apply_chunking_to_forward.input_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.apply_chunking_to_forward.input_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_tensors</strong> (<code>Tuple[torch.Tensor]</code>) —
The input tensors of <code>forward_fn</code> which will be chunked<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.apply_chunking_to_forward.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.Tensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A tensor with the same shape as the <code>forward_fn</code> would have given if applied`.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>This function chunks the <code>input_tensors</code> into smaller input tensor parts of size <code>chunk_size</code> over the dimension
<code>chunk_dim</code>. It then applies a layer <code>forward_fn</code> to each chunk independently to save memory.</p>
<p>If the <code>forward_fn</code> is independent across the <code>chunk_dim</code> this function will yield the same result as directly
applying <code>forward_fn</code> to <code>input_tensors</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># rename the usual forward() fn to forward_chunk()</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward_chunk</span>(<span class="hljs-params">self, hidden_states</span>):
hidden_states = self.decoder(hidden_states)
<span class="hljs-keyword">return</span> hidden_states
<span class="hljs-comment"># implement a chunked forward function</span>
<span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, hidden_states</span>):
<span class="hljs-keyword">return</span> apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.find_pruneable_heads_and_indices"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.modeling_utils.find_pruneable_heads_and_indices</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.find_pruneable_heads_and_indices" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.find_pruneable_heads_and_indices"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L95" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">heads<span class="opacity-60">: typing.List[int]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">n_heads<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">head_size<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">already_pruned_heads<span class="opacity-60">: typing.Set[int]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Tuple[Set[int], torch.LongTensor]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.find_pruneable_heads_and_indices.heads" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.find_pruneable_heads_and_indices.heads"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>heads</strong> (<code>List[int]</code>) — List of the indices of heads to prune.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.find_pruneable_heads_and_indices.n_heads" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.find_pruneable_heads_and_indices.n_heads"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>n_heads</strong> (<code>int</code>) — The number of heads in the model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.find_pruneable_heads_and_indices.head_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.find_pruneable_heads_and_indices.head_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>head_size</strong> (<code>int</code>) — The size of each head.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.find_pruneable_heads_and_indices.already_pruned_heads" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.find_pruneable_heads_and_indices.already_pruned_heads"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>already_pruned_heads</strong> (<code>Set[int]</code>) — A set of already pruned heads.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.find_pruneable_heads_and_indices.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Tuple[Set[int], torch.LongTensor]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A tuple with the remaining heads and their corresponding indices.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Finds the heads and their indices taking <code>already_pruned_heads</code> into account.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.prune_layer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.prune_layer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.prune_layer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.prune_layer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2377" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">layer<span class="opacity-60">: typing.Union[torch.nn.modules.linear.Linear, transformers.modeling_utils.Conv1D]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">index<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dim<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.nn.Linear</code> or <a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.Conv1D"
>Conv1D</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.prune_layer.layer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.prune_layer.layer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>layer</strong> (<code>Union[torch.nn.Linear, Conv1D]</code>) — The layer to prune.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.prune_layer.index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.prune_layer.index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>index</strong> (<code>torch.LongTensor</code>) — The indices to keep in the layer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.prune_layer.dim" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.prune_layer.dim"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dim</strong> (<code>int</code>, <em>optional</em>) — The dimension on which to keep the indices.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.prune_layer.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.nn.Linear</code> or <a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.Conv1D"
>Conv1D</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Prune a Conv1D or linear layer to keep only entries in index.</p>
<p>Used to remove heads.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.prune_conv1d_layer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.modeling_utils.prune_conv1d_layer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.prune_conv1d_layer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.prune_conv1d_layer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2344" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">layer<span class="opacity-60">: Conv1D</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">index<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dim<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.Conv1D"
>Conv1D</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.prune_conv1d_layer.layer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.prune_conv1d_layer.layer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>layer</strong> (<a href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.Conv1D">Conv1D</a>) — The layer to prune.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.prune_conv1d_layer.index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.prune_conv1d_layer.index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>index</strong> (<code>torch.LongTensor</code>) — The indices to keep in the layer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.prune_conv1d_layer.dim" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.prune_conv1d_layer.dim"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to 1) — The dimension on which to keep the indices.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.prune_conv1d_layer.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/internal/modeling_utils#transformers.Conv1D"
>Conv1D</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Prune a Conv1D layer to keep only entries in index. A Conv1D work as a Linear layer (see e.g. BERT) but the weights
are transposed.</p>
<p>Used to remove heads.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_utils.prune_linear_layer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.modeling_utils.prune_linear_layer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_utils.prune_linear_layer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_utils.prune_linear_layer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_utils.py#L2310" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">layer<span class="opacity-60">: Linear</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">index<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">dim<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.nn.Linear</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.prune_linear_layer.layer" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.prune_linear_layer.layer"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>layer</strong> (<code>torch.nn.Linear</code>) — The layer to prune.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.prune_linear_layer.index" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.prune_linear_layer.index"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>index</strong> (<code>torch.LongTensor</code>) — The indices to keep in the layer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_utils.prune_linear_layer.dim" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_utils.prune_linear_layer.dim"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to 0) — The dimension on which to keep the indices.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_utils.prune_linear_layer.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.nn.Linear</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The pruned layer as a new layer with <code>requires_grad=True</code>.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Prune a linear layer to keep only entries in index.</p>
<p>Used to remove heads.</p></div>
<h2 class="relative group"><a id="transformers.modeling_tf_utils.TFConv1D" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.TFConv1D"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TensorFlow custom layers
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFConv1D"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFConv1D</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFConv1D" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFConv1D"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1819" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_utils.TFConv1D.nf" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.TFConv1D.nf"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>nf</strong> (<code>int</code>) —
The number of output features.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_utils.TFConv1D.nx" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.TFConv1D.nx"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>nx</strong> (<code>int</code>) —
The number of input features.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_utils.TFConv1D.initializer_range" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.TFConv1D.initializer_range"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) —
The standard deviation to use to initialize the weights.
kwargs —
Additional keyword arguments passed along to the <code>__init__</code> of <code>tf.keras.layers.Layer</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).</p>
<p>Basically works like a linear layer but the weights are transposed.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFSharedEmbeddings"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFSharedEmbeddings</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFSharedEmbeddings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFSharedEmbeddings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1859" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSharedEmbeddings.vocab_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSharedEmbeddings.vocab_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>vocab_size</strong> (<code>int</code>) —
The size of the vocabulary, e.g., the number of unique tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSharedEmbeddings.hidden_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSharedEmbeddings.hidden_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_size</strong> (<code>int</code>) —
The size of the embedding vectors.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSharedEmbeddings.initializer_range" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSharedEmbeddings.initializer_range"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>initializer_range</strong> (<code>float</code>, <em>optional</em>) —
The standard deviation to use when initializing the weights. If no value is provided, it will default to
{@html "<span class="\"katex\""><span class="\"katex-mathml\""><math xmlns="\"http://www.w3.org/1998/Math/MathML\""><semantics><mrow><mn>1</mn><mi mathvariant="\"normal\"">/</mi><msqrt><mrow><mi>h</mi><mi>i</mi><mi>d</mi><mi>d</mi><mi>e</mi><mi>n</mi><mi mathvariant="\"normal\"">_</mi><mi>s</mi><mi>i</mi><mi>z</mi><mi>e</mi></mrow></msqrt></mrow><annotation encoding="\"application/x-tex\"">1/\\sqrt{hidden\\_size}</annotation></semantics></math></span><span class="\"katex-html\"" aria-hidden="\"true\""><span class="\"base\""><span class="\"strut\"" style="\"height:1.24em;vertical-align:-0.3628em;\""></span><span class="\"mord\"">1/</span><span class="\"mord" sqrt\"><span class="\"vlist-t" vlist-t2\"><span class="\"vlist-r\""><span class="\"vlist\"" style="\"height:0.8772em;\""><span class="\"svg-align\"" style="\"top:-3.2em;\""><span class="\"pstrut\"" style="\"height:3.2em;\""></span><span class="\"mord\"" style="\"padding-left:1em;\""><span class="\"mord" mathnormal\">hi</span><span class="\"mord" mathnormal\">dd</span><span class="\"mord" mathnormal\">e</span><span class="\"mord" mathnormal\">n</span><span class="\"mord\"" style="\"margin-right:0.02778em;\"">_</span><span class="\"mord" mathnormal\">s</span><span class="\"mord" mathnormal\">i</span><span class="\"mord" mathnormal\">ze</span></span></span><span style="\"top:-2.8372em;\""><span class="\"pstrut\"" style="\"height:3.2em;\""></span><span class="\"hide-tail\"" style="\"min-width:1.02em;height:1.28em;\""><svg xmlns="\"http://www.w3.org/2000/svg\"" width="400em" height="1.28em" viewBox="0 0 400000 1296" preserveAspectRatio="xMinYMin slice"><path d="M263,681c0.7,0,18,39.7,52,119\nc34,79.3,68.167,158.7,102.5,238c34.3,79.3,51.8,119.3,52.5,120\nc340,-704.7,510.7,-1060.3,512,-1067\nl0 -0\nc4.7,-7.3,11,-11,19,-11\nH40000v40H1012.3\ns-271.3,567,-271.3,567c-38.7,80.7,-84,175,-136,283c-52,108,-89.167,185.3,-111.5,232\nc-22.3,46.7,-33.8,70.3,-34.5,71c-4.7,4.7,-12.3,7,-23,7s-12,-1,-12,-1\ns-109,-253,-109,-253c-72.7,-168,-109.3,-252,-110,-252c-10.7,8,-22,16.7,-34,26\nc-22,17.3,-33.3,26,-34,26s-26,-26,-26,-26s76,-59,76,-59s76,-60,76,-60z\nM1001 80h400000v40h-400000z"/></svg></span></span></span><span class="\"vlist-s\"">​</span></span><span class="\"vlist-r\""><span class="\"vlist\"" style="\"height:0.3628em;\""><span></span></span></span></span></span></span></span></span>"}.
kwargs —
Additional keyword arguments passed along to the <code>__init__</code> of <code>tf.keras.layers.Layer</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Construct shared token embeddings.</p>
<p>The weights of the embedding layer is usually shared with the weights of the linear decoder when doing language
modeling.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFSharedEmbeddings.call"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>call</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFSharedEmbeddings.call" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFSharedEmbeddings.call"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1904" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">inputs<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mode<span class="opacity-60">: str = 'embedding'</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Tensor</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSharedEmbeddings.call.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSharedEmbeddings.call.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (<code>tf.Tensor</code>) —
In embedding mode, should be an int64 tensor with shape <code>[batch_size, length]</code>.</p>
<p>In linear mode, should be a float tensor with shape <code>[batch_size, length, hidden_size]</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSharedEmbeddings.call.mode" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSharedEmbeddings.call.mode"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mode</strong> (<code>str</code>, defaults to <code>"embedding"</code>) —
A valid value is either <code>"embedding"</code> or <code>"linear"</code>, the first one indicates that the layer should be
used as an embedding layer, the second one that the layer should be used as a linear decoder.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFSharedEmbeddings.call.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Tensor</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>In embedding mode, the output is a float32 embedding tensor, with shape <code>[batch_size, length, embedding_size]</code>.</p>
<p>In linear mode, the output is a float32 with shape <code>[batch_size, length, vocab_size]</code>.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Get token embeddings of inputs or decode final hidden state.</p>
<p>Shared weights logic is adapted from
<a href="https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24" rel="nofollow">here</a>.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFSequenceSummary"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFSequenceSummary</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFSequenceSummary" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFSequenceSummary"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L1957" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*args<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSequenceSummary.config" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSequenceSummary.config"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>config</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) —
The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
config class of your model for the default values it uses):</p>
<ul>
<li>
<p><strong>summary_type</strong> (<code>str</code>) — The method to use to make this summary. Accepted values are:</p>
<ul>
<li><code>"last"</code> — Take the last token hidden state (like XLNet)</li>
<li><code>"first"</code> — Take the first token hidden state (like Bert)</li>
<li><code>"mean"</code> — Take the mean of all tokens hidden states</li>
<li><code>"cls_index"</code> — Supply a Tensor of classification token position (GPT/GPT-2)</li>
<li><code>"attn"</code> — Not implemented now, use multi-head attention</li>
</ul>
</li>
<li>
<p><strong>summary_use_proj</strong> (<code>bool</code>) — Add a projection after the vector extraction.</p>
</li>
<li>
<p><strong>summary_proj_to_labels</strong> (<code>bool</code>) — If <code>True</code>, the projection outputs to <code>config.num_labels</code> classes
(otherwise to <code>config.hidden_size</code>).</p>
</li>
<li>
<p><strong>summary_activation</strong> (<code>Optional[str]</code>) — Set to <code>"tanh"</code> to add a tanh activation to the output,
another string or <code>None</code> will add no activation.</p>
</li>
<li>
<p><strong>summary_first_dropout</strong> (<code>float</code>) — Optional dropout probability before the projection and activation.</p>
</li>
<li>
<p><strong>summary_last_dropout</strong> (<code>float</code>)— Optional dropout probability after the projection and activation.</p>
</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSequenceSummary.initializer_range" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSequenceSummary.initializer_range"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>initializer_range</strong> (<code>float</code>, defaults to 0.02) — The standard deviation to use to initialize the weights.
kwargs —
Additional keyword arguments passed along to the <code>__init__</code> of <code>tf.keras.layers.Layer</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Compute a single vector summary of a sequence hidden states.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFSequenceSummary.register_for_auto_class"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>register_for_auto_class</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFSequenceSummary.register_for_auto_class" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFSequenceSummary.register_for_auto_class"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L2072" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">auto_class<span class="opacity-60"> = 'TFAutoModel'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFSequenceSummary.register_for_auto_class.auto_class" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFSequenceSummary.register_for_auto_class.auto_class"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"TFAutoModel"</code>) —
The auto class to register this new model with.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Register this class with a given auto class. This should only be used for custom models as the ones in the
library are already mapped with an auto class.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This API is experimental and may have some slight breaking changes in the next releases.</p></div></div></div>
<h2 class="relative group"><a id="transformers.modeling_tf_utils.TFCausalLanguageModelingLoss" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.TFCausalLanguageModelingLoss"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TensorFlow loss functions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFCausalLanguageModelingLoss"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFCausalLanguageModelingLoss</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFCausalLanguageModelingLoss" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFCausalLanguageModelingLoss"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L170" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Loss function suitable for causal language modeling (CLM), that is, the task of guessing the next token.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFMaskedLanguageModelingLoss</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFMaskedLanguageModelingLoss"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L261" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Loss function suitable for masked language modeling (MLM), that is, the task of guessing the masked tokens.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFMultipleChoiceLoss"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFMultipleChoiceLoss</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFMultipleChoiceLoss" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFMultipleChoiceLoss"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L251" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Loss function suitable for multiple choice tasks.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFQuestionAnsweringLoss"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFQuestionAnsweringLoss</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFQuestionAnsweringLoss" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFQuestionAnsweringLoss"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L192" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Loss function suitable for question answering.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFSequenceClassificationLoss"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFSequenceClassificationLoss</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFSequenceClassificationLoss" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFSequenceClassificationLoss"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L235" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Loss function suitable for sequence classification.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.TFTokenClassificationLoss"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.modeling_tf_utils.</span><span class="font-semibold">TFTokenClassificationLoss</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.TFTokenClassificationLoss" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.TFTokenClassificationLoss"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L207" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Loss function suitable for token classification.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.</p></div></div>
<h2 class="relative group"><a id="transformers.modeling_tf_utils.get_initializer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.get_initializer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>TensorFlow Helper Functions
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.get_initializer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.modeling_tf_utils.get_initializer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.get_initializer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.get_initializer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L2099" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">initializer_range<span class="opacity-60">: float = 0.02</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.initializers.TruncatedNormal</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_utils.get_initializer.initializer_range" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.get_initializer.initializer_range"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>initializer_range</strong> (<em>float</em>, defaults to 0.02) — Standard deviation of the initializer range.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.modeling_tf_utils.get_initializer.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.initializers.TruncatedNormal</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The truncated normal initializer.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Creates a <code>tf.initializers.TruncatedNormal</code> with the given range.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.modeling_tf_utils.keras_serializable"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.modeling_tf_utils.keras_serializable</span></h4><!-- HTML_TAG_END -->
<a id="transformers.modeling_tf_utils.keras_serializable" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.modeling_tf_utils.keras_serializable"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/modeling_tf_utils.py#L105" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.modeling_tf_utils.keras_serializable.cls" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.modeling_tf_utils.keras_serializable.cls"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls</strong> (a <code>tf.keras.layers.Layers subclass</code>) —
Typically a <code>TF.MainLayer</code> class in this project, in general must accept a <code>config</code> argument to its
initializer.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Decorate a Keras Layer class to support Keras serialization.</p>
<p>This is done by:</p>
<ol><li>Adding a <code>transformers_config</code> dict to the Keras config dictionary in <code>get_config</code> (called by Keras at
serialization time.</li>
<li>Wrapping <code>__init__</code> to accept that <code>transformers_config</code> dict (passed by Keras at deserialization time) and
convert it to a config object for the actual layer initializer.</li>
<li>Registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does not
need to be supplied in <code>custom_objects</code> in the call to <code>tf.keras.models.load_model</code>.</li></ol></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.shape_list"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.shape_list</span></h4><!-- HTML_TAG_END -->
<a id="transformers.shape_list" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.shape_list"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tf_utils.py#L31" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tensor<span class="opacity-60">: typing.Union[tensorflow.python.framework.ops.Tensor, numpy.ndarray]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.shape_list.tensor" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.shape_list.tensor"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tensor</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code>) — The tensor we want the shape of.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.shape_list.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The shape of the tensor as a list.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Deal with dynamic shape in tensorflow cleanly.</p></div>
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<h1 class="relative group"><a id="general-utilities" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#general-utilities"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>General Utilities
</span></h1>
<p>This page lists all of Transformers general utility functions that are found in the file <code>file_utils.py</code>.</p>
<p>Most of those are only useful if you are studying the general code in the library.</p>
<h2 class="relative group"><a id="transformers.file_utils.ExplicitEnum" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.ExplicitEnum"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Enums and namedtuples
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.ExplicitEnum"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.file_utils.</span><span class="font-semibold">ExplicitEnum</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.file_utils.ExplicitEnum" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.ExplicitEnum"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2710" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">names<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">qualname<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60"> = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Enum with more explicit error message for missing values.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PaddingStrategy"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.file_utils.</span><span class="font-semibold">PaddingStrategy</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PaddingStrategy" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PaddingStrategy"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2722" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">names<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">qualname<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60"> = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Possible values for the <code>padding</code> argument in <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizerBase.<strong>call</strong>()</a>. Useful for tab-completion in an
IDE.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TensorType"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TensorType</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TensorType" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TensorType"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2733" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">names<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">qualname<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60"> = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Possible values for the <code>return_tensors</code> argument in <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizerBase.<strong>call</strong>()</a>. Useful for
tab-completion in an IDE.</p></div>
<h2 class="relative group"><a id="transformers.add_start_docstrings" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.add_start_docstrings"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Special Decorators
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.add_start_docstrings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.add_start_docstrings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.add_start_docstrings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.add_start_docstrings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L863" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*docstr<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.add_start_docstrings_to_model_forward"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.file_utils.add_start_docstrings_to_model_forward</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.add_start_docstrings_to_model_forward" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.add_start_docstrings_to_model_forward"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L871" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*docstr<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.add_end_docstrings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.add_end_docstrings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.add_end_docstrings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.add_end_docstrings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L893" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*docstr<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.add_code_sample_docstrings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.file_utils.add_code_sample_docstrings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.add_code_sample_docstrings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.add_code_sample_docstrings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L1673" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*docstr<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">processor_class<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">checkpoint<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config_class<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">mask<span class="opacity-60"> = '[MASK]'</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model_cls<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">modality<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">expected_output<span class="opacity-60"> = ''</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">expected_loss<span class="opacity-60"> = ''</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.replace_return_docstrings"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.file_utils.replace_return_docstrings</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.replace_return_docstrings" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.replace_return_docstrings"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L1748" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">config_class<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div>
<h2 class="relative group"><a id="transformers.file_utils.cached_property" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.cached_property"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Special Properties
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.cached_property"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.file_utils.</span><span class="font-semibold">cached_property</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.file_utils.cached_property" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.cached_property"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2466" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fget<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fset<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">fdel<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">doc<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Descriptor that mimics @property but caches output in member variable.</p>
<p>From tensorflow_datasets</p>
<p>Built-in in functools from Python 3.8.</p></div>
<h2 class="relative group"><a id="transformers._LazyModule" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers._LazyModule"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Other Utilities
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers._LazyModule"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">_LazyModule</span></span></h3><!-- HTML_TAG_END -->
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<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2745" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">name<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module_file<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">import_structure<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module_spec<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">extra_objects<span class="opacity-60"> = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Module class that surfaces all objects but only performs associated imports when the objects are requested.</p></div>
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<h1 class="relative group"><a id="utilities-for-pipelines" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#utilities-for-pipelines"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Utilities for pipelines
</span></h1>
<p>This page lists all the utility functions the library provides for pipelines.</p>
<p>Most of those are only useful if you are studying the code of the models in the library.</p>
<h2 class="relative group"><a id="transformers.pipelines.ArgumentHandler" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipelines.ArgumentHandler"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Argument handling
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.pipelines.ArgumentHandler"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.pipelines.</span><span class="font-semibold">ArgumentHandler</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.pipelines.ArgumentHandler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.pipelines.ArgumentHandler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L394" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Base interface for handling arguments for each <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.pipelines.ZeroShotClassificationArgumentHandler"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.pipelines.</span><span class="font-semibold">ZeroShotClassificationArgumentHandler</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.pipelines.ZeroShotClassificationArgumentHandler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.pipelines.ZeroShotClassificationArgumentHandler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/zero_shot_classification.py#L14" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Handles arguments for zero-shot for text classification by turning each possible label into an NLI
premise/hypothesis pair.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.pipelines.QuestionAnsweringArgumentHandler"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.pipelines.</span><span class="font-semibold">QuestionAnsweringArgumentHandler</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.pipelines.QuestionAnsweringArgumentHandler" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.pipelines.QuestionAnsweringArgumentHandler"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/question_answering.py#L32" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question & context) to be mapped to
internal <code>SquadExample</code></p>
<p>QuestionAnsweringArgumentHandler manages all the possible to create a <code>SquadExample</code>from the command-line
supplied arguments.</p></div>
<h2 class="relative group"><a id="transformers.PipelineDataFormat" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Data format
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PipelineDataFormat"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PipelineDataFormat</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PipelineDataFormat" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PipelineDataFormat"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L404" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">column<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.output_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.output_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_path</strong> (<code>str</code>, <em>optional</em>) — Where to save the outgoing data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.input_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.input_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_path</strong> (<code>str</code>, <em>optional</em>) — Where to look for the input data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.column" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.column"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>column</strong> (<code>str</code>, <em>optional</em>) — The column to read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.overwrite" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.overwrite"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to overwrite the <code>output_path</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for all the pipeline supported data format both for reading and writing. Supported data formats
currently includes:</p>
<ul><li>JSON</li>
<li>CSV</li>
<li>stdin/stdout (pipe)</li></ul>
<p><code>PipelineDataFormat</code> also includes some utilities to work with multi-columns like mapping from datasets columns to
pipelines keyword arguments through the <code>dataset_kwarg_1=dataset_column_1</code> format.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PipelineDataFormat.from_str"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_str</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PipelineDataFormat.from_str" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PipelineDataFormat.from_str"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L481" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">format<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">column<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.PipelineDataFormat"
>PipelineDataFormat</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.from_str.output_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.from_str.output_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_path</strong> (<code>str</code>, <em>optional</em>) —
Where to save the outgoing data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.from_str.input_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.from_str.input_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_path</strong> (<code>str</code>, <em>optional</em>) —
Where to look for the input data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.from_str.column" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.from_str.column"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>column</strong> (<code>str</code>, <em>optional</em>) —
The column to read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.from_str.overwrite" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.from_str.overwrite"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to overwrite the <code>output_path</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PipelineDataFormat.from_str.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.PipelineDataFormat"
>PipelineDataFormat</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The proper data format.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Creates an instance of the right subclass of <a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a> depending on <code>format</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PipelineDataFormat.save"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PipelineDataFormat.save" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PipelineDataFormat.save"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L453" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60">: typing.Union[dict, typing.List[dict]]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.save.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.save.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>dict</code> or list of <code>dict</code>) — The data to store.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save the provided data object with the representation for the current <a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PipelineDataFormat.save_binary"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_binary</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PipelineDataFormat.save_binary" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PipelineDataFormat.save_binary"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L463" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60">: typing.Union[dict, typing.List[dict]]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipelineDataFormat.save_binary.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipelineDataFormat.save_binary.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>dict</code> or list of <code>dict</code>) — The data to store.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PipelineDataFormat.save_binary.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Path where the data has been saved.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Save the provided data object as a pickle-formatted binary data on the disk.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.CsvPipelineDataFormat"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">CsvPipelineDataFormat</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.CsvPipelineDataFormat" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.CsvPipelineDataFormat"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L517" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">column<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.CsvPipelineDataFormat.output_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.CsvPipelineDataFormat.output_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_path</strong> (<code>str</code>, <em>optional</em>) — Where to save the outgoing data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.CsvPipelineDataFormat.input_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.CsvPipelineDataFormat.input_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_path</strong> (<code>str</code>, <em>optional</em>) — Where to look for the input data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.CsvPipelineDataFormat.column" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.CsvPipelineDataFormat.column"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>column</strong> (<code>str</code>, <em>optional</em>) — The column to read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.CsvPipelineDataFormat.overwrite" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.CsvPipelineDataFormat.overwrite"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to overwrite the <code>output_path</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Support for pipelines using CSV data format.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.CsvPipelineDataFormat.save"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save</span></h4><!-- HTML_TAG_END -->
<a id="transformers.CsvPipelineDataFormat.save" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.CsvPipelineDataFormat.save"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L547" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60">: typing.List[dict]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.CsvPipelineDataFormat.save.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.CsvPipelineDataFormat.save.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>List[dict]</code>) — The data to store.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save the provided data object with the representation for the current <a href="/docs/transformers/pr_16143/en/internal/pipelines_utils#transformers.PipelineDataFormat">PipelineDataFormat</a>.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.JsonPipelineDataFormat"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">JsonPipelineDataFormat</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.JsonPipelineDataFormat" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.JsonPipelineDataFormat"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L561" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">column<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.JsonPipelineDataFormat.output_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.JsonPipelineDataFormat.output_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_path</strong> (<code>str</code>, <em>optional</em>) — Where to save the outgoing data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.JsonPipelineDataFormat.input_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.JsonPipelineDataFormat.input_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_path</strong> (<code>str</code>, <em>optional</em>) — Where to look for the input data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.JsonPipelineDataFormat.column" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.JsonPipelineDataFormat.column"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>column</strong> (<code>str</code>, <em>optional</em>) — The column to read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.JsonPipelineDataFormat.overwrite" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.JsonPipelineDataFormat.overwrite"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to overwrite the <code>output_path</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Support for pipelines using JSON file format.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.JsonPipelineDataFormat.save"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save</span></h4><!-- HTML_TAG_END -->
<a id="transformers.JsonPipelineDataFormat.save" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.JsonPipelineDataFormat.save"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L592" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60">: dict</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.JsonPipelineDataFormat.save.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.JsonPipelineDataFormat.save.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>dict</code>) — The data to store.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Save the provided data object in a json file.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PipedPipelineDataFormat"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PipedPipelineDataFormat</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PipedPipelineDataFormat" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PipedPipelineDataFormat"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L603" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">output_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_path<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">column<span class="opacity-60">: typing.Optional[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">overwrite<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipedPipelineDataFormat.output_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipedPipelineDataFormat.output_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>output_path</strong> (<code>str</code>, <em>optional</em>) — Where to save the outgoing data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipedPipelineDataFormat.input_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipedPipelineDataFormat.input_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_path</strong> (<code>str</code>, <em>optional</em>) — Where to look for the input data.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipedPipelineDataFormat.column" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipedPipelineDataFormat.column"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>column</strong> (<code>str</code>, <em>optional</em>) — The column to read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipedPipelineDataFormat.overwrite" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipedPipelineDataFormat.overwrite"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>overwrite</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to overwrite the <code>output_path</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Read data from piped input to the python process. For multi columns data, columns should separated by </p>
<p>If columns are provided, then the output will be a dictionary with {column_x: value_x}</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PipedPipelineDataFormat.save"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PipedPipelineDataFormat.save" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PipedPipelineDataFormat.save"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L633" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">data<span class="opacity-60">: dict</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PipedPipelineDataFormat.save.data" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PipedPipelineDataFormat.save.data"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>data</strong> (<code>dict</code>) — The data to store.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Print the data.</p></div></div>
<h2 class="relative group"><a id="transformers.pipelines.PipelineException" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipelines.PipelineException"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Utilities
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.pipelines.PipelineException"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.pipelines.</span><span class="font-semibold">PipelineException</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.pipelines.PipelineException" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.pipelines.PipelineException"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/pipelines/base.py#L377" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">task<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">model<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">reason<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipelines.PipelineException.task" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipelines.PipelineException.task"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>task</strong> (<code>str</code>) — The task of the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipelines.PipelineException.model" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipelines.PipelineException.model"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model</strong> (<code>str</code>) — The model used by the pipeline.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.pipelines.PipelineException.reason" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.pipelines.PipelineException.reason"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>reason</strong> (<code>str</code>) — The error message to display.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Raised by a <a href="/docs/transformers/pr_16143/en/main_classes/pipelines#transformers.Pipeline">Pipeline</a> when handling <strong>call</strong>.</p></div>
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<h1 class="relative group"><a id="utilities-for-generation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#utilities-for-generation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Utilities for Generation
</span></h1>
<p>This page lists all the utility functions used by <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate">generate()</a>,
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.greedy_search">greedy_search()</a>,
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.sample">sample()</a>,
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_search">beam_search()</a>,
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_sample">beam_sample()</a>,
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.group_beam_search">group_beam_search()</a>, and
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.constrained_beam_search">constrained_beam_search()</a>.</p>
<p>Most of those are only useful if you are studying the code of the generate methods in the library.</p>
<h2 class="relative group"><a id="generate-outputs" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#generate-outputs"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Generate Outputs
</span></h2>
<p>The output of <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate">generate()</a> is an instance of a subclass of
<a href="/docs/transformers/pr_16143/en/main_classes/output#transformers.file_utils.ModelOutput">ModelOutput</a>. This output is a data structure containing all the information returned
by <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate">generate()</a>, but that can also be used as tuple or dictionary.</p>
<p>Here’s an example:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
model = GPT2LMHeadModel.from_pretrained(<span class="hljs-string">"gpt2"</span>)
inputs = tokenizer(<span class="hljs-string">"Hello, my dog is cute and "</span>, return_tensors=<span class="hljs-string">"pt"</span>)
generation_output = model.generate(**inputs, return_dict_in_generate=<span class="hljs-literal">True</span>, output_scores=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>The <code>generation_output</code> object is a <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.generation_utils.GreedySearchDecoderOnlyOutput">GreedySearchDecoderOnlyOutput</a>, as we can
see in the documentation of that class below, it means it has the following attributes:</p>
<ul><li><code>sequences</code>: the generated sequences of tokens</li>
<li><code>scores</code> (optional): the prediction scores of the language modelling head, for each generation step</li>
<li><code>hidden_states</code> (optional): the hidden states of the model, for each generation step</li>
<li><code>attentions</code> (optional): the attention weights of the model, for each generation step</li></ul>
<p>Here we have the <code>scores</code> since we passed along <code>output_scores=True</code>, but we don’t have <code>hidden_states</code> and
<code>attentions</code> because we didn’t pass <code>output_hidden_states=True</code> or <code>output_attentions=True</code>.</p>
<p>You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get <code>None</code>. Here for instance <code>generation_output.scores</code> are all the generated prediction scores of the
language modeling head, and <code>generation_output.attentions</code> is <code>None</code>.</p>
<p>When using our <code>generation_output</code> object as a tuple, it only keeps the attributes that don’t have <code>None</code> values.
Here, for instance, it has two elements, <code>loss</code> then <code>logits</code>, so</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->generation_output[:<span class="hljs-number">2</span>]<!-- HTML_TAG_END --></pre></div>
<p>will return the tuple <code>(generation_output.sequences, generation_output.scores)</code> for instance.</p>
<p>When using our <code>generation_output</code> object as a dictionary, it only keeps the attributes that don’t have <code>None</code>
values. Here, for instance, it has two keys that are <code>sequences</code> and <code>scores</code>.</p>
<p>We document here all output types.</p>
<h3 class="relative group"><a id="transformers.generation_utils.GreedySearchDecoderOnlyOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchDecoderOnlyOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>GreedySearchOutput
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GreedySearchDecoderOnlyOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">GreedySearchDecoderOnlyOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GreedySearchDecoderOnlyOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GreedySearchDecoderOnlyOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L62" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchDecoderOnlyOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchDecoderOnlyOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchDecoderOnlyOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchDecoderOnlyOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. <code>(max_length-input_ids.shape[-1],)</code>-shaped tuple of <code>torch.FloatTensor</code> with each
tensor of shape <code>(batch_size, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchDecoderOnlyOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchDecoderOnlyOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchDecoderOnlyOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchDecoderOnlyOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of decoder-only generation models using greedy search.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.GreedySearchEncoderDecoderOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">GreedySearchEncoderDecoderOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L90" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. <code>(max_length-1,)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape
<code>(batch_size, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.GreedySearchEncoderDecoderOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.GreedySearchEncoderDecoderOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_flax_utils.FlaxGreedySearchOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_flax_utils.</span><span class="font-semibold">FlaxGreedySearchOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_flax_utils.FlaxGreedySearchOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_flax_utils.FlaxGreedySearchOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_utils.py#L45" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: ndarray = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxGreedySearchOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxGreedySearchOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, max_length)</code>) —
The generated sequences.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Flax Base class for outputs of decoder-only generation models using greedy search.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h3 class="relative group"><a id="transformers.generation_utils.SampleDecoderOnlyOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleDecoderOnlyOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>SampleOutput
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.SampleDecoderOnlyOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">SampleDecoderOnlyOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.SampleDecoderOnlyOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.SampleDecoderOnlyOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L132" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleDecoderOnlyOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleDecoderOnlyOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleDecoderOnlyOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleDecoderOnlyOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. <code>(max_length-input_ids.shape[-1],)</code>-shaped tuple of <code>torch.FloatTensor</code> with each
tensor of shape <code>(batch_size*num_return_sequences, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleDecoderOnlyOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleDecoderOnlyOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(num_return_sequences*batch_size, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleDecoderOnlyOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleDecoderOnlyOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(num_return_sequences*batch_size, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of decoder-only generation models using sampling.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.SampleEncoderDecoderOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">SampleEncoderDecoderOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.SampleEncoderDecoderOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.SampleEncoderDecoderOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L161" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleEncoderDecoderOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleEncoderDecoderOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleEncoderDecoderOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleEncoderDecoderOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. <code>(max_length-1,)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape
<code>(batch_size*num_return_sequences, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleEncoderDecoderOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleEncoderDecoderOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape
<code>(batch_size*num_return_sequences, num_heads, sequence_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleEncoderDecoderOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleEncoderDecoderOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size*num_return_sequences, sequence_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleEncoderDecoderOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleEncoderDecoderOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleEncoderDecoderOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleEncoderDecoderOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.SampleEncoderDecoderOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.SampleEncoderDecoderOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_flax_utils.FlaxSampleOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_flax_utils.</span><span class="font-semibold">FlaxSampleOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_flax_utils.FlaxSampleOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_flax_utils.FlaxSampleOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_utils.py#L59" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: ndarray = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_flax_utils.FlaxSampleOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_flax_utils.FlaxSampleOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, max_length)</code>) —
The generated sequences.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Flax Base class for outputs of decoder-only generation models using sampling.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="None"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>replace</span></h4><!-- HTML_TAG_END -->
<a id="None" class="header-link invisible with-hover:group-hover:visible pr-2" href="#None"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/flax/struct.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**updates<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>“Returns a new object replacing the specified fields with new values.</p></div></div>
<h3 class="relative group"><a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BeamSearchOutput
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.BeamSearchDecoderOnlyOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">BeamSearchDecoderOnlyOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L204" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences_scores<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_indices<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput.sequences_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-input_ids.shape[-1],)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape
<code>(batch_size*num_beams*num_return_sequences, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput.beam_indices" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput.beam_indices"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-input_ids.shape[-1],)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchDecoderOnlyOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchDecoderOnlyOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of decoder-only generation models using beam search.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.BeamSearchEncoderDecoderOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">BeamSearchEncoderDecoderOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L239" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences_scores<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_indices<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.sequences_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size*num_return_sequences)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-1,)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape <code>(batch_size*num_beams, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.beam_indices" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.beam_indices"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-1,)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSearchEncoderDecoderOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)</p></div>
<h3 class="relative group"><a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BeamSampleOutput
</span></h3>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.BeamSampleDecoderOnlyOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">BeamSampleDecoderOnlyOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L290" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences_scores<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_indices<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_return_sequences, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput.sequences_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_return_sequence)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-input_ids.shape[-1],)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape
<code>(batch_size*num_beams*num_return_sequences, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput.beam_indices" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput.beam_indices"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-input_ids.shape[-1],)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput.attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput.attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleDecoderOnlyOutput.hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleDecoderOnlyOutput.hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of decoder-only generation models using beam sample.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.generation_utils.BeamSampleEncoderDecoderOutput"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.generation_utils.</span><span class="font-semibold">BeamSampleEncoderDecoderOutput</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L325" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: LongTensor = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences_scores<span class="opacity-60">: typing.Optional[torch.FloatTensor] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_indices<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.LongTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[torch.FloatTensor]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cross_attentions<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">decoder_hidden_states<span class="opacity-60">: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size*num_beams, sequence_length)</code>) —
The generated sequences. The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter
if all batches finished early due to the <code>eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.sequences_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_return_sequence)</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Final beam scores of the generated <code>sequences</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tuple(torch.FloatTensor)</code> <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
<code>(max_length-1,)</code>-shaped tuple of <code>torch.FloatTensor</code> with each tensor of shape <code>(batch_size*num_beams, config.vocab_size)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.beam_indices" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.beam_indices"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>beam_indices</strong> (<code>tuple(tuple(torch.LongTensor))</code>, <em>optional</em>, returned when <code>output_scores=True</code> is passed or when <code>config.output_scores=True</code>) —
Beam indices of generated token id at each generation step. <code>(batch_size*num_return_sequences)</code>-shaped
tuple of <code>(max_length-1,)</code>-shaped tuples of scalar <code>torch.LongTensor</code> tensors.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for each layer of the decoder) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.encoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoder_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each layer) of
shape <code>(batch_size*num_beams, sequence_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.cross_attentions" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.cross_attentions"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cross_attentions</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or <code>config.output_attentions=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size, num_heads, generated_length, sequence_length)</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_hidden_states" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.generation_utils.BeamSampleEncoderDecoderOutput.decoder_hidden_states"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>decoder_hidden_states</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) —
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
<code>torch.FloatTensor</code> of shape <code>(batch_size*num_beams, generated_length, hidden_size)</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)</p></div>
<h2 class="relative group"><a id="transformers.LogitsProcessor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.LogitsProcessor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>LogitsProcessor
</span></h2>
<p>A <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> can be used to modify the prediction scores of a language model head for
generation.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.LogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">LogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.LogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.LogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L51" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all logit processors that can be applied during generation.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.LogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.LogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.LogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L54" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.LogitsProcessor.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.LogitsProcessor.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.LogitsProcessor.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.LogitsProcessor.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.LogitsProcessor.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Torch method for processing logits.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.LogitsProcessorList"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">LogitsProcessorList</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.LogitsProcessorList" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.LogitsProcessorList"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L73" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">iterable<span class="opacity-60"> = ()</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>This class can be used to create a list of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> or <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> to subsequently process a
<code>scores</code> input tensor. This class inherits from list and adds a specific <em><strong>call</strong></em> method to apply each
<a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> or <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> to the inputs.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.LogitsProcessorList.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.LogitsProcessorList.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.LogitsProcessorList.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L80" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.LogitsProcessorList.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.LogitsProcessorList.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.LogitsProcessorList.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.LogitsProcessorList.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.LogitsProcessorList.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.LogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">LogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.LogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.LogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L62" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.LogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.LogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.LogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L65" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.LogitsWarper.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.LogitsWarper.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.LogitsWarper.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.LogitsWarper.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.LogitsWarper.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Torch method for warping logits.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.MinLengthLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">MinLengthLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.MinLengthLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.MinLengthLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L96" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MinLengthLogitsProcessor.min_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MinLengthLogitsProcessor.min_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_length</strong> (<code>int</code>) —
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float("Inf")</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MinLengthLogitsProcessor.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MinLengthLogitsProcessor.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> enforcing a min-length by setting EOS probability to 0.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.MinLengthLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.MinLengthLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.MinLengthLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L117" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TemperatureLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TemperatureLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TemperatureLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TemperatureLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L124" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">temperature<span class="opacity-60">: float</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TemperatureLogitsWarper.temperature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TemperatureLogitsWarper.temperature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>temperature</strong> (<code>float</code>) —
The value used to module the logits distribution.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> for temperature (exponential scaling output probability distribution).</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TemperatureLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TemperatureLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TemperatureLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L139" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.RepetitionPenaltyLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">RepetitionPenaltyLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.RepetitionPenaltyLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.RepetitionPenaltyLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L144" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">penalty<span class="opacity-60">: float</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.RepetitionPenaltyLogitsProcessor.repetition_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.RepetitionPenaltyLogitsProcessor.repetition_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repetition_penalty</strong> (<code>float</code>) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> enforcing an exponential penalty on repeated sequences.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.RepetitionPenaltyLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.RepetitionPenaltyLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.RepetitionPenaltyLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L160" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TopPLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TopPLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TopPLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TopPLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L170" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60">: float = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TopPLogitsWarper.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TopPLogitsWarper.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>) —
If set to < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher are kept
for generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TopPLogitsWarper.filter_value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TopPLogitsWarper.filter_value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TopPLogitsWarper.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TopPLogitsWarper.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TopPLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TopPLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TopPLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L193" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TopKLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TopKLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TopKLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TopKLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L212" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60">: float = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TopKLogitsWarper.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TopKLogitsWarper.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TopKLogitsWarper.filter_value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TopKLogitsWarper.filter_value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TopKLogitsWarper.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TopKLogitsWarper.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsWarper">LogitsWarper</a> that performs top-k, i.e. restricting to the k highest probability elements.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TopKLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TopKLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TopKLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L233" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.NoRepeatNGramLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">NoRepeatNGramLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.NoRepeatNGramLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.NoRepeatNGramLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L309" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ngram_size<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.NoRepeatNGramLogitsProcessor.ngram_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.NoRepeatNGramLogitsProcessor.ngram_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ngram_size</strong> (<code>int</code>) —
All ngrams of size <code>ngram_size</code> can only occur once.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> that enforces no repetition of n-grams. See
<a href="https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345" rel="nofollow">Fairseq</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.NoRepeatNGramLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.NoRepeatNGramLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.NoRepeatNGramLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L324" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.NoBadWordsLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">NoBadWordsLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.NoBadWordsLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.NoBadWordsLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L376" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bad_words_ids<span class="opacity-60">: typing.List[typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.NoBadWordsLogitsProcessor.bad_words_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.NoBadWordsLogitsProcessor.bad_words_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bad_words_ids</strong> (<code>List[List[int]]</code>) —
List of list of token ids that are not allowed to be generated. In order to get the token ids of the words
that should not appear in the generated text, use <code>tokenizer(bad_words, add_prefix_space=True, add_special_tokens=False).input_ids</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.NoBadWordsLogitsProcessor.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.NoBadWordsLogitsProcessor.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> that enforces that specified sequences will never be sampled.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.NoBadWordsLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.NoBadWordsLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.NoBadWordsLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L418" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PrefixConstrainedLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PrefixConstrainedLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PrefixConstrainedLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PrefixConstrainedLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L504" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prefix_allowed_tokens_fn<span class="opacity-60">: typing.Callable[[int, torch.Tensor], typing.List[int]]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> that enforces constrained generation and is useful for prefix-conditioned constrained
generation. See <a href="https://arxiv.org/abs/2010.00904" rel="nofollow">Autoregressive Entity Retrieval</a> for more information.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PrefixConstrainedLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PrefixConstrainedLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PrefixConstrainedLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L521" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.HammingDiversityLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">HammingDiversityLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.HammingDiversityLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.HammingDiversityLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L530" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">diversity_penalty<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beam_groups<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.HammingDiversityLogitsProcessor.diversity_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.HammingDiversityLogitsProcessor.diversity_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>diversity_penalty</strong> (<code>float</code>) —
This value is subtracted from a beam’s score if it generates a token same as any beam from other group at a
particular time. Note that <code>diversity_penalty</code> is only effective if <code>group beam search</code> is enabled.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.HammingDiversityLogitsProcessor.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.HammingDiversityLogitsProcessor.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>) —
Number of beams used for group beam search. See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more
details.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.HammingDiversityLogitsProcessor.num_beam_groups" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.HammingDiversityLogitsProcessor.num_beam_groups"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beam_groups</strong> (<code>int</code>) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> that enforces diverse beam search. Note that this logits processor is only effective for
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.group_beam_search">PreTrainedModel.group_beam_search()</a>. See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence
Models</a> for more details.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.HammingDiversityLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.HammingDiversityLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.HammingDiversityLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L561" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">current_tokens<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">beam_group_idx<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ForcedBOSTokenLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ForcedBOSTokenLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ForcedBOSTokenLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ForcedBOSTokenLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L590" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ForcedBOSTokenLogitsProcessor.bos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ForcedBOSTokenLogitsProcessor.bos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token_id</strong> (<code>int</code>) —
The id of the token to force as the first generated token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> that enforces the specified token as the first generated token.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ForcedBOSTokenLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ForcedBOSTokenLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ForcedBOSTokenLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L602" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ForcedEOSTokenLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ForcedEOSTokenLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ForcedEOSTokenLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ForcedEOSTokenLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L611" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ForcedEOSTokenLogitsProcessor.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ForcedEOSTokenLogitsProcessor.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ForcedEOSTokenLogitsProcessor.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ForcedEOSTokenLogitsProcessor.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> that enforces the specified token as the last generated token when <code>max_length</code> is reached.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ForcedEOSTokenLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ForcedEOSTokenLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ForcedEOSTokenLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L626" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.InfNanRemoveLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">InfNanRemoveLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.InfNanRemoveLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.InfNanRemoveLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L635" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.LogitsProcessor">LogitsProcessor</a> that removes all <code>nan</code> and <code>inf</code> values to avoid the generation method to fail. Note that using
the logits processor should only be used if necessary since it can slow down the generation method. <code>max_length</code> is
reached.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.InfNanRemoveLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.InfNanRemoveLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.InfNanRemoveLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_logits_process.py#L642" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L50" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all logit processors that can be applied during generation.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L53" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFLogitsProcessor.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFLogitsProcessor.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFLogitsProcessor.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFLogitsProcessor.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFLogitsProcessor.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>TF method for processing logits.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFLogitsProcessorList"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFLogitsProcessorList</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFLogitsProcessorList" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFLogitsProcessorList"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L72" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">iterable<span class="opacity-60"> = ()</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>This class can be used to create a list of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsProcessor">TFLogitsProcessor</a> to subsequently process a <code>scores</code> input tensor.
This class inherits from list and adds a specific <em><strong>call</strong></em> method to apply each <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsProcessor">TFLogitsProcessor</a> to the
inputs.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFLogitsProcessorList.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFLogitsProcessorList.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFLogitsProcessorList.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L79" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFLogitsProcessorList.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFLogitsProcessorList.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFLogitsProcessorList.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFLogitsProcessorList.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFLogitsProcessorList.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L61" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L64" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFLogitsWarper.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFLogitsWarper.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFLogitsWarper.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFLogitsWarper.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.TFLogitsWarper.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>tf.Tensor</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>TF method for warping logits.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFTemperatureLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFTemperatureLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFTemperatureLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFTemperatureLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L95" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">temperature<span class="opacity-60">: float</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFTemperatureLogitsWarper.temperature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFTemperatureLogitsWarper.temperature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>temperature</strong> (<code>float</code>) —
The value used to module the logits distribution.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsWarper">TFLogitsWarper</a> for temperature (exponential scaling output probability distribution).</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFTemperatureLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFTemperatureLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFTemperatureLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L110" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFTopPLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFTopPLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFTopPLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFTopPLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L144" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60">: float = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFTopPLogitsWarper.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFTopPLogitsWarper.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>) —
If set to < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher are kept
for generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFTopPLogitsWarper.filter_value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFTopPLogitsWarper.filter_value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFTopPLogitsWarper.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFTopPLogitsWarper.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsWarper">TFLogitsWarper</a> that performs top-p, i.e. restricting to top tokens summing to <= prob_cut_off.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFTopPLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFTopPLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFTopPLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L166" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFTopKLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFTopKLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFTopKLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFTopKLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L115" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60">: float = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFTopKLogitsWarper.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFTopKLogitsWarper.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFTopKLogitsWarper.filter_value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFTopKLogitsWarper.filter_value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFTopKLogitsWarper.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFTopKLogitsWarper.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsWarper">TFLogitsWarper</a> that performs top-k, i.e. restricting to the k highest probability elements.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFTopKLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFTopKLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFTopKLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L136" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFMinLengthLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFMinLengthLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFMinLengthLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFMinLengthLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L198" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFMinLengthLogitsProcessor.min_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFMinLengthLogitsProcessor.min_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_length</strong> (<code>int</code>) —
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float("Inf")</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFMinLengthLogitsProcessor.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFMinLengthLogitsProcessor.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsProcessor">TFLogitsProcessor</a> enforcing a min-length by setting EOS probability to 0.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFMinLengthLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFMinLengthLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFMinLengthLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L219" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFNoBadWordsLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFNoBadWordsLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFNoBadWordsLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFNoBadWordsLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L271" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bad_words_ids<span class="opacity-60">: typing.List[typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFNoBadWordsLogitsProcessor.bad_words_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFNoBadWordsLogitsProcessor.bad_words_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bad_words_ids</strong> (<code>List[List[int]]</code>) —
List of list of token ids that are not allowed to be generated. In order to get the tokens of the words
that should not appear in the generated text, use <code>tokenizer(bad_word, add_prefix_space=True).input_ids</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFNoBadWordsLogitsProcessor.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFNoBadWordsLogitsProcessor.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsProcessor">TFLogitsProcessor</a> that enforces that specified sequences will never be sampled.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFNoBadWordsLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFNoBadWordsLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFNoBadWordsLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L334" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFNoRepeatNGramLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFNoRepeatNGramLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFNoRepeatNGramLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFNoRepeatNGramLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L354" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ngram_size<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFNoRepeatNGramLogitsProcessor.ngram_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFNoRepeatNGramLogitsProcessor.ngram_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ngram_size</strong> (<code>int</code>) —
All ngrams of size <code>ngram_size</code> can only occur once.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsProcessor">TFLogitsProcessor</a> that enforces no repetition of n-grams. See
<a href="https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345" rel="nofollow">Fairseq</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFNoRepeatNGramLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFNoRepeatNGramLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFNoRepeatNGramLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L392" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFRepetitionPenaltyLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TFRepetitionPenaltyLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TFRepetitionPenaltyLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFRepetitionPenaltyLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L233" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">penalty<span class="opacity-60">: float</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TFRepetitionPenaltyLogitsProcessor.repetition_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TFRepetitionPenaltyLogitsProcessor.repetition_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repetition_penalty</strong> (<code>float</code>) —
The parameter for repetition penalty. 1.0 means no penalty. See <a href="https://arxiv.org/pdf/1909.05858.pdf" rel="nofollow">this
paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.TFLogitsProcessor">TFLogitsProcessor</a> enforcing an exponential penalty on repeated sequences.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TFRepetitionPenaltyLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.TFRepetitionPenaltyLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TFRepetitionPenaltyLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_logits_process.py#L262" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: Tensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: Tensor</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L50" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all logit processors that can be applied during generation.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L53" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxLogitsProcessor.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxLogitsProcessor.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxLogitsProcessor.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxLogitsProcessor.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.FlaxLogitsProcessor.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Flax method for processing logits.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxLogitsProcessorList"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxLogitsProcessorList</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxLogitsProcessorList" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxLogitsProcessorList"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L72" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">iterable<span class="opacity-60"> = ()</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>This class can be used to create a list of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsProcessor">FlaxLogitsProcessor</a> or <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsWarper">FlaxLogitsWarper</a> to subsequently process
a <code>scores</code> input tensor. This class inherits from list and adds a specific <em><strong>call</strong></em> method to apply each
<a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsProcessor">FlaxLogitsProcessor</a> or <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsWarper">FlaxLogitsWarper</a> to the inputs.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxLogitsProcessorList.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxLogitsProcessorList.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxLogitsProcessorList.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L79" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cur_len<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxLogitsProcessorList.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxLogitsProcessorList.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxLogitsProcessorList.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxLogitsProcessorList.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.FlaxLogitsProcessorList.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L61" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all logit warpers that can be applied during generation with multinomial sampling.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L64" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxLogitsWarper.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxLogitsWarper.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxLogitsWarper.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxLogitsWarper.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs —
Additional logits processor specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.FlaxLogitsWarper.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>jnp.ndarray</code> of shape <code>(batch_size, config.vocab_size)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The processed prediction scores.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Flax method for warping logits.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxTemperatureLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxTemperatureLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxTemperatureLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxTemperatureLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L95" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">temperature<span class="opacity-60">: float</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxTemperatureLogitsWarper.temperature" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxTemperatureLogitsWarper.temperature"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>temperature</strong> (<code>float</code>) —
The value used to module the logits distribution.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsWarper">FlaxLogitsWarper</a> for temperature (exponential scaling output probability distribution).</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxTemperatureLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxTemperatureLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxTemperatureLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L110" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cur_len<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxTopPLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxTopPLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxTopPLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxTopPLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L115" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60">: float = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxTopPLogitsWarper.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxTopPLogitsWarper.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>) —
If set to < 1, only the most probable tokens with probabilities that add up to <code>top_p</code> or higher are kept
for generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxTopPLogitsWarper.filter_value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxTopPLogitsWarper.filter_value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxTopPLogitsWarper.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxTopPLogitsWarper.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsWarper">FlaxLogitsWarper</a> that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxTopPLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxTopPLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxTopPLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L137" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cur_len<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxTopKLogitsWarper"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxTopKLogitsWarper</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxTopKLogitsWarper" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxTopKLogitsWarper"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L156" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60">: float = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxTopKLogitsWarper.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxTopKLogitsWarper.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>) —
The number of highest probability vocabulary tokens to keep for top-k-filtering.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxTopKLogitsWarper.filter_value" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxTopKLogitsWarper.filter_value"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>filter_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>-float("Inf")</code>) —
All filtered values will be set to this float value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxTopKLogitsWarper.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxTopKLogitsWarper.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimum number of tokens that cannot be filtered.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsWarper">FlaxLogitsWarper</a> that performs top-k, i.e. restricting to the k highest probability elements.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxTopKLogitsWarper.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxTopKLogitsWarper.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxTopKLogitsWarper.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L177" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cur_len<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxForcedBOSTokenLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxForcedBOSTokenLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxForcedBOSTokenLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxForcedBOSTokenLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L192" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">bos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxForcedBOSTokenLogitsProcessor.bos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxForcedBOSTokenLogitsProcessor.bos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token_id</strong> (<code>int</code>) —
The id of the token to force as the first generated token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsProcessor">FlaxLogitsProcessor</a> that enforces the specified token as the first generated token.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxForcedBOSTokenLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxForcedBOSTokenLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxForcedBOSTokenLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L204" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cur_len<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxForcedEOSTokenLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxForcedEOSTokenLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxForcedEOSTokenLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxForcedEOSTokenLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L216" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxForcedEOSTokenLogitsProcessor.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxForcedEOSTokenLogitsProcessor.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxForcedEOSTokenLogitsProcessor.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxForcedEOSTokenLogitsProcessor.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>) —
The id of the token to force as the last generated token when <code>max_length</code> is reached.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsProcessor">FlaxLogitsProcessor</a> that enforces the specified token as the last generated token when <code>max_length</code> is reached.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxForcedEOSTokenLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxForcedEOSTokenLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxForcedEOSTokenLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L231" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cur_len<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxMinLengthLogitsProcessor"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">FlaxMinLengthLogitsProcessor</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.FlaxMinLengthLogitsProcessor" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxMinLengthLogitsProcessor"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L243" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxMinLengthLogitsProcessor.min_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxMinLengthLogitsProcessor.min_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_length</strong> (<code>int</code>) —
The minimum length below which the score of <code>eos_token_id</code> is set to <code>-float("Inf")</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.FlaxMinLengthLogitsProcessor.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.FlaxMinLengthLogitsProcessor.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.FlaxLogitsProcessor">FlaxLogitsProcessor</a> enforcing a min-length by setting EOS probability to 0.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.FlaxMinLengthLogitsProcessor.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.FlaxMinLengthLogitsProcessor.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.FlaxMinLengthLogitsProcessor.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_flax_logits_process.py#L264" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: ndarray</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">cur_len<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<h2 class="relative group"><a id="transformers.StoppingCriteria" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.StoppingCriteria"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>StoppingCriteria
</span></h2>
<p>A <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.StoppingCriteria">StoppingCriteria</a> can be used to change when to stop generation (other than EOS token).</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.StoppingCriteria"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">StoppingCriteria</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.StoppingCriteria" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.StoppingCriteria"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L33" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all stopping criteria that can be applied during generation.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.StoppingCriteria.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.StoppingCriteria.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.StoppingCriteria.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L36" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.StoppingCriteria.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.StoppingCriteria.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.StoppingCriteria.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.StoppingCriteria.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.StoppingCriteriaList"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">StoppingCriteriaList</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.StoppingCriteriaList" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.StoppingCriteriaList"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L110" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">iterable<span class="opacity-60"> = ()</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.StoppingCriteriaList.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.StoppingCriteriaList.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.StoppingCriteriaList.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L111" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.StoppingCriteriaList.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.StoppingCriteriaList.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.StoppingCriteriaList.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.StoppingCriteriaList.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.MaxLengthCriteria"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">MaxLengthCriteria</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.MaxLengthCriteria" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.MaxLengthCriteria"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L41" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MaxLengthCriteria.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MaxLengthCriteria.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>) —
The maximum length that the output sequence can have in number of tokens.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>This class can be used to stop generation whenever the full generated number of tokens exceeds <code>max_length</code>. Keep
in mind for decoder-only type of transformers, this will include the initial prompted tokens.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.MaxLengthCriteria.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.MaxLengthCriteria.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.MaxLengthCriteria.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L54" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MaxLengthCriteria.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MaxLengthCriteria.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MaxLengthCriteria.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MaxLengthCriteria.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.MaxTimeCriteria"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">MaxTimeCriteria</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.MaxTimeCriteria" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.MaxTimeCriteria"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L88" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_time<span class="opacity-60">: float</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">initial_timestamp<span class="opacity-60">: typing.Optional[float] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MaxTimeCriteria.max_time" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MaxTimeCriteria.max_time"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_time</strong> (<code>float</code>) —
The maximum allowed time in seconds for the generation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MaxTimeCriteria.initial_time" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MaxTimeCriteria.initial_time"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>initial_time</strong> (<code>float</code>, <em>optional</em>, defaults to <code>time.time()</code>) —
The start of the generation allowed time.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>This class can be used to stop generation whenever the full generation exceeds some amount of time. By default, the
time will start being counted when you initialize this function. You can override this by passing an
<code>initial_time</code>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.MaxTimeCriteria.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.MaxTimeCriteria.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.MaxTimeCriteria.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_stopping_criteria.py#L105" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MaxTimeCriteria.__call__.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MaxTimeCriteria.__call__.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a>. See <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.MaxTimeCriteria.__call__.scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.MaxTimeCriteria.__call__.scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.vocab_size)</code>) —
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs —
Additional stopping criteria specific kwargs.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div></div></div>
<h2 class="relative group"><a id="transformers.Constraint" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.Constraint"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Constraints
</span></h2>
<p>A <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.Constraint">Constraint</a> can be used to force the generation to include specific tokens or sequences in the output.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">Constraint</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.Constraint" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L5" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.</p>
<p>All classes that inherit Constraint must follow the requirement that</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START -->completed = <span class="hljs-literal">False</span>
<span class="hljs-keyword">while</span> <span class="hljs-keyword">not</span> completed:
_, completed = constraint.update(constraint.advance())<!-- HTML_TAG_END --></pre></div>
<p>will always terminate (halt).</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint.advance"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>advance</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Constraint.advance" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint.advance"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L48" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>token_ids(<code>torch.tensor</code>)</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Constraint.advance.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>token_ids(<code>torch.tensor</code>)</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Must be a tensor of a list of indexable tokens, not some integer.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>When called, returns the token that would take this constraint one step closer to being fulfilled.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint.copy"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>copy</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Constraint.copy" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint.copy"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L113" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stateful<span class="opacity-60"> = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>constraint(<code>Constraint</code>)</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Constraint.copy.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>constraint(<code>Constraint</code>)</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The same constraint as the one being called from.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Creates a new instance of this constraint.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint.does_advance"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>does_advance</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Constraint.does_advance" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint.does_advance"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L60" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Reads in a token and returns whether it creates progress.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint.remaining"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>remaining</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Constraint.remaining" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint.remaining"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L104" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Returns the number of remaining steps of <code>advance()</code> in order to complete this constraint.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint.reset"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>reset</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Constraint.reset" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint.reset"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L94" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Resets the state of this constraint to its initialization. We would call this in cases where the fulfillment of
a constraint is abrupted by an unwanted token.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint.test"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>test</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Constraint.test" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint.test"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L24" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Tests whether this constraint has been properly defined.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.Constraint.update"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>update</span></h4><!-- HTML_TAG_END -->
<a id="transformers.Constraint.update" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.Constraint.update"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L69" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_id<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>stepped(<code>bool</code>)</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.Constraint.update.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>stepped(<code>bool</code>)</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Whether this constraint has become one step closer to being fulfuilled.
completed(<code>bool</code>):
Whether this constraint has been completely fulfilled by this token being generated.
reset (<code>bool</code>):
Whether this constraint has reset its progress by this token being generated.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Reads in a token and returns booleans that indicate the progress made by it. This function will update the
state of this object unlikes <code>does_advance(self, token_id: int)</code>.</p>
<p>This isn’t to test whether a certain token will advance the progress; it’s to update its state as if it has
been generated. This becomes important if token_id != desired token (refer to else statement in
PhrasalConstraint)</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PhrasalConstraint"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PhrasalConstraint</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PhrasalConstraint" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PhrasalConstraint"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L129" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids<span class="opacity-60">: typing.List[int]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PhrasalConstraint.token_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PhrasalConstraint.token_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids</strong> (<code>List[int]</code>) —
The id of the token that must be generated by the output.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.Constraint">Constraint</a> enforcing that an ordered sequence of tokens is included in the output.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.DisjunctiveConstraint"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">DisjunctiveConstraint</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.DisjunctiveConstraint" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.DisjunctiveConstraint"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L260" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">nested_token_ids<span class="opacity-60">: typing.List[typing.List[int]]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DisjunctiveConstraint.nested_token_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DisjunctiveConstraint.nested_token_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>nested_token_ids</strong> (<code>List[List[int]]</code>) — a list of words, where each word is a list of ids. This constraint<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.DisjunctiveConstraint.is" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.DisjunctiveConstraint.is"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is</strong> fulfilled by generating just one from the list of words. —<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A special <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.Constraint">Constraint</a> that is fulfilled by fulfilling just one of several constraints.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConstraintListState"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ConstraintListState</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ConstraintListState" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConstraintListState"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L349" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">constraints<span class="opacity-60">: typing.List[transformers.generation_beam_constraints.Constraint]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstraintListState.constraints" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstraintListState.constraints"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>constraints</strong> (<code>List[Constraint]</code>) —
A list of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.Constraint">Constraint</a> objects that must be fulfilled by the beam scorer.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A class for beam scorers to track its progress through a list of constraints.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConstraintListState.advance"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>advance</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ConstraintListState.advance" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConstraintListState.advance"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L381" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>The list of tokens to generate such that we can make progress.
By “list” we don’t mean the list of token that will fully fulfill a constraint.</p>
<p>Given constraints <code>c_i = {t_ij | j == # of tokens}</code>, If we’re not in the middle of progressing through a
specific constraint <code>c_i</code>, we return:</p>
<p><code>[t_k1 for k in indices of unfulfilled constraints]</code></p>
<p>If we are in the middle of a constraint, then we return:
<code>[t_ij]</code>, where <code>i</code> is the index of the inprogress constraint, <code>j</code> is the next step for the constraint.</p>
<p>Though we don’t care which constraint is fulfilled first, if we are in the progress of fulfilling a constraint,
that’s the only one we’ll return.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConstraintListState.reset"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>reset</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ConstraintListState.reset" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConstraintListState.reset"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_constraints.py#L416" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids<span class="opacity-60">: typing.Optional[typing.List[int]]</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>token_ids: the tokens generated thus far to reset the state of the progress through constraints.</p></div></div>
<h2 class="relative group"><a id="transformers.BeamScorer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BeamSearch
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BeamScorer"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">BeamScorer</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.BeamScorer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BeamScorer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L88" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Abstract base class for all beam scorers that are used for <a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_search">beam_search()</a> and
<a href="/docs/transformers/pr_16143/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.beam_sample">beam_sample()</a>.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BeamScorer.process"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>process</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BeamScorer.process" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BeamScorer.process"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L94" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_tokens<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_indices<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>UserDict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.process.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.process.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.process.next_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.process.next_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>next_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Current scores of the top <code>2 * num_beams</code> non-finished beam hypotheses.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.process.next_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.process.next_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>next_tokens</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
<code>input_ids</code> of the tokens corresponding to the top <code>2 * num_beams</code> non-finished beam hypotheses.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.process.next_indices" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.process.next_indices"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>next_indices</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Beam indices indicating to which beam hypothesis the <code>next_tokens</code> correspond.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.process.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.process.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.process.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.process.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BeamScorer.process.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>UserDict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A dictionary composed of the fields as defined above:</p>
<ul>
<li><strong>next_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) — Updated scores of all
non-finished beams.</li>
<li><strong>next_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) — Next tokens to be added
to the non-finished beam_hypotheses.</li>
<li><strong>next_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) — Beam indices
indicating to which beam the next tokens shall be added.</li>
</ul>
<!-- HTML_TAG_END --></p></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BeamScorer.finalize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>finalize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BeamScorer.finalize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BeamScorer.finalize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L106" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_tokens<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_indices<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>torch.LongTensor</code> of shape <code>(batch_size * num_return_sequences, sequence_length)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.finalize.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.finalize.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.finalize.final_beam_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.finalize.final_beam_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>final_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) —
The final scores of all non-finished beams.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.finalize.final_beam_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.finalize.final_beam_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>final_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) —
The last tokens to be added to the non-finished beam_hypotheses.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.finalize.final_beam_indices" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.finalize.final_beam_indices"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>final_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) —
The beam indices indicating to which beam the <code>final_beam_tokens</code> shall be added.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.finalize.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.finalize.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamScorer.finalize.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamScorer.finalize.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.BeamScorer.finalize.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>torch.LongTensor</code> of shape <code>(batch_size * num_return_sequences, sequence_length)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The generated sequences.
The second dimension (sequence_length) is either equal to <code>max_length</code> or shorter if all batches finished early
due to the <code>eos_token_id</code>.</p>
<!-- HTML_TAG_END --></p></div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BeamSearchScorer"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">BeamSearchScorer</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.BeamSearchScorer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BeamSearchScorer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L120" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_size<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: device</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">length_penalty<span class="opacity-60">: typing.Optional[float] = 1.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_early_stopping<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beam_hyps_to_keep<span class="opacity-60">: typing.Optional[int] = 1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beam_groups<span class="opacity-60">: typing.Optional[int] = 1</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>) —
Batch Size of <code>input_ids</code> for which standard beam search decoding is run in parallel.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>) —
Number of beams for beam search.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>torch.device</code>) —
Defines the device type (<em>e.g.</em>, <code>"cpu"</code> or <code>"cuda"</code>) on which this instance of <code>BeamSearchScorer</code> will be
allocated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.length_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.length_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the
model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer
sequences.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.do_early_stopping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.do_early_stopping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.num_beam_hyps_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.num_beam_hyps_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beam_hyps_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The number of beam hypotheses that shall be returned upon calling
<code>finalize</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.BeamSearchScorer.num_beam_groups" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.BeamSearchScorer.num_beam_groups"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beam_groups</strong> (<code>int</code>) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> implementing standard beam search decoding.</p>
<p>Adapted in part from <a href="https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529" rel="nofollow">Facebook’s XLM beam search
code</a>.</p>
<p>Reference for the diverse beam search algorithm and implementation <a href="https://github.com/ashwinkalyan/dbs/blob/master/dbs/beam_utils.lua" rel="nofollow">Ashwin Kalyan’s DBS
implementation</a></p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BeamSearchScorer.process"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>process</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BeamSearchScorer.process" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BeamSearchScorer.process"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L206" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_tokens<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_indices<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.BeamSearchScorer.finalize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>finalize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.BeamSearchScorer.finalize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.BeamSearchScorer.finalize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L291" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">final_beam_scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">final_beam_tokens<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">final_beam_indices<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConstrainedBeamSearchScorer"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">ConstrainedBeamSearchScorer</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.ConstrainedBeamSearchScorer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConstrainedBeamSearchScorer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L356" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_size<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beams<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">constraints<span class="opacity-60">: typing.List[transformers.generation_beam_constraints.Constraint]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">device<span class="opacity-60">: device</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">length_penalty<span class="opacity-60">: typing.Optional[float] = 1.0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">do_early_stopping<span class="opacity-60">: typing.Optional[bool] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beam_hyps_to_keep<span class="opacity-60">: typing.Optional[int] = 1</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_beam_groups<span class="opacity-60">: typing.Optional[int] = 1</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.batch_size" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.batch_size"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_size</strong> (<code>int</code>) —
Batch Size of <code>input_ids</code> for which standard beam search decoding is run in parallel.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>) —
The maximum length of the sequence to be generated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.num_beams" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.num_beams"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beams</strong> (<code>int</code>) —
Number of beams for beam search.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.constraints" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.constraints"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>constraints</strong> (<code>List[Constraint]</code>) —
A list of positive constraints represented as <code>Constraint</code> objects that must be fulfilled in the generation
output. For more information, the documentation of <a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.Constraint">Constraint</a> should be read.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.device" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.device"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>device</strong> (<code>torch.device</code>) —
Defines the device type (<em>e.g.</em>, <code>"cpu"</code> or <code>"cuda"</code>) on which this instance of <code>BeamSearchScorer</code> will be
allocated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.length_penalty" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.length_penalty"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>length_penalty</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
Exponential penalty to the length. 1.0 means no penalty. Set to values < 1.0 in order to encourage the
model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer
sequences.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.do_early_stopping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.do_early_stopping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>do_early_stopping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether to stop the beam search when at least <code>num_beams</code> sentences are finished per batch or not.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.num_beam_hyps_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.num_beam_hyps_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beam_hyps_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
The number of beam hypotheses that shall be returned upon calling
<code>finalize</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.num_beam_groups" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.num_beam_groups"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_beam_groups</strong> (<code>int</code>) —
Number of groups to divide <code>num_beams</code> into in order to ensure diversity among different groups of beams.
See <a href="https://arxiv.org/pdf/1610.02424.pdf" rel="nofollow">this paper</a> for more details.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p><a href="/docs/transformers/pr_16143/en/internal/generation_utils#transformers.BeamScorer">BeamScorer</a> implementing constrained beam search decoding.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConstrainedBeamSearchScorer.process"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>process</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ConstrainedBeamSearchScorer.process" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConstrainedBeamSearchScorer.process"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L450" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_tokens<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">next_indices<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">scores_for_all_vocab<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>UserDict</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.process.input_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.process.input_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using any class inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a>. See
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.process.next_scores" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.process.next_scores"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>next_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Current scores of the top <code>2 * num_beams</code> non-finished beam hypotheses.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.process.next_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.process.next_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>next_tokens</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
<code>input_ids</code> of the tokens corresponding to the top <code>2 * num_beams</code> non-finished beam hypotheses.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.process.next_indices" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.process.next_indices"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>next_indices</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, 2 * num_beams)</code>) —
Beam indices indicating to which beam hypothesis the <code>next_tokens</code> correspond.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.process.scores_for_all_vocab" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.process.scores_for_all_vocab"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>scores_for_all_vocab</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams, sequence_length)</code>) —
The scores of all tokens in the vocabulary for each of the beam hypotheses.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.process.pad_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.process.pad_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>padding</em> token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.ConstrainedBeamSearchScorer.process.eos_token_id" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.ConstrainedBeamSearchScorer.process.eos_token_id"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>) —
The id of the <em>end-of-sequence</em> token.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.ConstrainedBeamSearchScorer.process.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>UserDict</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A dictionary composed of the fields as defined above:</p>
<ul>
<li>
<p><strong>next_beam_scores</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) — Updated scores of
all
non-finished beams.</p>
</li>
<li>
<p><strong>next_beam_tokens</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) — Next tokens to be
added
to the non-finished beam_hypotheses.</p>
</li>
<li>
<p><strong>next_beam_indices</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size * num_beams)</code>) — Beam indices
indicating to which beam the next tokens shall be added.</p>
</li>
</ul>
<!-- HTML_TAG_END --></p></div></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.ConstrainedBeamSearchScorer.finalize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>finalize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.ConstrainedBeamSearchScorer.finalize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.ConstrainedBeamSearchScorer.finalize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_beam_search.py#L725" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">input_ids<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">final_beam_scores<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">final_beam_tokens<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">final_beam_indices<span class="opacity-60">: LongTensor</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">eos_token_id<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div></div></div>
<h2 class="relative group"><a id="transformers.top_k_top_p_filtering" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.top_k_top_p_filtering"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Utilities
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.top_k_top_p_filtering"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.top_k_top_p_filtering</span></h4><!-- HTML_TAG_END -->
<a id="transformers.top_k_top_p_filtering" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.top_k_top_p_filtering"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_utils.py#L3290" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60">: FloatTensor</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60">: float = 1.0</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60">: float = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60">: int = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.top_k_top_p_filtering.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.top_k_top_p_filtering.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If > 0, only keep the top k tokens with highest probability (top-k filtering)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.top_k_top_p_filtering.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.top_k_top_p_filtering.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (<a href="http://arxiv.org/abs/1904.09751" rel="nofollow">http://arxiv.org/abs/1904.09751</a>)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.top_k_top_p_filtering.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.top_k_top_p_filtering.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimumber of tokens we keep per batch example in the output.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Filter a distribution of logits using top-k and/or nucleus (top-p) filtering</p>
<p>From: <a href="https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317" rel="nofollow">https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317</a></p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.tf_top_k_top_p_filtering"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>transformers.tf_top_k_top_p_filtering</span></h4><!-- HTML_TAG_END -->
<a id="transformers.tf_top_k_top_p_filtering" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.tf_top_k_top_p_filtering"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/generation_tf_utils.py#L2270" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">logits<span class="opacity-60"></span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_k<span class="opacity-60"> = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">top_p<span class="opacity-60"> = 1.0</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filter_value<span class="opacity-60"> = -inf</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">min_tokens_to_keep<span class="opacity-60"> = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.tf_top_k_top_p_filtering.top_k" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.tf_top_k_top_p_filtering.top_k"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_k</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If > 0, only keep the top k tokens with highest probability (top-k filtering)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.tf_top_k_top_p_filtering.top_p" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.tf_top_k_top_p_filtering.top_p"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>top_p</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) —
If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
filtering is described in Holtzman et al. (<a href="http://arxiv.org/abs/1904.09751" rel="nofollow">http://arxiv.org/abs/1904.09751</a>)<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.tf_top_k_top_p_filtering.min_tokens_to_keep" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.tf_top_k_top_p_filtering.min_tokens_to_keep"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>min_tokens_to_keep</strong> (<code>int</code>, <em>optional</em>, defaults to 1) —
Minimumber of tokens we keep per batch example in the output.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Filter a distribution of logits using top-k and/or nucleus (top-p) filtering</p>
<p>From: <a href="https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317" rel="nofollow">https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317</a></p></div>
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<h1 class="relative group"><a id="utilities-for-tokenizers" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#utilities-for-tokenizers"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Utilities for Tokenizers
</span></h1>
<p>This page lists all the utility functions used by the tokenizers, mainly the class
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a> that implements the common methods between
<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> and <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> and the mixin
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.SpecialTokensMixin">SpecialTokensMixin</a>.</p>
<p>Most of those are only useful if you are studying the code of the tokenizers in the library.</p>
<h2 class="relative group"><a id="transformers.PreTrainedTokenizerBase" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>PreTrainedTokenizerBase
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">PreTrainedTokenizerBase</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L1433" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.model_max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.model_max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_max_length</strong> (<code>int</code>, <em>optional</em>) —
The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is
loaded with <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a>, this will be set to the
value stored for the associated model in <code>max_model_input_sizes</code> (see above). If no value is provided, will
default to VERY_LARGE_INTEGER (<code>int(1e30)</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.padding_side" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.padding_side"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding_side</strong> (<code>str</code>, <em>optional</em>) —
The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.truncation_side" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.truncation_side"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation_side</strong> (<code>str</code>, <em>optional</em>) —
The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’].
Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.model_input_names" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.model_input_names"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>model_input_names</strong> (<code>List[string]</code>, <em>optional</em>) —
The list of inputs accepted by the forward pass of the model (like <code>"token_type_ids"</code> or
<code>"attention_mask"</code>). Default value is picked from the class attribute of the same name.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.bos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.bos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the beginning of a sentence. Will be associated to <code>self.bos_token</code> and
<code>self.bos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.eos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.eos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the end of a sentence. Will be associated to <code>self.eos_token</code> and
<code>self.eos_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.unk_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.unk_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing an out-of-vocabulary token. Will be associated to <code>self.unk_token</code> and
<code>self.unk_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.sep_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.sep_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sep_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token separating two different sentences in the same input (used by BERT for instance). Will be
associated to <code>self.sep_token</code> and <code>self.sep_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation. Will be associated to <code>self.pad_token</code> and <code>self.pad_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.cls_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.cls_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the class of the input (used by BERT for instance). Will be associated to
<code>self.cls_token</code> and <code>self.cls_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.mask_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.mask_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT). Will be associated to <code>self.mask_token</code> and <code>self.mask_token_id</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.additional_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.additional_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>additional_special_tokens</strong> (tuple or list of <code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A tuple or a list of additional special tokens. Add them here to ensure they won’t be split by the
tokenization process. Will be associated to <code>self.additional_special_tokens</code> and
<code>self.additional_special_tokens_ids</code>.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Base class for <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> and <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>.</p>
<p>Handles shared (mostly boiler plate) methods for those two classes.</p>
<p>Class attributes (overridden by derived classes)</p>
<ul><li><strong>vocab_files_names</strong> (<code>Dict[str, str]</code>) — A dictionary with, as keys, the <code>__init__</code> keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).</li>
<li><strong>pretrained_vocab_files_map</strong> (<code>Dict[str, Dict[str, str]]</code>) — A dictionary of dictionaries, with the
high-level keys being the <code>__init__</code> keyword name of each vocabulary file required by the model, the
low-level being the <code>short-cut-names</code> of the pretrained models with, as associated values, the <code>url</code> to the
associated pretrained vocabulary file.</li>
<li><strong>max_model_input_sizes</strong> (<code>Dict[str, Optional[int]]</code>) — A dictionary with, as keys, the <code>short-cut-names</code>
of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model,
or <code>None</code> if the model has no maximum input size.</li>
<li><strong>pretrained_init_configuration</strong> (<code>Dict[str, Dict[str, Any]]</code>) — A dictionary with, as keys, the
<code>short-cut-names</code> of the pretrained models, and as associated values, a dictionary of specific arguments to
pass to the <code>__init__</code> method of the tokenizer class for this pretrained model when loading the tokenizer
with the <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.from_pretrained">from_pretrained()</a> method.</li>
<li><strong>model_input_names</strong> (<code>List[str]</code>) — A list of inputs expected in the forward pass of the model.</li>
<li><strong>padding_side</strong> (<code>str</code>) — The default value for the side on which the model should have padding applied.
Should be <code>'right'</code> or <code>'left'</code>.</li>
<li><strong>truncation_side</strong> (<code>str</code>) — The default value for the side on which the model should have truncation
applied. Should be <code>'right'</code> or <code>'left'</code>.</li></ul>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.__call__"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>__call__</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.__call__" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.__call__"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2379" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[typing.List[str]]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_pair<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_split_into_words<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_token_type_ids<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_overflowing_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_special_tokens_mask<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_offsets_mapping<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.text_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.text_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_pair</strong> (<code>str</code>, <code>List[str]</code>, <code>List[List[str]]</code>) —
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
<code>is_split_into_words=True</code> (to lift the ambiguity with a batch of sequences).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_token_type_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_token_type_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_overflowing_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_overflowing_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_special_tokens_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_special_tokens_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_offsets_mapping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_offsets_mapping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.return_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.return_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.__call__.verbose" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.__call__.verbose"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.__call__.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> — List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> — List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>“token_type_ids”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>“attention_mask”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> — List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> — Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> — The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.as_target_tokenizer"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>as_target_tokenizer</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.as_target_tokenizer" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.as_target_tokenizer"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3404" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.batch_decode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>batch_decode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.batch_decode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.batch_decode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3250" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">sequences<span class="opacity-60">: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">clean_up_tokenization_spaces<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.sequences" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.sequences"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sequences</strong> (<code>Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
List of tokenized input ids. Can be obtained using the <code>__call__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to clean up the tokenization spaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_decode.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_decode.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific decode method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.batch_decode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The list of decoded sentences.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Convert a list of lists of token ids into a list of strings by calling decode.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.batch_encode_plus"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>batch_encode_plus</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.batch_encode_plus" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2600" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">batch_text_or_text_pairs<span class="opacity-60">: typing.Union[typing.List[str], typing.List[typing.Tuple[str, str]], typing.List[typing.List[str]], typing.List[typing.Tuple[typing.List[str], typing.List[str]]], typing.List[typing.List[int]], typing.List[typing.Tuple[typing.List[int], typing.List[int]]]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_split_into_words<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_token_type_ids<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_overflowing_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_special_tokens_mask<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_offsets_mapping<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.batch_text_or_text_pairs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.batch_text_or_text_pairs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>batch_text_or_text_pairs</strong> (<code>List[str]</code>, <code>List[Tuple[str, str]]</code>, <code>List[List[str]]</code>, <code>List[Tuple[List[str], List[str]]]</code>, and for not-fast tokenizers, also <code>List[List[int]]</code>, <code>List[Tuple[List[int], List[int]]]</code>) —
Batch of sequences or pair of sequences to be encoded. This can be a list of
string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see
details in <code>encode_plus</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.return_token_type_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.return_token_type_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.return_overflowing_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.return_overflowing_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.return_special_tokens_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.return_special_tokens_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.return_offsets_mapping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.return_offsets_mapping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.return_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.return_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.batch_encode_plus.verbose" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.batch_encode_plus.verbose"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.batch_encode_plus.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> — List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> — List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>“token_type_ids”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>“attention_mask”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> — List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> — Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> — The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This method is deprecated, <code>__call__</code> should be used instead.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>build_inputs_with_special_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2884" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids_0<span class="opacity-60">: typing.List[int]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids_1<span class="opacity-60">: typing.Optional[typing.List[int]] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens.token_ids_0" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens.token_ids_0"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids_0</strong> (<code>List[int]</code>) — The first tokenized sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens.token_ids_1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens.token_ids_1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) — The second tokenized sequence.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.build_inputs_with_special_tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The model input with special tokens.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens.</p>
<p>This implementation does not add special tokens and this method should be overridden in a subclass.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.clean_up_tokenization"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>clean_up_tokenization</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.clean_up_tokenization" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.clean_up_tokenization"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3359" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">out_string<span class="opacity-60">: str</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.clean_up_tokenization.out_string" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.clean_up_tokenization.out_string"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>out_string</strong> (<code>str</code>) — The text to clean up.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.clean_up_tokenization.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The cleaned-up string.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.convert_tokens_to_string"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>convert_tokens_to_string</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.convert_tokens_to_string" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.convert_tokens_to_string"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3237" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tokens<span class="opacity-60">: typing.List[str]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.convert_tokens_to_string.tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.convert_tokens_to_string.tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tokens</strong> (<code>List[str]</code>) — The token to join in a string.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.convert_tokens_to_string.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The joined tokens.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a sequence of tokens in a single string. The most simple way to do it is <code>" ".join(tokens)</code> but we
often want to remove sub-word tokenization artifacts at the same time.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>create_token_type_ids_from_sequences</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2864" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids_0<span class="opacity-60">: typing.List[int]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids_1<span class="opacity-60">: typing.Optional[typing.List[int]] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences.token_ids_0" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences.token_ids_0"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids_0</strong> (<code>List[int]</code>) — The first tokenized sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences.token_ids_1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences.token_ids_1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) — The second tokenized sequence.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.create_token_type_ids_from_sequences.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The token type ids.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Create the token type IDs corresponding to the sequences passed. <a href="../glossary#token-type-ids">What are token type
IDs?</a></p>
<p>Should be overridden in a subclass if the model has a special way of building those.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.decode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>decode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.decode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.decode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3283" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids<span class="opacity-60">: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">skip_special_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">clean_up_tokenization_spaces<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.token_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.token_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids</strong> (<code>Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]</code>) —
List of tokenized input ids. Can be obtained using the <code>__call__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.skip_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.skip_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>skip_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to remove special tokens in the decoding.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.clean_up_tokenization_spaces" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.clean_up_tokenization_spaces"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to clean up the tokenization spaces.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.decode.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.decode.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific decode method.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.decode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The decoded sentence.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.</p>
<p>Similar to doing <code>self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.encode"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>encode</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.encode" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.encode"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2189" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_pair<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[int]</code>, <code>torch.Tensor</code>, <code>tf.Tensor</code> or <code>np.ndarray</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>) —
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
<code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.text_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.text_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_pair</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>, <em>optional</em>) —
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the <code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.encode.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[int]</code>, <code>torch.Tensor</code>, <code>tf.Tensor</code> or <code>np.ndarray</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The tokenized ids of the text.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.</p>
<p>Same as doing <code>self.convert_tokens_to_ids(self.tokenize(text))</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.encode_plus"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>encode_plus</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.encode_plus" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.encode_plus"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2504" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text_pair<span class="opacity-60">: typing.Union[str, typing.List[str], typing.List[int], NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">is_split_into_words<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_token_type_ids<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_overflowing_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_special_tokens_mask<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_offsets_mapping<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code> (the latter only for not-fast tokenizers)) —
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
<code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.text_pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.text_pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text_pair</strong> (<code>str</code>, <code>List[str]</code> or <code>List[int]</code>, <em>optional</em>) —
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
the <code>tokenize</code> method) or a list of integers (tokenized string ids using the <code>convert_tokens_to_ids</code>
method).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.return_token_type_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.return_token_type_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.return_overflowing_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.return_overflowing_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.return_special_tokens_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.return_special_tokens_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.return_offsets_mapping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.return_offsets_mapping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.return_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.return_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.encode_plus.verbose" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.encode_plus.verbose"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.encode_plus.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> — List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> — List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>“token_type_ids”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>“attention_mask”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> — List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> — Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> — The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Tokenize and prepare for the model a sequence or a pair of sequences.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This method is deprecated, <code>__call__</code> should be used instead.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.from_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>from_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.from_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.from_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L1551" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pretrained_model_name_or_path<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">*init_inputs<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.pretrained_model_name_or_path" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.pretrained_model_name_or_path"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) —
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like <code>bert-base-uncased</code>, or namespaced under a
user or organization name, like <code>dbmdz/bert-base-german-cased</code>.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>(<strong>Deprecated</strong>, not applicable to all derived classes) A path or url to a single saved vocabulary
file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g.,
<code>./my_model_directory/vocab.txt</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.cache_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.cache_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) —
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the
standard cache should not be used.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.force_download" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.force_download"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to force the (re-)download the vocabulary files and override the cached versions if they
exist.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.resume_download" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.resume_download"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to delete incompletely received files. Attempt to resume the download if such a file
exists.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.proxies" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.proxies"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) —
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}</code>. The proxies are used on each request.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.local_files_only" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.local_files_only"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to only rely on local files and not to attempt to download any files.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.revision(str," class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.revision(str,"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>revision(<code>str</code>,</strong> <em>optional</em>, defaults to <code>"main"</code>) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.subfolder" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.subfolder"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>subfolder</strong> (<code>str</code>, <em>optional</em>) —
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>inputs</strong> (additional positional arguments, <em>optional</em>) —
Will be passed along to the Tokenizer <code>__init__</code> method.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.from_pretrained.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.from_pretrained.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the Tokenizer <code>__init__</code> method. Can be used to set special tokens like <code>bos_token</code>,
<code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__</code> for more details.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Instantiate a <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase">PreTrainedTokenizerBase</a> (or a derived class) from a predefined
tokenizer.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Passing <code>use_auth_token=True</code> is required when you want to use a private model.</p></div>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># We can't instantiate directly the base class *PreTrainedTokenizerBase* so let's show our examples on a derived class: BertTokenizer</span>
<span class="hljs-comment"># Download vocabulary from huggingface.co and cache.</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
<span class="hljs-comment"># Download vocabulary from huggingface.co (user-uploaded) and cache.</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"dbmdz/bert-base-german-cased"</span>)
<span class="hljs-comment"># If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"./test/saved_model/"</span>)
<span class="hljs-comment"># If the tokenizer uses a single vocabulary file, you can point directly to this file</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"./test/saved_model/my_vocab.txt"</span>)
<span class="hljs-comment"># You can link tokens to special vocabulary when instantiating</span>
tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>, unk_token=<span class="hljs-string">"<unk>"</span>)
<span class="hljs-comment"># You should be sure '<unk>' is in the vocabulary when doing that.</span>
<span class="hljs-comment"># Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)</span>
<span class="hljs-keyword">assert</span> tokenizer.unk_token == <span class="hljs-string">"<unk>"</span><!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.get_special_tokens_mask"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_special_tokens_mask</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.get_special_tokens_mask" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.get_special_tokens_mask"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3328" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids_0<span class="opacity-60">: typing.List[int]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">token_ids_1<span class="opacity-60">: typing.Optional[typing.List[int]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">already_has_special_tokens<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A list of integers in the range [0, 1]</span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.get_special_tokens_mask.token_ids_0" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.get_special_tokens_mask.token_ids_0"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids_0</strong> (<code>List[int]</code>) —
List of ids of the first sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.get_special_tokens_mask.token_ids_1" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.get_special_tokens_mask.token_ids_1"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) —
List of ids of the second sequence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.get_special_tokens_mask.already_has_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.get_special_tokens_mask.already_has_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the token list is already formatted with special tokens for the model.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.get_special_tokens_mask.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A list of integers in the range [0, 1]</p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>1 for a special token, 0 for a sequence token.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> or <code>encode_plus</code> methods.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.get_vocab"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>get_vocab</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.get_vocab" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.get_vocab"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L1539" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Dict[str, int]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.get_vocab.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Dict[str, int]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The vocabulary.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Returns the vocabulary as a dictionary of token to index.</p>
<p><code>tokenizer.get_vocab()[token]</code> is equivalent to <code>tokenizer.convert_tokens_to_ids(token)</code> when <code>token</code> is in the
vocab.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.pad"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>pad</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.pad" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.pad"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2703" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">encoded_inputs<span class="opacity-60">: typing.Union[transformers.tokenization_utils_base.BatchEncoding, typing.List[transformers.tokenization_utils_base.BatchEncoding], typing.Dict[str, typing.List[int]], typing.Dict[str, typing.List[typing.List[int]]], typing.List[typing.Dict[str, typing.List[int]]]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60">: bool = True</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad.encoded_inputs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad.encoded_inputs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>encoded_inputs</strong> (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a>, list of <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a>, <code>Dict[str, List[int]]</code>, <code>Dict[str, List[List[int]]</code> or <code>List[Dict[str, List[int]]]</code>) —
Tokenized inputs. Can represent one input (<a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a> or <code>Dict[str, List[int]]</code>) or a batch of
tokenized inputs (list of <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding">BatchEncoding</a>, <em>Dict[str, List[List[int]]]</em> or <em>List[Dict[str,
List[int]]]</em>) so you can use this method during preprocessing as well as in a PyTorch Dataloader
collate function.</p>
<p>Instead of <code>List[int]</code> you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see
the note above for the return type.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Select a strategy to pad the returned sequences (according to the model’s padding side and padding
index) among:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Maximum length of the returned list and optionally padding length (see above).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value.</p>
<p>This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability</p>
<blockquote>
<p>= 7.5 (Volta).</p>
</blockquote><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.pad.verbose" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.pad.verbose"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch.</p>
<p>Padding side (left/right) padding token ids are defined at the tokenizer level (with <code>self.padding_side</code>,
<code>self.pad_token_id</code> and <code>self.pad_token_type_id</code>)</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>If the <code>encoded_inputs</code> passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
result will use the same type unless you provide a different tensor type with <code>return_tensors</code>. In the case of
PyTorch tensors, you will lose the specific device of your tensors however.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.prepare_for_model"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>prepare_for_model</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.prepare_for_model" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.prepare_for_model"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2904" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ids<span class="opacity-60">: typing.List[int]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pair_ids<span class="opacity-60">: typing.Optional[typing.List[int]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: typing.Union[bool, str, transformers.file_utils.PaddingStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pad_to_multiple_of<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: typing.Union[str, transformers.file_utils.TensorType, NoneType] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_token_type_ids<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_attention_mask<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_overflowing_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_special_tokens_mask<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_offsets_mapping<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_length<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">prepend_batch_axis<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ids</strong> (<code>List[int]</code>) —
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the <code>tokenize</code> and
<code>convert_tokens_to_ids</code> methods.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.pair_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.pair_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pair_ids</strong> (<code>List[int]</code>, <em>optional</em>) —
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the <code>tokenize</code>
and <code>convert_tokens_to_ids</code> methods.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to encode the sequences with the special tokens relative to their model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length to use by one of the truncation/padding parameters.</p>
<p>If left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a number along with <code>max_length</code>, the overflowing tokens returned when
<code>return_overflowing_tokens=True</code> will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.is_split_into_words" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.is_split_into_words"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>is_split_into_words</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to <code>True</code>, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.pad_to_multiple_of" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.pad_to_multiple_of"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_to_multiple_of</strong> (<code>int</code>, <em>optional</em>) —
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.return_token_type_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.return_token_type_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_token_type_ids</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.return_attention_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.return_attention_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_attention_mask</strong> (<code>bool</code>, <em>optional</em>) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the <code>return_outputs</code> attribute.</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.return_overflowing_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.return_overflowing_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_overflowing_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with <code>truncation_strategy = longest_first</code> or <code>True</code>, an error is raised instead
of returning overflowing tokens.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.return_special_tokens_mask" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.return_special_tokens_mask"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_special_tokens_mask</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return special tokens mask information.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.return_offsets_mapping" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.return_offsets_mapping"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_offsets_mapping</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return <code>(char_start, char_end)</code> for each token.</p>
<p>This is only available on fast tokenizers inheriting from <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a>, if using
Python’s tokenizer, this method will raise <code>NotImplementedError</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.return_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.return_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_length</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to return the lengths of the encoded inputs.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_for_model.verbose" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_for_model.verbose"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>verbose</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
Whether or not to print more information and warnings.
**kwargs — passed to the <code>self.tokenize()</code> method<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.prepare_for_model.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li>
<p><strong>input_ids</strong> — List of token ids to be fed to a model.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a></p>
</li>
<li>
<p><strong>token_type_ids</strong> — List of token type ids to be fed to a model (when <code>return_token_type_ids=True</code> or
if <em>“token_type_ids”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a></p>
</li>
<li>
<p><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model (when
<code>return_attention_mask=True</code> or if <em>“attention_mask”</em> is in <code>self.model_input_names</code>).</p>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
</li>
<li>
<p><strong>overflowing_tokens</strong> — List of overflowing tokens sequences (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>num_truncated_tokens</strong> — Number of tokens truncated (when a <code>max_length</code> is specified and
<code>return_overflowing_tokens=True</code>).</p>
</li>
<li>
<p><strong>special_tokens_mask</strong> — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when <code>add_special_tokens=True</code> and <code>return_special_tokens_mask=True</code>).</p>
</li>
<li>
<p><strong>length</strong> — The length of the inputs (when <code>return_length=True</code>)</p>
</li>
</ul>
<!-- HTML_TAG_END --></p></div></div>
<p>Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens. Please Note, for <em>pair_ids</em>
different than <code>None</code> and <em>truncation_strategy = longest_first</em> or <code>True</code>, it is not possible to return
overflowing tokens. Such a combination of arguments will raise an error.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>prepare_seq2seq_batch</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3438" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">src_texts<span class="opacity-60">: typing.List[str]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">tgt_texts<span class="opacity-60">: typing.Optional[typing.List[str]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">max_target_length<span class="opacity-60">: typing.Optional[int] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">padding<span class="opacity-60">: str = 'longest'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">return_tensors<span class="opacity-60">: str = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation<span class="opacity-60">: bool = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.src_texts" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.src_texts"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>src_texts</strong> (<code>List[str]</code>) —
List of documents to summarize or source language texts.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.tgt_texts" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.tgt_texts"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>tgt_texts</strong> (<code>list</code>, <em>optional</em>) —
List of summaries or target language texts.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.max_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.max_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length for encoder inputs (documents to summarize or source language texts) If
left unset or set to <code>None</code>, this will use the predefined model maximum length if a maximum length is
required by one of the truncation/padding parameters. If the model has no specific maximum input length
(like XLNet) truncation/padding to a maximum length will be deactivated.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.max_target_length" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.max_target_length"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>max_target_length</strong> (<code>int</code>, <em>optional</em>) —
Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set
to <code>None</code>, this will use the max_length value.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.padding" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.padding"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>padding</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.file_utils.PaddingStrategy">PaddingStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
Activates and controls padding. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest'</code>: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).</li>
<li><code>'max_length'</code>: Pad to a maximum length specified with the argument <code>max_length</code> or to the maximum
acceptable input length for the model if that argument is not provided.</li>
<li><code>False</code> or <code>'do_not_pad'</code> (default): No padding (i.e., can output a batch with sequences of different
lengths).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.return_tensors" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.return_tensors"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) —
If set, will return tensors instead of list of python integers. Acceptable values are:</p>
<ul>
<li><code>'tf'</code>: Return TensorFlow <code>tf.constant</code> objects.</li>
<li><code>'pt'</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
<li><code>'np'</code>: Return Numpy <code>np.ndarray</code> objects.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.truncation" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.truncation"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation</strong> (<code>bool</code>, <code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>True</code>) —
Activates and controls truncation. Accepts the following values:</p>
<ul>
<li><code>True</code> or <code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>False</code> or <code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
**kwargs —
Additional keyword arguments passed along to <code>self.__call__</code>.</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.prepare_seq2seq_batch.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>A <a
href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.BatchEncoding"
>BatchEncoding</a> with the following fields:</p>
<ul>
<li><strong>input_ids</strong> — List of token ids to be fed to the encoder.</li>
<li><strong>attention_mask</strong> — List of indices specifying which tokens should be attended to by the model.</li>
<li><strong>labels</strong> — List of token ids for tgt_texts.</li>
</ul>
<p>The full set of keys <code>[input_ids, attention_mask, labels]</code>, will only be returned if tgt_texts is passed.
Otherwise, input_ids, attention_mask will be the only keys.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Prepare model inputs for translation. For best performance, translate one sentence at a time.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.file_utils.PushToHubMixin.push_to_hub"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>push_to_hub</span></h4><!-- HTML_TAG_END -->
<a id="transformers.file_utils.PushToHubMixin.push_to_hub" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.file_utils.PushToHubMixin.push_to_hub"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/file_utils.py#L2842" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_path_or_name<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">repo_url<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_temp_dir<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">commit_message<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">organization<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">private<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">use_auth_token<span class="opacity-60">: typing.Union[bool, str, NoneType] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**model_card_kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_path_or_name"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_path_or_name</strong> (<code>str</code>, <em>optional</em>) —
Can either be a repository name for your tokenizer in the Hub or a path to a local folder (in which case
the repository will have the name of that local folder). If not specified, will default to the name
given by <code>repo_url</code> and a local directory with that name will be created.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.repo_url" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.repo_url"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>repo_url</strong> (<code>str</code>, <em>optional</em>) —
Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
repository will be created in your namespace (unless you specify an <code>organization</code>) with <code>repo_name</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_temp_dir"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_temp_dir</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to clone the distant repo in a temporary directory or in <code>repo_path_or_name</code> inside the
current working directory. This will slow things down if you are making changes in an existing repo
since you will need to clone the repo before every push.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.commit_message" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.commit_message"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>commit_message</strong> (<code>str</code>, <em>optional</em>) —
Message to commit while pushing. Will default to <code>"add tokenizer"</code>.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.organization" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.organization"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>organization</strong> (<code>str</code>, <em>optional</em>) —
Organization in which you want to push your tokenizer (you must be a member of this organization).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.private" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.private"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>private</strong> (<code>bool</code>, <em>optional</em>) —
Whether or not the repository created should be private (requires a paying subscription).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.file_utils.PushToHubMixin.push_to_hub.use_auth_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>use_auth_token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) —
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>). Will default to <code>True</code> if
<code>repo_url</code> is not specified.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.file_utils.PushToHubMixin.push_to_hub.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The url of the commit of your tokenizer in the given repository.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Upload the tokenizer files to the 🤗 Model Hub while synchronizing a local clone of the repo in
<code>repo_path_or_name</code>.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>)
<span class="hljs-comment"># Push the tokenizer to your namespace with the name "my-finetuned-bert" with no local clone.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, use_temp_dir=<span class="hljs-literal">True</span>)
<span class="hljs-comment"># Push the tokenizer to an organization with the name "my-finetuned-bert" and have a local clone in the</span>
<span class="hljs-comment"># *my-finetuned-bert* folder.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, organization=<span class="hljs-string">"huggingface"</span>)
<span class="hljs-comment"># Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.</span>
tokenizer.push_to_hub(<span class="hljs-string">"my-finetuned-bert"</span>, repo_url=<span class="hljs-string">"https://huggingface.co/sgugger/my-finetuned-bert"</span>)<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.register_for_auto_class"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>register_for_auto_class</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.register_for_auto_class" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.register_for_auto_class"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3412" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">auto_class<span class="opacity-60"> = 'AutoTokenizer'</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.register_for_auto_class.auto_class" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.register_for_auto_class.auto_class"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>auto_class</strong> (<code>str</code> or <code>type</code>, <em>optional</em>, defaults to <code>"AutoTokenizer"</code>) —
The auto class to register this new tokenizer with.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the
library are already mapped with <code>AutoTokenizer</code>.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>This API is experimental and may have some slight breaking changes in the next releases.</p></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.save_pretrained"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_pretrained</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.save_pretrained" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.save_pretrained"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L1990" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60">: typing.Union[str, os.PathLike]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">legacy_format<span class="opacity-60">: typing.Optional[bool] = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filename_prefix<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">push_to_hub<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span>A tuple of <code>str</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.save_pretrained.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.save_pretrained.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) — The path to a directory where the tokenizer will be saved.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.save_pretrained.legacy_format" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.save_pretrained.legacy_format"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>legacy_format</strong> (<code>bool</code>, <em>optional</em>) —
Only applicable for a fast tokenizer. If unset (default), will save the tokenizer in the unified JSON
format as well as in legacy format if it exists, i.e. with tokenizer specific vocabulary and a separate
added_tokens files.</p>
<p>If <code>False</code>, will only save the tokenizer in the unified JSON format. This format is incompatible with
“slow” tokenizers (not powered by the <em>tokenizers</em> library), so the tokenizer will not be able to be
loaded in the corresponding “slow” tokenizer.</p>
<p>If <code>True</code>, will save the tokenizer in legacy format. If the “slow” tokenizer doesn’t exits, a value
error is raised.
filename_prefix — (<code>str</code>, <em>optional</em>):
A prefix to add to the names of the files saved by the tokenizer.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.save_pretrained.push_to_hub" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.save_pretrained.push_to_hub"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to push your model to the Hugging Face model hub after saving it.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p>Using <code>push_to_hub=True</code> will synchronize the repository you are pushing to with <code>save_directory</code>,
which requires <code>save_directory</code> to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along <code>temp_dir=True</code> to use a temporary directory instead.</p>
</div><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.save_pretrained.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p>A tuple of <code>str</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The files saved.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Save the full tokenizer state.</p>
<p>This method make sure the full tokenizer can then be re-loaded using the
<code>from_pretrained</code> class method..</p>
<p>Warning,None This won’t save modifications you may have applied to the tokenizer after the instantiation (for
instance, modifying <code>tokenizer.do_lower_case</code> after creation).</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.save_vocabulary"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>save_vocabulary</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.save_vocabulary" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.save_vocabulary"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2151" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">save_directory<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">filename_prefix<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Tuple(str)</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.save_vocabulary.save_directory" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.save_vocabulary.save_directory"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>save_directory</strong> (<code>str</code>) —
The directory in which to save the vocabulary.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.save_vocabulary.filename_prefix" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.save_vocabulary.filename_prefix"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>filename_prefix</strong> (<code>str</code>, <em>optional</em>) —
An optional prefix to add to the named of the saved files.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.save_vocabulary.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Tuple(str)</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Paths to the files saved.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Save only the vocabulary of the tokenizer (vocabulary + added tokens).</p>
<p>This method won’t save the configuration and special token mappings of the tokenizer. Use
<code>_save_pretrained()</code>to save the whole state of the tokenizer.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.tokenize"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>tokenize</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.tokenize" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.tokenize"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L2169" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">text<span class="opacity-60">: str</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pair<span class="opacity-60">: typing.Optional[str] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">add_special_tokens<span class="opacity-60">: bool = False</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>List[str]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.tokenize.text" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.tokenize.text"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>text</strong> (<code>str</code>) —
The sequence to be encoded.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.tokenize.pair" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.tokenize.pair"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pair</strong> (<code>str</code>, <em>optional</em>) —
A second sequence to be encoded with the first.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.tokenize.add_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.tokenize.add_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>add_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Whether or not to add the special tokens associated with the corresponding model.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.tokenize.kwargs" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.tokenize.kwargs"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) —
Will be passed to the underlying model specific encode method. See details in
<a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__"><strong>call</strong>()</a><!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.tokenize.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>List[str]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The list of tokens.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Converts a string in a sequence of tokens, replacing unknown tokens with the <code>unk_token</code>.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.PreTrainedTokenizerBase.truncate_sequences"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>truncate_sequences</span></h4><!-- HTML_TAG_END -->
<a id="transformers.PreTrainedTokenizerBase.truncate_sequences" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.PreTrainedTokenizerBase.truncate_sequences"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L3040" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">ids<span class="opacity-60">: typing.List[int]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">pair_ids<span class="opacity-60">: typing.Optional[typing.List[int]] = None</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">num_tokens_to_remove<span class="opacity-60">: int = 0</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">truncation_strategy<span class="opacity-60">: typing.Union[str, transformers.tokenization_utils_base.TruncationStrategy] = 'longest_first'</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">stride<span class="opacity-60">: int = 0</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>Tuple[List[int], List[int], List[int]]</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.truncate_sequences.ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.truncate_sequences.ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>ids</strong> (<code>List[int]</code>) —
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the <code>tokenize</code> and
<code>convert_tokens_to_ids</code> methods.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.truncate_sequences.pair_ids" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.truncate_sequences.pair_ids"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pair_ids</strong> (<code>List[int]</code>, <em>optional</em>) —
Tokenized input ids of the second sequence. Can be obtained from a string by chaining the <code>tokenize</code>
and <code>convert_tokens_to_ids</code> methods.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.truncate_sequences.num_tokens_to_remove" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.truncate_sequences.num_tokens_to_remove"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>num_tokens_to_remove</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
Number of tokens to remove using the truncation strategy.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.truncate_sequences.truncation_strategy" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.truncate_sequences.truncation_strategy"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>truncation_strategy</strong> (<code>str</code> or <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.tokenization_utils_base.TruncationStrategy">TruncationStrategy</a>, <em>optional</em>, defaults to <code>False</code>) —
The strategy to follow for truncation. Can be:</p>
<ul>
<li><code>'longest_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
batch of pairs) is provided.</li>
<li><code>'only_first'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'only_second'</code>: Truncate to a maximum length specified with the argument <code>max_length</code> or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.</li>
<li><code>'do_not_truncate'</code> (default): No truncation (i.e., can output batch with sequence lengths greater
than the model maximum admissible input size).</li>
</ul><!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.PreTrainedTokenizerBase.truncate_sequences.stride" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.PreTrainedTokenizerBase.truncate_sequences.stride"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>stride</strong> (<code>int</code>, <em>optional</em>, defaults to 0) —
If set to a positive number, the overflowing tokens returned will contain some tokens from the main
sequence returned. The value of this argument defines the number of additional tokens.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.PreTrainedTokenizerBase.truncate_sequences.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>Tuple[List[int], List[int], List[int]]</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The truncated <code>ids</code>, the truncated <code>pair_ids</code> and the list of
overflowing tokens. Note: The <em>longest_first</em> strategy returns empty list of overflowing tokens if a pair
of sequences (or a batch of pairs) is provided.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Truncates a sequence pair in-place following the strategy.</p></div></div>
<h2 class="relative group"><a id="transformers.SpecialTokensMixin" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>SpecialTokensMixin
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SpecialTokensMixin"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">SpecialTokensMixin</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.SpecialTokensMixin" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SpecialTokensMixin"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L753" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">verbose<span class="opacity-60"> = True</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">**kwargs<span class="opacity-60"></span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.bos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.bos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the beginning of a sentence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.eos_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.eos_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the end of a sentence.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.unk_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.unk_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing an out-of-vocabulary token.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.sep_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.sep_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>sep_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token separating two different sentences in the same input (used by BERT for instance).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.pad_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.pad_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.cls_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.cls_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>cls_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing the class of the input (used by BERT for instance).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.mask_token" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.mask_token"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>mask_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT).<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.additional_special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.additional_special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>additional_special_tokens</strong> (tuple or list of <code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) —
A tuple or a list of additional special tokens.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>A mixin derived by <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> and <a href="/docs/transformers/pr_16143/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> to handle specific behaviors related to
special tokens. In particular, this class hold the attributes which can be used to directly access these special
tokens in a model-independent manner and allow to set and update the special tokens.</p>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SpecialTokensMixin.add_special_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>add_special_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.SpecialTokensMixin.add_special_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SpecialTokensMixin.add_special_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L833" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens_dict<span class="opacity-60">: typing.Dict[str, typing.Union[str, tokenizers.AddedToken]]</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.add_special_tokens.special_tokens_dict" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.add_special_tokens.special_tokens_dict"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>special_tokens_dict</strong> (dictionary <em>str</em> to <em>str</em> or <code>tokenizers.AddedToken</code>) —
Keys should be in the list of predefined special attributes: [<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>,
<code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>, <code>additional_special_tokens</code>].</p>
<p>Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer
assign the index of the <code>unk_token</code> to them).<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.SpecialTokensMixin.add_special_tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Number of tokens added to the vocabulary.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If
special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the
current vocabulary).</p>
<p>Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding
matrix of the model so that its embedding matrix matches the tokenizer.</p>
<p>In order to do that, please use the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel.resize_token_embeddings">resize_token_embeddings()</a> method.</p>
<p>Using <code>add_special_tokens</code> will ensure your special tokens can be used in several ways:</p>
<ul><li>Special tokens are carefully handled by the tokenizer (they are never split).</li>
<li>You can easily refer to special tokens using tokenizer class attributes like <code>tokenizer.cls_token</code>. This
makes it easy to develop model-agnostic training and fine-tuning scripts.</li></ul>
<p>When possible, special tokens are already registered for provided pretrained models (for instance
<a href="/docs/transformers/pr_16143/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> <code>cls_token</code> is already registered to be :obj<em>’[CLS]’</em> and XLM’s one is also registered to be
<code>'</s>'</code>).</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># Let's see how to add a new classification token to GPT-2</span>
tokenizer = GPT2Tokenizer.from_pretrained(<span class="hljs-string">"gpt2"</span>)
model = GPT2Model.from_pretrained(<span class="hljs-string">"gpt2"</span>)
special_tokens_dict = {<span class="hljs-string">"cls_token"</span>: <span class="hljs-string">"<CLS>"</span>}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
<span class="hljs-built_in">print</span>(<span class="hljs-string">"We have added"</span>, num_added_toks, <span class="hljs-string">"tokens"</span>)
<span class="hljs-comment"># Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.</span>
model.resize_token_embeddings(<span class="hljs-built_in">len</span>(tokenizer))
<span class="hljs-keyword">assert</span> tokenizer.cls_token == <span class="hljs-string">"<CLS>"</span><!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SpecialTokensMixin.add_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>add_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.SpecialTokensMixin.add_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SpecialTokensMixin.add_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L905" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">new_tokens<span class="opacity-60">: typing.Union[str, tokenizers.AddedToken, typing.List[typing.Union[str, tokenizers.AddedToken]]]</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">special_tokens<span class="opacity-60">: bool = False</span></span>
</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.add_tokens.new_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.add_tokens.new_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>new_tokens</strong> (<code>str</code>, <code>tokenizers.AddedToken</code> or a list of <em>str</em> or <code>tokenizers.AddedToken</code>) —
Tokens are only added if they are not already in the vocabulary. <code>tokenizers.AddedToken</code> wraps a string
token to let you personalize its behavior: whether this token should only match against a single word,
whether this token should strip all potential whitespaces on the left side, whether this token should
strip all potential whitespaces on the right side, etc.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.SpecialTokensMixin.add_tokens.special_tokens" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.SpecialTokensMixin.add_tokens.special_tokens"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) —
Can be used to specify if the token is a special token. This mostly change the normalization behavior
(special tokens like CLS or [MASK] are usually not lower-cased for instance).</p>
<p>See details for <code>tokenizers.AddedToken</code> in HuggingFace tokenizers library.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.SpecialTokensMixin.add_tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>Number of tokens added to the vocabulary.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary.</p>
<p>Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding
matrix of the model so that its embedding matrix matches the tokenizer.</p>
<p>In order to do that, please use the <a href="/docs/transformers/pr_16143/en/main_classes/model#transformers.PreTrainedModel.resize_token_embeddings">resize_token_embeddings()</a> method.</p>
<p>Examples:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-comment"># Let's see how to increase the vocabulary of Bert model and tokenizer</span>
tokenizer = BertTokenizerFast.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
model = BertModel.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)
num_added_toks = tokenizer.add_tokens([<span class="hljs-string">"new_tok1"</span>, <span class="hljs-string">"my_new-tok2"</span>])
<span class="hljs-built_in">print</span>(<span class="hljs-string">"We have added"</span>, num_added_toks, <span class="hljs-string">"tokens"</span>)
<span class="hljs-comment"># Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.</span>
model.resize_token_embeddings(<span class="hljs-built_in">len</span>(tokenizer))<!-- HTML_TAG_END --></pre></div></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.SpecialTokensMixin.sanitize_special_tokens"><!-- HTML_TAG_START --><h4 class="!m-0"><span class="flex-1 rounded-xl py-0.5 break-all bg-gradient-to-r from-blue-50/60 to-white dark:from-gray-900 dark:to-gray-950 text-blue-700 dark:text-blue-300 font-medium px-2"><svg width="1em" height="1em" viewBox="0 0 32 33" class="mr-1 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg"><path d="M5.80566 18.3545C4.90766 17.4565 4.90766 16.0005 5.80566 15.1025L14.3768 6.53142C15.2748 5.63342 16.7307 5.63342 17.6287 6.53142L26.1999 15.1025C27.0979 16.0005 27.0979 17.4565 26.1999 18.3545L17.6287 26.9256C16.7307 27.8236 15.2748 27.8236 14.3768 26.9256L5.80566 18.3545Z" fill="currentColor" fill-opacity="0.25"/><path fill-rule="evenodd" clip-rule="evenodd" d="M16.4801 13.9619C16.4801 12.9761 16.7467 12.5436 16.9443 12.3296C17.1764 12.078 17.5731 11.8517 18.2275 11.707C18.8821 11.5623 19.638 11.5342 20.4038 11.5582C20.7804 11.57 21.1341 11.5932 21.4719 11.6156L21.5263 11.6193C21.8195 11.6389 22.1626 11.6618 22.4429 11.6618V7.40825C22.3209 7.40825 22.1219 7.39596 21.7544 7.37149C21.4202 7.34925 20.9976 7.32115 20.5371 7.30672C19.6286 7.27824 18.4672 7.29779 17.3093 7.55377C16.1512 7.8098 14.8404 8.33724 13.8181 9.4452C12.7612 10.5907 12.2266 12.1236 12.2266 13.9619V15.0127H10.6836V19.2662H12.2266V26.6332H16.4801V19.2662H20.3394V15.0127H16.4801V13.9619Z" fill="currentColor"/></svg>sanitize_special_tokens</span></h4><!-- HTML_TAG_END -->
<a id="transformers.SpecialTokensMixin.sanitize_special_tokens" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.SpecialTokensMixin.sanitize_special_tokens"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L821" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span>)</span>
<span class="font-bold">→</span>
<span class="rounded hover:bg-gray-400 cursor-pointer"><!-- HTML_TAG_START --><span><code>int</code></span><!-- HTML_TAG_END --></span></p>
<div class="!mb-10 relative docstring-details ">
<div class="flex items-center font-semibold space-x-3 text-base !mt-0 !mb-0 text-gray-800" id="transformers.SpecialTokensMixin.sanitize_special_tokens.returns"><p class="text-base">Returns</p>
<!-- HTML_TAG_START -->
<p><code>int</code></p>
<!-- HTML_TAG_END -->
<span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700"></span></div>
<p class="text-base"><!-- HTML_TAG_START -->
<p>The number of tokens added in the vocabulary during the operation.</p>
<!-- HTML_TAG_END --></p></div></div>
<p>Make sure that all the special tokens attributes of the tokenizer (<code>tokenizer.mask_token</code>,
<code>tokenizer.cls_token</code>, etc.) are in the vocabulary.</p>
<p>Add the missing ones to the vocabulary if needed.</p></div></div>
<h2 class="relative group"><a id="transformers.tokenization_utils_base.TruncationStrategy" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.tokenization_utils_base.TruncationStrategy"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Enums and namedtuples
</span></h2>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.tokenization_utils_base.TruncationStrategy"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.tokenization_utils_base.</span><span class="font-semibold">TruncationStrategy</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.tokenization_utils_base.TruncationStrategy" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.tokenization_utils_base.TruncationStrategy"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L129" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">value<span class="opacity-60"></span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">names<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">module<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">qualname<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">type<span class="opacity-60"> = None</span></span>
</span><span class="comma cursor-default"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60"> = 1</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
</div></div>
<p>Possible values for the <code>truncation</code> argument in <a href="/docs/transformers/pr_16143/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizerBase.<strong>call</strong>()</a>. Useful for tab-completion in
an IDE.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.CharSpan"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">CharSpan</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.CharSpan" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.CharSpan"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L141" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.CharSpan.start" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.CharSpan.start"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start</strong> (<code>int</code>) — Index of the first character in the original string.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.CharSpan.end" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.CharSpan.end"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end</strong> (<code>int</code>) — Index of the character following the last character in the original string.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Character span in the original string.</p></div>
<div class="docstring"><div><span class="group flex space-x-1.5 items-center text-gray-800 bg-gradient-to-r rounded-tr-lg -mt-4 -ml-4 pt-3 px-2.5" id="transformers.TokenSpan"><!-- HTML_TAG_START --><h3 class="!m-0"><span class="flex-1 break-all md:text-lg bg-gradient-to-r px-2.5 py-1.5 rounded-xl from-indigo-50/70 to-white dark:from-gray-900 dark:to-gray-950 dark:text-indigo-300 text-indigo-700"><svg class="mr-1.5 text-indigo-500 inline-block -mt-0.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width=".8em" height=".8em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg><span class="font-light">class</span> <span class="font-medium">transformers.</span><span class="font-semibold">TokenSpan</span></span></h3><!-- HTML_TAG_END -->
<a id="transformers.TokenSpan" class="header-link invisible with-hover:group-hover:visible pr-2" href="#transformers.TokenSpan"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></a>
<a class="!ml-auto !text-gray-400 !no-underline text-sm flex items-center" href="https://github.com/huggingface/transformers/blob/pr_16143/src/transformers/tokenization_utils_base.py#L154" target="_blank"><span><</span>
<span class="hidden md:block mx-0.5 hover:!underline">source</span>
<span>></span></a></span>
<p class="font-mono text-xs md:text-sm !leading-relaxed !my-6"><span>(</span>
<span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">start<span class="opacity-60">: int</span></span>
</span><span class="comma cursor-pointer"><span class="rounded hover:bg-black hover:text-white dark:hover:bg-white dark:hover:text-black">end<span class="opacity-60">: int</span></span>
</span>
<span>)</span>
</p>
<div class="!mb-10 relative docstring-details ">
<p class="flex items-center font-semibold !mt-2 !mb-2 text-gray-800">Parameters <span class="flex-auto border-t-2 border-gray-100 dark:border-gray-700 ml-3"></span></p>
<ul class="px-2"><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenSpan.start" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenSpan.start"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>start</strong> (<code>int</code>) — Index of the first token in the span.<!-- HTML_TAG_END -->
</span></span>
</li><li class="text-base !pl-4 my-3"><span class="group flex space-x-1.5 items-start"><a id="transformers.TokenSpan.end" class="header-link block pr-0.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers.TokenSpan.end"><span><svg class="text-smd" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span><!-- HTML_TAG_START --><strong>end</strong> (<code>int</code>) — Index of the token following the last token in the span.<!-- HTML_TAG_END -->
</span></span>
</li></ul>
</div></div>
<p>Token span in an encoded string (list of tokens).</p></div>
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<h1 class="relative group"><a id="guia-de-instalao" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#guia-de-instalao"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Guia de Instalação
</span></h1>
<p>Neste guia poderá encontrar informações para a instalação do 🤗 Transformers para qualquer biblioteca de
Machine Learning com a qual esteja a trabalhar. Além disso, poderá encontrar informações sobre como gerar cachês e
configurar o 🤗 Transformers para execução em modo offline (opcional).</p>
<p>🤗 Transformers foi testado com Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, e Flax. Para instalar a biblioteca de
deep learning com que deseja trabalhar, siga as instruções correspondentes listadas a seguir:</p>
<ul><li><a href="https://pytorch.org/get-started/locally/" rel="nofollow">PyTorch</a></li>
<li><a href="https://www.tensorflow.org/install/pip" rel="nofollow">TensorFlow 2.0</a></li>
<li><a href="https://flax.readthedocs.io/en/latest/" rel="nofollow">Flax</a></li></ul>
<h2 class="relative group"><a id="instalao-pelo-pip" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#instalao-pelo-pip"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Instalação pelo Pip
</span></h2>
<p>É sugerido instalar o 🤗 Transformers num <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">ambiente virtual</a>. Se precisar
de mais informações sobre ambientes virtuais em Python, consulte este <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">guia</a>.
Um ambiente virtual facilitará a manipulação e organização de projetos e evita problemas de compatibilidade entre dependências.</p>
<p>Comece criando um ambiente virtual no diretório do seu projeto:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python -m venv .<span class="hljs-built_in">env</span><!-- HTML_TAG_END --></pre></div>
<p>E para ativar o ambiente virtual:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">source</span> .<span class="hljs-built_in">env</span>/bin/activate<!-- HTML_TAG_END --></pre></div>
<p>Agora É possível instalar o 🤗 Transformers com o comando a seguir:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install transformers<!-- HTML_TAG_END --></pre></div>
<p>Somente para a CPU, é possível instalar o 🤗 Transformers e a biblioteca de deep learning respectiva apenas numa linha.</p>
<p>Por exemplo, para instalar o 🤗 Transformers e o PyTorch, digite:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install transformers[torch]<!-- HTML_TAG_END --></pre></div>
<p>🤗 Transformers e TensorFlow 2.0:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install transformers[tf-cpu]<!-- HTML_TAG_END --></pre></div>
<p>🤗 Transformers e Flax:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install transformers[flax]<!-- HTML_TAG_END --></pre></div>
<p>Por último, verifique se o 🤗 Transformers foi instalado com sucesso usando o seguinte comando para baixar um modelo pré-treinado:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python -c <span class="hljs-string">"from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"</span><!-- HTML_TAG_END --></pre></div>
<p>Em seguida, imprima um rótulo e sua pontuação:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: 0.9998704791069031}]<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="instalao-usando-a-fonte" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#instalao-usando-a-fonte"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Instalação usando a fonte
</span></h2>
<p>Para instalar o 🤗 Transformers a partir da fonte use o seguinte comando:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install git+https://github.com/huggingface/transformers<!-- HTML_TAG_END --></pre></div>
<p>O comando acima instalará a versão <code>master</code> mais atual em vez da última versão estável. A versão <code>master</code> é útil para
utilizar os últimos updates contidos em 🤗 Transformers. Por exemplo, um erro recente pode ter sido corrigido somente
após a última versão estável, antes que houvesse um novo lançamento. No entanto, há a possibilidade que a versão <code>master</code> não esteja estável.
A equipa trata de mantér a versão <code>master</code> operacional e a maioria dos erros são resolvidos em poucas horas ou dias.
Se encontrar quaisquer problemas, por favor abra um <a href="https://github.com/huggingface/transformers/issues" rel="nofollow">Issue</a> para que o
mesmo possa ser corrigido o mais rápido possível.</p>
<p>Verifique que o 🤗 Transformers está instalado corretamente usando o seguinte comando:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python -c <span class="hljs-string">"from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"</span><!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="instalao-editvel" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#instalao-editvel"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Instalação editável
</span></h2>
<p>Uma instalação editável será necessária caso desejas um dos seguintes:</p>
<ul><li>Usar a versão <code>master</code> do código fonte.</li>
<li>Contribuir ao 🤗 Transformers e precisa testar mudanças ao código.</li></ul>
<p>Para tal, clone o repositório e instale o 🤗 Transformers com os seguintes comandos:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->git <span class="hljs-built_in">clone</span> https://github.com/huggingface/transformers.git
<span class="hljs-built_in">cd</span> transformers
pip install -e .<!-- HTML_TAG_END --></pre></div>
<p>Estes comandos vão ligar o diretório para o qual foi clonado o repositório ao caminho de bibliotecas do Python.
O Python agora buscará dentro dos arquivos que foram clonados além dos caminhos normais da biblioteca.
Por exemplo, se os pacotes do Python se encontram instalados no caminho <code>~/anaconda3/envs/main/lib/python3.7/site-packages/</code>,
o Python também buscará módulos no diretório onde clonamos o repositório <code>~/transformers/</code>.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>É necessário manter o diretório <code>transformers</code> se desejas continuar usando a biblioteca.</p></div>
<p>Assim, É possível atualizar sua cópia local para com a última versão do 🤗 Transformers com o seguinte comando:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">cd</span> ~/transformers/
git pull<!-- HTML_TAG_END --></pre></div>
<p>O ambiente de Python que foi criado para a instalação do 🤗 Transformers encontrará a versão <code>master</code> em execuções seguintes.</p>
<h2 class="relative group"><a id="instalao-usando-o-conda" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#instalao-usando-o-conda"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Instalação usando o Conda
</span></h2>
<p>É possível instalar o 🤗 Transformers a partir do canal conda <code>huggingface</code> com o seguinte comando:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->conda install -c huggingface transformers<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="configurao-do-cach" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#configurao-do-cach"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Configuração do Cachê
</span></h2>
<p>Os modelos pré-treinados são baixados e armazenados no cachê local, encontrado em <code>~/.cache/huggingface/transformers/</code>.
Este é o diretório padrão determinado pela variável <code>TRANSFORMERS_CACHE</code> dentro do shell.
No Windows, este diretório pré-definido é dado por <code>C:\Users\username\.cache\huggingface\transformers</code>.
É possível mudar as variáveis dentro do shell em ordem de prioridade para especificar um diretório de cachê diferente:</p>
<ol><li>Variável de ambiente do shell (por padrão): <code>TRANSFORMERS_CACHE</code>.</li>
<li>Variável de ambiente do shell:<code>HF_HOME</code> + <code>transformers/</code>.</li>
<li>Variável de ambiente do shell: <code>XDG_CACHE_HOME</code> + <code>/huggingface/transformers</code>.</li></ol>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>O 🤗 Transformers usará as variáveis de ambiente do shell <code>PYTORCH_TRANSFORMERS_CACHE</code> ou <code>PYTORCH_PRETRAINED_BERT_CACHE</code>
se estiver vindo de uma versão anterior da biblioteca que tenha configurado essas variáveis de ambiente, a menos que
você especifique a variável de ambiente do shell <code>TRANSFORMERS_CACHE</code>.</p></div>
<h2 class="relative group"><a id="modo-offline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#modo-offline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Modo Offline
</span></h2>
<p>O 🤗 Transformers também pode ser executado num ambiente de firewall ou fora da rede (offline) usando arquivos locais.
Para tal, configure a variável de ambiente de modo que <code>TRANSFORMERS_OFFLINE=1</code>.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Você pode adicionar o <a href="https://huggingface.co/docs/datasets/" rel="nofollow">🤗 Datasets</a> ao pipeline de treinamento offline declarando
a variável de ambiente <code>HF_DATASETS_OFFLINE=1</code>.</p></div>
<p>Segue um exemplo de execução do programa numa rede padrão com firewall para instâncias externas, usando o seguinte comando:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...<!-- HTML_TAG_END --></pre></div>
<p>Execute esse mesmo programa numa instância offline com o seguinte comando:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...<!-- HTML_TAG_END --></pre></div>
<p>O script agora deve ser executado sem travar ou expirar, pois procurará apenas por arquivos locais.</p>
<h3 class="relative group"><a id="obtendo-modelos-e-tokenizers-para-uso-offline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#obtendo-modelos-e-tokenizers-para-uso-offline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Obtendo modelos e tokenizers para uso offline
</span></h3>
<p>Outra opção para usar o 🤗 Transformers offline é baixar os arquivos antes e depois apontar para o caminho local onde estão localizados. Existem três maneiras de fazer isso:</p>
<ul><li><p>Baixe um arquivo por meio da interface de usuário do <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> clicando no ícone ↓.</p>
<p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/download-icon.png" alt="download-icon"></p></li></ul>
<ul><li><p>Use o pipeline do <code>PreTrainedModel.from_pretrained()</code> e <code>PreTrainedModel.save_pretrained()</code>:</p>
<ol><li><p>Baixa os arquivos previamente com <code>PreTrainedModel.from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bigscience/T0_3B"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"bigscience/T0_3B"</span>)<!-- HTML_TAG_END --></pre></div></li></ol></li></ul>
<ol start="2"><li><p>Salve os arquivos em um diretório específico com <code>PreTrainedModel.save_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenizer.save_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)
<span class="hljs-meta">>>> </span>model.save_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)<!-- HTML_TAG_END --></pre></div></li>
<li><p>Quando estiver offline, acesse os arquivos com <code>PreTrainedModel.from_pretrained()</code> do diretório especificado:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)
<span class="hljs-meta">>>> </span>model = AutoModel.from_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)<!-- HTML_TAG_END --></pre></div></li></ol>
<ul><li><p>Baixando arquivos programaticamente com a biblioteca <a href="https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub" rel="nofollow">huggingface_hub</a>:</p>
<ol><li><p>Instale a biblioteca <a href="https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub" rel="nofollow">huggingface_hub</a> em seu ambiente virtual:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->python -m pip install huggingface_hub<!-- HTML_TAG_END --></pre></div></li>
<li><p>Utiliza a função <a href="https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub" rel="nofollow"><code>hf_hub_download</code></a> para baixar um arquivo para um caminho específico. Por exemplo, o comando a seguir baixará o arquivo <code>config.json</code> para o modelo <a href="https://huggingface.co/bigscience/T0_3B" rel="nofollow">T0</a> no caminho desejado:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download
<span class="hljs-meta">>>> </span>hf_hub_download(repo_id=<span class="hljs-string">"bigscience/T0_3B"</span>, filename=<span class="hljs-string">"config.json"</span>, cache_dir=<span class="hljs-string">"./your/path/bigscience_t0"</span>)<!-- HTML_TAG_END --></pre></div></li></ol></li></ul>
<p>Depois que o arquivo for baixado e armazenado no cachê local, especifique seu caminho local para carregá-lo e usá-lo:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig
<span class="hljs-meta">>>> </span>config = AutoConfig.from_pretrained(<span class="hljs-string">"./your/path/bigscience_t0/config.json"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Para obter mais detalhes sobre como baixar arquivos armazenados no Hub, consulte a seção <a href="https://huggingface.co/docs/hub/how-to-downstream" rel="nofollow">How to download files from the Hub</a>.</p></div>
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0 | hf_public_repos/doc-build-dev/transformers/pr_18789 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_toctree.yml | - sections:
- local: index
title: 🤗 Transformers
- local: quicktour
title: Tour rápido
- local: installation
title: Instalação
title: Início
- sections:
- local: pipeline_tutorial
title: Pipelines para inferência
- local: training
title: Fine-tuning de um modelo pré-treinado
- local: accelerate
title: Treinamento distribuído com 🤗 Accelerate
title: Tutoriais
- sections:
- local: fast_tokenizers
title: Usando os Tokenizers do 🤗 Tokenizers
- local: create_a_model
title: Criando uma arquitetura customizada
- sections:
- local: tasks/sequence_classification
title: Classificação de texto
- local: tasks/token_classification
title: Classificação de tokens
title: Fine-tuning para tarefas específicas
- local: multilingual
title: Modelos multilinguísticos para inferência
title: Guias práticos
| 457 |
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<h1 class="relative group"><a id="modelos-multilingusticos-para-inferncia" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#modelos-multilingusticos-para-inferncia"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Modelos multilinguísticos para inferência
</span></h1>
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<p>Existem vários modelos multilinguísticos no 🤗 Transformers e seus usos para inferência diferem dos modelos monolíngues.
No entanto, nem <em>todos</em> os usos dos modelos multilíngues são tão diferentes.
Alguns modelos, como o <a href="https://huggingface.co/bert-base-multilingual-uncased" rel="nofollow">bert-base-multilingual-uncased</a>,
podem ser usados como se fossem monolíngues. Este guia irá te ajudar a usar modelos multilíngues cujo uso difere
para o propósito de inferência.</p>
<h2 class="relative group"><a id="xlm" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#xlm"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>XLM
</span></h2>
<p>O XLM tem dez checkpoints diferentes dos quais apenas um é monolíngue.
Os nove checkpoints restantes do modelo são subdivididos em duas categorias:
checkpoints que usam de language embeddings e os que não.</p>
<h3 class="relative group"><a id="xlm-com-language-embeddings" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#xlm-com-language-embeddings"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>XLM com language embeddings
</span></h3>
<p>Os seguintes modelos de XLM usam language embeddings para especificar a linguagem utilizada para a inferência.</p>
<ul><li><code>xlm-mlm-ende-1024</code> (Masked language modeling, English-German)</li>
<li><code>xlm-mlm-enfr-1024</code> (Masked language modeling, English-French)</li>
<li><code>xlm-mlm-enro-1024</code> (Masked language modeling, English-Romanian)</li>
<li><code>xlm-mlm-xnli15-1024</code> (Masked language modeling, XNLI languages)</li>
<li><code>xlm-mlm-tlm-xnli15-1024</code> (Masked language modeling + translation, XNLI languages)</li>
<li><code>xlm-clm-enfr-1024</code> (Causal language modeling, English-French)</li>
<li><code>xlm-clm-ende-1024</code> (Causal language modeling, English-German)</li></ul>
<p>Os language embeddings são representados por um tensor de mesma dimensão que os <code>input_ids</code> passados ao modelo.
Os valores destes tensores dependem do idioma utilizado e se identificam pelos atributos <code>lang2id</code> e <code>id2lang</code> do tokenizador.</p>
<p>Neste exemplo, carregamos o checkpoint <code>xlm-clm-enfr-1024</code>(Causal language modeling, English-French):</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> XLMTokenizer, XLMWithLMHeadModel
<span class="hljs-meta">>>> </span>tokenizer = XLMTokenizer.from_pretrained(<span class="hljs-string">"xlm-clm-enfr-1024"</span>)
<span class="hljs-meta">>>> </span>model = XLMWithLMHeadModel.from_pretrained(<span class="hljs-string">"xlm-clm-enfr-1024"</span>)<!-- HTML_TAG_END --></pre></div>
<p>O atributo <code>lang2id</code> do tokenizador mostra os idiomas deste modelo e seus ids:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(tokenizer.lang2id)
{<span class="hljs-string">'en'</span>: <span class="hljs-number">0</span>, <span class="hljs-string">'fr'</span>: <span class="hljs-number">1</span>}<!-- HTML_TAG_END --></pre></div>
<p>Em seguida, cria-se um input de exemplo:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>input_ids = torch.tensor([tokenizer.encode(<span class="hljs-string">"Wikipedia was used to"</span>)]) <span class="hljs-comment"># batch size of 1</span><!-- HTML_TAG_END --></pre></div>
<p>Estabelece-se o id do idioma, por exemplo <code>"en"</code>, e utiliza-se o mesmo para definir a language embedding.
A language embedding é um tensor preenchido com <code>0</code>, que é o id de idioma para o inglês.
Este tensor deve ser do mesmo tamanho que os <code>input_ids</code>.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>language_id = tokenizer.lang2id[<span class="hljs-string">"en"</span>] <span class="hljs-comment"># 0</span>
<span class="hljs-meta">>>> </span>langs = torch.tensor([language_id] * input_ids.shape[<span class="hljs-number">1</span>]) <span class="hljs-comment"># torch.tensor([0, 0, 0, ..., 0])</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># We reshape it to be of size (batch_size, sequence_length)</span>
<span class="hljs-meta">>>> </span>langs = langs.view(<span class="hljs-number">1</span>, -<span class="hljs-number">1</span>) <span class="hljs-comment"># is now of shape [1, sequence_length] (we have a batch size of 1)</span><!-- HTML_TAG_END --></pre></div>
<p>Agora você pode passar os <code>input_ids</code> e a language embedding ao modelo:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>outputs = model(input_ids, langs=langs)<!-- HTML_TAG_END --></pre></div>
<p>O script <a href="https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py" rel="nofollow">run_generation.py</a> pode gerar um texto com language embeddings utilizando os checkpoints <code>xlm-clm</code>.</p>
<h3 class="relative group"><a id="xlm-sem-language-embeddings" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#xlm-sem-language-embeddings"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>XLM sem language embeddings
</span></h3>
<p>Os seguintes modelos XLM não requerem o uso de language embeddings durante a inferência:</p>
<ul><li><code>xlm-mlm-17-1280</code> (Modelagem de linguagem com máscara, 17 idiomas)</li>
<li><code>xlm-mlm-100-1280</code> (Modelagem de linguagem com máscara, 100 idiomas)</li></ul>
<p>Estes modelos são utilizados para representações genéricas de frase diferentemente dos checkpoints XLM anteriores.</p>
<h2 class="relative group"><a id="bert" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#bert"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>BERT
</span></h2>
<p>Os seguintes modelos do BERT podem ser utilizados para tarefas multilinguísticas:</p>
<ul><li><code>bert-base-multilingual-uncased</code> (Modelagem de linguagem com máscara + Previsão de frases, 102 idiomas)</li>
<li><code>bert-base-multilingual-cased</code> (Modelagem de linguagem com máscara + Previsão de frases, 104 idiomas)</li></ul>
<p>Estes modelos não requerem language embeddings durante a inferência. Devem identificar a linguagem a partir
do contexto e realizar a inferência em sequência.</p>
<h2 class="relative group"><a id="xlmroberta" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#xlmroberta"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>XLM-RoBERTa
</span></h2>
<p>Os seguintes modelos do XLM-RoBERTa podem ser utilizados para tarefas multilinguísticas:</p>
<ul><li><code>xlm-roberta-base</code> (Modelagem de linguagem com máscara, 100 idiomas)</li>
<li><code>xlm-roberta-large</code> Modelagem de linguagem com máscara, 100 idiomas)</li></ul>
<p>O XLM-RoBERTa foi treinado com 2,5 TB de dados do CommonCrawl recém-criados e testados em 100 idiomas.
Proporciona fortes vantagens sobre os modelos multilinguísticos publicados anteriormente como o mBERT e o XLM em tarefas
subsequentes como a classificação, a rotulagem de sequências e à respostas a perguntas.</p>
<h2 class="relative group"><a id="m2m100" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#m2m100"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>M2M100
</span></h2>
<p>Os seguintes modelos de M2M100 podem ser utilizados para traduções multilinguísticas:</p>
<ul><li><code>facebook/m2m100_418M</code> (Tradução)</li>
<li><code>facebook/m2m100_1.2B</code> (Tradução)</li></ul>
<p>Neste exemplo, o checkpoint <code>facebook/m2m100_418M</code> é carregado para traduzir do mandarim ao inglês. É possível
estabelecer o idioma de origem no tokenizador:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> M2M100ForConditionalGeneration, M2M100Tokenizer
<span class="hljs-meta">>>> </span>en_text = <span class="hljs-string">"Do not meddle in the affairs of wizards, for they are subtle and quick to anger."</span>
<span class="hljs-meta">>>> </span>chinese_text = <span class="hljs-string">"不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒."</span>
<span class="hljs-meta">>>> </span>tokenizer = M2M100Tokenizer.from_pretrained(<span class="hljs-string">"facebook/m2m100_418M"</span>, src_lang=<span class="hljs-string">"zh"</span>)
<span class="hljs-meta">>>> </span>model = M2M100ForConditionalGeneration.from_pretrained(<span class="hljs-string">"facebook/m2m100_418M"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Tokenização do texto:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>encoded_zh = tokenizer(chinese_text, return_tensors=<span class="hljs-string">"pt"</span>)<!-- HTML_TAG_END --></pre></div>
<p>O M2M100 força o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
É definido o <code>forced_bos_token_id</code> como <code>en</code> no método <code>generate</code> para traduzir ao inglês.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id(<span class="hljs-string">"en"</span>))
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(generated_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.'</span><!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="mbart" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#mbart"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>MBart
</span></h2>
<p>Os seguintes modelos do MBart podem ser utilizados para tradução multilinguística:</p>
<ul><li><code>facebook/mbart-large-50-one-to-many-mmt</code> (Tradução automática multilinguística de um a vários, 50 idiomas)</li>
<li><code>facebook/mbart-large-50-many-to-many-mmt</code> (Tradução automática multilinguística de vários a vários, 50 idiomas)</li>
<li><code>facebook/mbart-large-50-many-to-one-mmt</code> (Tradução automática multilinguística vários a um, 50 idiomas)</li>
<li><code>facebook/mbart-large-50</code> (Tradução multilinguística, 50 idiomas)</li>
<li><code>facebook/mbart-large-cc25</code></li></ul>
<p>Neste exemplo, carrega-se o checkpoint <code>facebook/mbart-large-50-many-to-many-mmt</code> para traduzir do finlandês ao inglês.
Pode-se definir o idioma de origem no tokenizador:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>en_text = <span class="hljs-string">"Do not meddle in the affairs of wizards, for they are subtle and quick to anger."</span>
<span class="hljs-meta">>>> </span>fi_text = <span class="hljs-string">"Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia."</span>
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"facebook/mbart-large-50-many-to-many-mmt"</span>, src_lang=<span class="hljs-string">"fi_FI"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"facebook/mbart-large-50-many-to-many-mmt"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Tokenizando o texto:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>encoded_en = tokenizer(en_text, return_tensors=<span class="hljs-string">"pt"</span>)<!-- HTML_TAG_END --></pre></div>
<p>O MBart força o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
É definido o <code>forced_bos_token_id</code> como <code>en</code> no método <code>generate</code> para traduzir ao inglês.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id(<span class="hljs-string">"en_XX"</span>))
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(generated_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">"Don't interfere with the wizard's affairs, because they are subtle, will soon get angry."</span><!-- HTML_TAG_END --></pre></div>
<p>Se estiver usando o checkpoint <code>facebook/mbart-large-50-many-to-one-mmt</code> não será necessário forçar o id do idioma de destino
como sendo o primeiro token generado, caso contrário a usagem é a mesma.</p>
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<h1 class="relative group"><a id="pipelines-para-inferncia" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipelines-para-inferncia"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipelines para inferência
</span></h1>
<p>Um [pipeline] simplifica o uso dos modelos no <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> para a inferência de uma diversidade de tarefas,
como a geração de texto, a segmentação de imagens e a classificação de áudio.
Inclusive, se não tem experiência com alguma modalidade específica ou não compreende o código que forma os modelos,
pode usar eles mesmo assim com o [pipeline]! Este tutorial te ensinará a:</p>
<ul><li>Utilizar um <code>pipeline()</code> para inferência.</li>
<li>Utilizar um tokenizador ou model específico.</li>
<li>Utilizar um <code>pipeline()</code> para tarefas de áudio e visão computacional.</li></ul>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Acesse a documentação do <code>pipeline()</code> para obter uma lista completa de tarefas possíveis.</p></div>
<h2 class="relative group"><a id="uso-do-pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#uso-do-pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Uso do pipeline
</span></h2>
<p>Mesmo que cada tarefa tenha um <code>pipeline()</code> associado, é mais simples usar a abstração geral do <code>pipeline()</code> que
contém todos os pipelines das tarefas mais específicas.
O <code>pipeline()</code> carrega automaticamenta um modelo predeterminado e um tokenizador com capacidade de inferência para sua
tarefa.</p>
<ol><li>Comece carregando um <code>pipeline()</code> e especifique uma tarefa de inferência:</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>generator = pipeline(task=<span class="hljs-string">"text-generation"</span>)<!-- HTML_TAG_END --></pre></div>
<ol start="2"><li>Passe seu dado de entrada, no caso um texto, ao <code>pipeline()</code>:</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(<span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>)
[{<span class="hljs-string">'generated_text'</span>: <span class="hljs-string">'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Se tiver mais de uma entrada, passe-a como uma lista:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>,
<span class="hljs-meta">... </span> <span class="hljs-string">"Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne"</span>,
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Qualquer parâmetro adicional para a sua tarefa também pode ser incluído no <code>pipeline()</code>. A tarefa <code>text-generation</code> tem um método
<code>generate()</code> com vários parâmetros para controlar a saída.
Por exemplo, se quiser gerar mais de uma saída, defina-a no parâmetro <code>num_return_sequences</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>,
<span class="hljs-meta">... </span> num_return_sequences=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="selecionando-um-modelo-e-um-tokenizador" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#selecionando-um-modelo-e-um-tokenizador"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Selecionando um modelo e um tokenizador
</span></h3>
<p>O <code>pipeline()</code> aceita qualquer modelo do <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>. Há rótulos adicionais no Model Hub
que te permitem filtrar pelo modelo que gostaria de usar para sua tarefa. Uma vez que tiver escolhido o modelo apropriado,
carregue-o com as classes <code>AutoModelFor</code> e [`AutoTokenizer’] correspondentes. Por exemplo, carregue a classe <code>AutoModelForCausalLM</code>
para uma tarefa de modelagem de linguagem causal:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Crie uma <code>pipeline()</code> para a sua tarefa e especifíque o modelo e o tokenizador que foram carregados:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>generator = pipeline(task=<span class="hljs-string">"text-generation"</span>, model=model, tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div>
<p>Passe seu texto de entrada ao <code>pipeline()</code> para gerar algum texto:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(<span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>)
[{<span class="hljs-string">'generated_text'</span>: <span class="hljs-string">'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'</span>}]<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="pipeline-de-audio" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline-de-audio"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipeline de audio
</span></h2>
<p>A flexibilidade do <code>pipeline()</code> significa que também pode-se extender às tarefas de áudio.
La flexibilidad de <code>pipeline()</code> significa que también se puede extender a tareas de audio.</p>
<p>Por exemplo, classifiquemos a emoção de um breve fragmento do famoso discurso de John F. Kennedy /home/rzimmerdev/dev/transformers/docs/source/pt/pipeline_tutorial.mdx
Encontre um modelo de <a href="https://huggingface.co/models?pipeline_tag=audio-classification" rel="nofollow">audio classification</a> para
reconhecimento de emoções no Model Hub e carregue-o usando o <code>pipeline()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>audio_classifier = pipeline(
<span class="hljs-meta">... </span> task=<span class="hljs-string">"audio-classification"</span>, model=<span class="hljs-string">"ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"</span>
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Passe o arquivo de áudio ao <code>pipeline()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>audio_classifier(<span class="hljs-string">"jfk_moon_speech.wav"</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'calm'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.13856211304664612</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'disgust'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.13148026168346405</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'happy'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.12635163962841034</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'angry'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.12439591437578201</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'fearful'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.12404385954141617</span>}]<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="pipeline-de-viso-computacional" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline-de-viso-computacional"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipeline de visão computacional
</span></h2>
<p>Finalmente, utilizar um <code>pipeline()</code> para tarefas de visão é praticamente a mesma coisa.
Especifique a sua tarefa de visão e passe a sua imagem ao classificador.
A imagem pode ser um link ou uma rota local à imagem. Por exemplo, que espécie de gato está presente na imagem?</p>
<p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" alt="pipeline-cat-chonk"></p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>vision_classifier = pipeline(task=<span class="hljs-string">"image-classification"</span>)
<span class="hljs-meta">>>> </span>vision_classifier(
<span class="hljs-meta">... </span> images=<span class="hljs-string">"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"</span>
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'lynx, catamount'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.4403027892112732</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'</span>,
<span class="hljs-string">'score'</span>: <span class="hljs-number">0.03433405980467796</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'snow leopard, ounce, Panthera uncia'</span>,
<span class="hljs-string">'score'</span>: <span class="hljs-number">0.032148055732250214</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'Egyptian cat'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.02353910356760025</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'tiger cat'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.023034192621707916</span>}]<!-- HTML_TAG_END --></pre></div>
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<h1 class="relative group"><a id="usando-os-tokenizers-do-tokenizers" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#usando-os-tokenizers-do-tokenizers"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Usando os Tokenizers do 🤗 Tokenizers
</span></h1>
<p>O <code>PreTrainedTokenizerFast</code> depende da biblioteca <a href="https://huggingface.co/docs/tokenizers" rel="nofollow">🤗 Tokenizers</a>. O Tokenizer obtido da biblioteca 🤗 Tokenizers pode ser carregado facilmente pelo 🤗 Transformers.</p>
<p>Antes de entrar nos detalhes, vamos começar criando um tokenizer fictício em algumas linhas:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers <span class="hljs-keyword">import</span> Tokenizer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers.models <span class="hljs-keyword">import</span> BPE
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers.trainers <span class="hljs-keyword">import</span> BpeTrainer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers.pre_tokenizers <span class="hljs-keyword">import</span> Whitespace
<span class="hljs-meta">>>> </span>tokenizer = Tokenizer(BPE(unk_token=<span class="hljs-string">"[UNK]"</span>))
<span class="hljs-meta">>>> </span>trainer = BpeTrainer(special_tokens=[<span class="hljs-string">"[UNK]"</span>, <span class="hljs-string">"[CLS]"</span>, <span class="hljs-string">"[SEP]"</span>, <span class="hljs-string">"[PAD]"</span>, <span class="hljs-string">"[MASK]"</span>])
<span class="hljs-meta">>>> </span>tokenizer.pre_tokenizer = Whitespace()
<span class="hljs-meta">>>> </span>files = [...]
<span class="hljs-meta">>>> </span>tokenizer.train(files, trainer)<!-- HTML_TAG_END --></pre></div>
<p>Agora temos um tokenizer treinado nos arquivos que foram definidos. Nós podemos continuar usando nessa execução ou salvar em um arquivo JSON para re-utilizar no futuro.</p>
<h2 class="relative group"><a id="carregando-diretamente-de-um-objeto-tokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#carregando-diretamente-de-um-objeto-tokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Carregando diretamente de um objeto tokenizer
</span></h2>
<p>Vamos ver como aproveitar esse objeto tokenizer na biblioteca 🤗 Transformers. A classe <code>PreTrainedTokenizerFast</code> permite uma instanciação fácil, aceitando o objeto <em>tokenizer</em> instanciado como um argumento:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PreTrainedTokenizerFast
<span class="hljs-meta">>>> </span>fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)<!-- HTML_TAG_END --></pre></div>
<p>Esse objeto pode ser utilizado com todos os métodos compartilhados pelos tokenizers dos 🤗 Transformers! Vá para <a href="main_classes/tokenizer">a página do tokenizer</a> para mais informações.</p>
<h2 class="relative group"><a id="carregando-de-um-arquivo-json" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#carregando-de-um-arquivo-json"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Carregando de um arquivo JSON
</span></h2>
<p>Para carregar um tokenizer de um arquivo JSON vamos primeiro começar salvando nosso tokenizer:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenizer.save(<span class="hljs-string">"tokenizer.json"</span>)<!-- HTML_TAG_END --></pre></div>
<p>A pasta para qual salvamos esse arquivo pode ser passada para o método de inicialização do <code>PreTrainedTokenizerFast</code> usando o <code>tokenizer_file</code> parâmetro:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PreTrainedTokenizerFast
<span class="hljs-meta">>>> </span>fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file=<span class="hljs-string">"tokenizer.json"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Esse objeto pode ser utilizado com todos os métodos compartilhados pelos tokenizers dos 🤗 Transformers! Vá para <a href="main_classes/tokenizer">a página do tokenizer</a> para mais informações.</p>
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<h1 class="relative group"><a id="tour-rpido" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#tour-rpido"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Tour rápido
</span></h1>
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<p>Comece a trabalhar com 🤗 Transformers! Comece usando <code>pipeline()</code> para rápida inferência e facilmente carregue um modelo pré-treinado e um tokenizer com <a href="./model_doc/auto">AutoClass</a> para resolver tarefas de texto, visão ou áudio.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Todos os exemplos de código apresentados na documentação têm um botão no canto superior direito para escolher se você deseja ocultar ou mostrar o código no Pytorch ou no TensorFlow. Caso contrário, é esperado que funcione para ambos back-ends sem nenhuma alteração.</p></div>
<h2 class="relative group"><a id="pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipeline
</span></h2>
<p><code>pipeline()</code> é a maneira mais fácil de usar um modelo pré-treinado para uma dada tarefa.</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/tiZFewofSLM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>A <code>pipeline()</code> apoia diversas tarefas fora da caixa:</p>
<p><strong>Texto</strong>:</p>
<ul><li>Análise sentimental: classifica a polaridade de um texto.</li>
<li>Geração de texto (em Inglês): gera texto a partir de uma entrada.</li>
<li>Reconhecimento de entidade mencionada: legenda cada palavra com uma classe que a representa (pessoa, data, local, etc…) </li>
<li>Respostas: extrai uma resposta dado algum contexto e uma questão</li>
<li>Máscara de preenchimento: preenche o espaço, dado um texto com máscaras de palavras.</li>
<li>Sumarização: gera o resumo de um texto longo ou documento.</li>
<li>Tradução: traduz texto para outra língua.</li>
<li>Extração de características: cria um tensor que representa o texto.</li></ul>
<p><strong>Imagem</strong>:</p>
<ul><li>Classificação de imagens: classifica uma imagem.</li>
<li>Segmentação de imagem: classifica cada pixel da imagem.</li>
<li>Detecção de objetos: detecta objetos em uma imagem.</li></ul>
<p><strong>Audio</strong>:</p>
<ul><li>Classficação de áudio: legenda um trecho de áudio fornecido.</li>
<li>Reconhecimento de fala automático: transcreve audio em texto.</li></ul>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Para mais detalhes sobre a <code>pipeline()</code> e tarefas associadas, siga a documentação <a href="./main_classes/pipelines">aqui</a>.</p></div>
<h3 class="relative group"><a id="uso-da-pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#uso-da-pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Uso da pipeline
</span></h3>
<p>No exemplo a seguir, você usará <code>pipeline()</code> para análise sentimental.</p>
<p>Instale as seguintes dependências se você ainda não o fez:</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
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<pre><!-- HTML_TAG_START -->pip install torch<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<pre><!-- HTML_TAG_START -->pip install tensorflow<!-- HTML_TAG_END --></pre></div>
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<p>Importe <code>pipeline()</code> e especifique a tarefa que deseja completar:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>classifier = pipeline(<span class="hljs-string">"sentiment-analysis"</span>)<!-- HTML_TAG_END --></pre></div>
<p>A pipeline baixa and armazena um <a href="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english" rel="nofollow">modelo pré-treinado</a> padrão e tokenizer para análise sentimental. Agora você pode usar <code>classifier</code> no texto alvo: </p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>classifier(<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.9998</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Para mais de uma sentença, passe uma lista para a <code>pipeline()</code>, a qual retornará uma lista de dicionários:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>results = classifier([<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>])
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> result <span class="hljs-keyword">in</span> results:
<span class="hljs-meta">... </span> <span class="hljs-built_in">print</span>(<span class="hljs-string">f"label: <span class="hljs-subst">{result[<span class="hljs-string">'label'</span>]}</span>, with score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">'score'</span>], <span class="hljs-number">4</span>)}</span>"</span>)
label: POSITIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.9998</span>
label: NEGATIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.5309</span><!-- HTML_TAG_END --></pre></div>
<p>A <code>pipeline()</code> também pode iterar sobre um Dataset inteiro. Comece instalando a biblioteca de <a href="https://huggingface.co/docs/datasets/" rel="nofollow">🤗 Datasets</a>:</p>
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<pre><!-- HTML_TAG_START -->pip install datasets <!-- HTML_TAG_END --></pre></div>
<p>Crie uma <code>pipeline()</code> com a tarefa que deseja resolver e o modelo que deseja usar.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>speech_recognizer = pipeline(<span class="hljs-string">"automatic-speech-recognition"</span>, model=<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>)<!-- HTML_TAG_END --></pre></div>
<p>A seguir, carregue uma base de dados (confira a 🤗 <a href="https://huggingface.co/docs/datasets/quickstart.html" rel="nofollow">Iniciação em Datasets</a> para mais detalhes) que você gostaria de iterar sobre. Por exemplo, vamos carregar o dataset <a href="https://huggingface.co/datasets/PolyAI/minds14" rel="nofollow">MInDS-14</a>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio
<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"PolyAI/minds14"</span>, name=<span class="hljs-string">"en-US"</span>, split=<span class="hljs-string">"train"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Precisamos garantir que a taxa de amostragem do conjunto de dados corresponda à taxa de amostragem em que o facebook/wav2vec2-base-960h foi treinado.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>dataset = dataset.cast_column(<span class="hljs-string">"audio"</span>, Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))<!-- HTML_TAG_END --></pre></div>
<p>Os arquivos de áudio são carregados e re-amostrados automaticamente ao chamar a coluna <code>"audio"</code>.
Vamos extrair as arrays de formas de onda originais das primeiras 4 amostras e passá-las como uma lista para o pipeline:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>result = speech_recognizer(dataset[:<span class="hljs-number">4</span>][<span class="hljs-string">"audio"</span>])
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>([d[<span class="hljs-string">"text"</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> result])
[<span class="hljs-string">'I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT'</span>, <span class="hljs-string">"FONDERING HOW I'D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE"</span>, <span class="hljs-string">"I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE APSO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS"</span>, <span class="hljs-string">'HOW DO I TURN A JOIN A COUNT'</span>]<!-- HTML_TAG_END --></pre></div>
<p>Para um conjunto de dados maior onde as entradas são maiores (como em fala ou visão), será necessário passar um gerador em vez de uma lista que carregue todas as entradas na memória. Consulte a <a href="./main_classes/pipelines">documentação do pipeline</a> para mais informações.</p>
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<span>Use outro modelo e tokenizer na pipeline
</span></h3>
<p>A <code>pipeline()</code> pode acomodar qualquer modelo do <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>, facilitando sua adaptação para outros casos de uso. Por exemplo, se você quiser um modelo capaz de lidar com texto em francês, use as tags no Model Hub para filtrar um modelo apropriado. O principal resultado filtrado retorna um <a href="https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment" rel="nofollow">modelo BERT</a> bilíngue ajustado para análise de sentimentos. Ótimo, vamos usar este modelo!</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span><!-- HTML_TAG_END --></pre></div>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
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<span>Pytorch</span></div>
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<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>Use o <code>AutoModelForSequenceClassification</code> e <code>AutoTokenizer</code> para carregar o modelo pré-treinado e seu tokenizer associado (mais em <code>AutoClass</code> abaixo):</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Use o <code>TFAutoModelForSequenceClassification</code> and <code>AutoTokenizer</code> para carregar o modelo pré-treinado e o tokenizer associado (mais em <code>TFAutoClass</code> abaixo):</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<p>Então você pode especificar o modelo e o tokenizador na <code>pipeline()</code> e aplicar o <code>classifier</code> no seu texto alvo: </p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>classifier = pipeline(<span class="hljs-string">"sentiment-analysis"</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">>>> </span>classifier(<span class="hljs-string">"Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers."</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'5 stars'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.7273</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Se você não conseguir achar um modelo para o seu caso de uso, precisará usar fine-tune em um modelo pré-treinado nos seus dados. Veja nosso <a href="./training">tutorial de fine-tuning</a> para descobrir como. Finalmente, depois que você tiver usado esse processo em seu modelo, considere compartilhá-lo conosco (veja o tutorial <a href="./model_sharing">aqui</a>) na plataforma Model Hub afim de democratizar NLP! 🤗</p>
<h2 class="relative group"><a id="autoclass" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#autoclass"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoClass
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/AhChOFRegn4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Por baixo dos panos, as classes <code>AutoModelForSequenceClassification</code> e <code>AutoTokenizer</code> trabalham juntas para fortificar o <code>pipeline()</code>. Um <a href="./model_doc/auto">AutoClass</a> é um atalho que automaticamente recupera a arquitetura de um modelo pré-treinado a partir de seu nome ou caminho. Basta selecionar a <code>AutoClass</code> apropriada para sua tarefa e seu tokenizer associado com <code>AutoTokenizer</code>. </p>
<p>Vamos voltar ao nosso exemplo e ver como você pode usar a <code>AutoClass</code> para replicar os resultados do <code>pipeline()</code>.</p>
<h3 class="relative group"><a id="autotokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#autotokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoTokenizer
</span></h3>
<p>Um tokenizer é responsável por pré-processar o texto em um formato que seja compreensível para o modelo. Primeiro, o tokenizer dividirá o texto em palavras chamadas <em>tokens</em>. Existem várias regras que regem o processo de tokenização, incluindo como dividir uma palavra e em que nível (saiba mais sobre tokenização <a href="./tokenizer_summary">aqui</a>). A coisa mais importante a lembrar, porém, é que você precisa instanciar o tokenizer com o mesmo nome do modelo para garantir que está usando as mesmas regras de tokenização com as quais um modelo foi pré-treinado.</p>
<p>Carregue um tokenizer com <code>AutoTokenizer</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
<p>Em seguida, o tokenizer converte os tokens em números para construir um tensor como entrada para o modelo. Isso é conhecido como o <em>vocabulário</em> do modelo.</p>
<p>Passe o texto para o tokenizer:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>encoding = tokenizer(<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(encoding)
{<span class="hljs-string">'input_ids'</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">11312</span>, <span class="hljs-number">10320</span>, <span class="hljs-number">12495</span>, <span class="hljs-number">19308</span>, <span class="hljs-number">10114</span>, <span class="hljs-number">11391</span>, <span class="hljs-number">10855</span>, <span class="hljs-number">10103</span>, <span class="hljs-number">100</span>, <span class="hljs-number">58263</span>, <span class="hljs-number">13299</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
<span class="hljs-string">'token_type_ids'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'attention_mask'</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]}<!-- HTML_TAG_END --></pre></div>
<p>O tokenizer retornará um dicionário contendo:</p>
<ul><li><a href="./glossary#input-ids">input_ids</a>: representações numéricas de seus tokens.</li>
<li><a href=".glossary#attention-mask">atttention_mask</a>: indica quais tokens devem ser atendidos.</li></ul>
<p>Assim como o <code>pipeline()</code>, o tokenizer aceitará uma lista de entradas. Além disso, o tokenizer também pode preencher e truncar o texto para retornar um lote com comprimento uniforme:</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_batch = tokenizer(
<span class="hljs-meta">... </span> [<span class="hljs-string">"We are very happy to show you the 🤗 transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>],
<span class="hljs-meta">... </span> padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_batch = tokenizer(
<span class="hljs-meta">... </span> [<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>],
<span class="hljs-meta">... </span> padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"tf"</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<p>Leia o tutorial de <a href="./pr%C3%A9-processamento">pré-processamento</a> para obter mais detalhes sobre tokenização.</p>
<h3 class="relative group"><a id="automodel" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#automodel"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoModel
</span></h3>
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<span>Pytorch</span></div>
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<p>🤗 Transformers fornecem uma maneira simples e unificada de carregar instâncias pré-treinadas. Isso significa que você pode carregar um <code>AutoModel</code> como carregaria um <code>AutoTokenizer</code>. A única diferença é selecionar o <code>AutoModel</code> correto para a tarefa. Como você está fazendo classificação de texto ou sequência, carregue <code>AutoModelForSequenceClassification</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Veja o <a href="./task_summary">sumário de tarefas</a> para qual classe de <code>AutoModel</code> usar para cada tarefa.</p></div>
<p>Agora você pode passar seu grupo de entradas pré-processadas diretamente para o modelo. Você apenas tem que descompactar o dicionário usando <code>**</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_outputs = pt_model(**pt_batch)<!-- HTML_TAG_END --></pre></div>
<p>O modelo gera as ativações finais no atributo <code>logits</code>. Aplique a função softmax aos <code>logits</code> para recuperar as probabilidades:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn
<span class="hljs-meta">>>> </span>pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(pt_predictions)
tensor([[<span class="hljs-number">0.0021</span>, <span class="hljs-number">0.0018</span>, <span class="hljs-number">0.0115</span>, <span class="hljs-number">0.2121</span>, <span class="hljs-number">0.7725</span>],
[<span class="hljs-number">0.2084</span>, <span class="hljs-number">0.1826</span>, <span class="hljs-number">0.1969</span>, <span class="hljs-number">0.1755</span>, <span class="hljs-number">0.2365</span>]], grad_fn=<SoftmaxBackward0>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<p>🤗 Transformers fornecem uma maneira simples e unificada de carregar instâncias pré-treinadas. Isso significa que você pode carregar um <code>TFAutoModel</code> como carregaria um <code>AutoTokenizer</code>. A única diferença é selecionar o <code>TFAutoModel</code> correto para a tarefa. Como você está fazendo classificação de texto ou sequência, carregue <code>TFAutoModelForSequenceClassification</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Veja o <a href="./task_summary">sumário de tarefas</a> para qual classe de <code>AutoModel</code> usar para cada tarefa.</p></div>
<p>Agora você pode passar seu grupo de entradas pré-processadas diretamente para o modelo através da passagem de chaves de dicionários ao tensor.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_outputs = tf_model(tf_batch)<!-- HTML_TAG_END --></pre></div>
<p>O modelo gera as ativações finais no atributo <code>logits</code>. Aplique a função softmax aos <code>logits</code> para recuperar as probabilidades:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">>>> </span>tf_predictions<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Todos os modelos de 🤗 Transformers (PyTorch ou TensorFlow) geram tensores <em>antes</em> da função de ativação final (como softmax) pois essa função algumas vezes é fundida com a perda.</p></div>
<p>Os modelos são um standard <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow"><code>torch.nn.Module</code></a> ou um [<code>tf.keras.Model</code>](https: //<a href="http://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">www.tensorflow.org/api_docs/python/tf/keras/Model</a>) para que você possa usá-los em seu loop de treinamento habitual. No entanto, para facilitar as coisas, 🤗 Transformers fornece uma classe <code>Trainer</code> para PyTorch que adiciona funcionalidade para treinamento distribuído, precisão mista e muito mais. Para o TensorFlow, você pode usar o método <code>fit</code> de <a href="https://keras.io/" rel="nofollow">Keras</a>. Consulte o <a href="./training">tutorial de treinamento</a> para obter mais detalhes.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>As saídas do modelo 🤗 Transformers são classes de dados especiais para que seus atributos sejam preenchidos automaticamente em um IDE.
As saídas do modelo também se comportam como uma tupla ou um dicionário (por exemplo, você pode indexar com um inteiro, uma parte ou uma string), caso em que os atributos <code>None</code> são ignorados.</p></div>
<h3 class="relative group"><a id="salvar-um-modelo" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#salvar-um-modelo"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Salvar um modelo
</span></h3>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
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<span>Pytorch</span></div>
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<p>Uma vez que seu modelo estiver afinado, você pode salvá-lo com seu Tokenizer usando <code>PreTrainedModel.save_pretrained()</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_save_directory = <span class="hljs-string">"./pt_save_pretrained"</span>
<span class="hljs-meta">>>> </span>tokenizer.save_pretrained(pt_save_directory)
<span class="hljs-meta">>>> </span>pt_model.save_pretrained(pt_save_directory)<!-- HTML_TAG_END --></pre></div>
<p>Quando você estiver pronto para usá-lo novamente, recarregue com <code>PreTrainedModel.from_pretrained()</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"./pt_save_pretrained"</span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<p>Uma vez que seu modelo estiver afinado, você pode salvá-lo com seu Tokenizer usando <code>TFPreTrainedModel.save_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_save_directory = <span class="hljs-string">"./tf_save_pretrained"</span>
<span class="hljs-meta">>>> </span>tokenizer.save_pretrained(tf_save_directory)
<span class="hljs-meta">>>> </span>tf_model.save_pretrained(tf_save_directory)<!-- HTML_TAG_END --></pre></div>
<p>Quando você estiver pronto para usá-lo novamente, recarregue com <code>TFPreTrainedModel.from_pretrained()</code></p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"./tf_save_pretrained"</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<p>Um recurso particularmente interessante dos 🤗 Transformers é a capacidade de salvar um modelo e recarregá-lo como um modelo PyTorch ou TensorFlow. Use <code>from_pt</code> ou <code>from_tf</code> para converter o modelo de um framework para outro:</p>
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<span>Pytorch</span></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
<span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<span>Hide TensorFlow content</span></div></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
<span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
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<h1 class="relative group"><a id="transformers" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>🤗 Transformers
</span></h1>
<p>Estado da Arte para Aprendizado de Máquina em PyTorch, TensorFlow e JAX.
O 🤗 Transformers disponibiliza APIs para facilmente baixar e treinar modelos pré-treinados de última geração.
O uso de modelos pré-treinados pode diminuir os seus custos de computação, a sua pegada de carbono, além de economizar o
tempo necessário para se treinar um modelo do zero. Os modelos podem ser usados para diversas tarefas:</p>
<ul><li>📝 Textos: classificação, extração de informações, perguntas e respostas, resumir, traduzir e gerar textos em mais de 100 idiomas.</li>
<li>🖼 Imagens: classificação, deteção de objetos, e segmentação.</li>
<li>🗣 Audio: reconhecimento de fala e classificação de áudio.</li>
<li>🐙 Multimodal: perguntas tabeladas e respsostas, reconhecimento ótico de charactéres, extração de informação de
documentos escaneados, classificação de vídeo, perguntas e respostas visuais.</li></ul>
<p>Nossa biblioteca aceita integração contínua entre três das bibliotecas mais populares de aprendizado profundo:
Our library supports seamless integration between three of the most popular deep learning libraries:
<a href="https://pytorch.org/" rel="nofollow">PyTorch</a>, <a href="https://www.tensorflow.org/" rel="nofollow">TensorFlow</a> e <a href="https://jax.readthedocs.io/en/latest/" rel="nofollow">JAX</a>.
Treine seu modelo em três linhas de código em um framework, e carregue-o para execução em outro.</p>
<p>Cada arquitetura 🤗 Transformers é definida em um módulo individual do Python, para que seja facilmente customizável para pesquisa e experimentos.</p>
<h2 class="relative group"><a id="se-voc-estiver-procurando-suporte-do-time-da-hugging-face-acesse" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#se-voc-estiver-procurando-suporte-do-time-da-hugging-face-acesse"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Se você estiver procurando suporte do time da Hugging Face, acesse
</span></h2>
<a target="_blank" href="https://huggingface.co/support"><img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">&lt;/img>
</a><br>
<h2 class="relative group"><a id="contedo" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#contedo"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Conteúdo
</span></h2>
<p>A documentação é dividida em cinco partes:</p>
<ul><li><p><strong>INÍCIO</strong> contém um tour rápido de instalação e instruções para te dar um empurrão inicial com os 🤗 Transformers.</p></li>
<li><p><strong>TUTORIAIS</strong> são perfeitos para começar a aprender sobre a nossa biblioteca. Essa seção irá te ajudar a desenvolver
habilidades básicas necessárias para usar o 🤗 Transformers.</p></li>
<li><p><strong>GUIAS PRÁTICOS</strong> irão te mostrar como alcançar um certo objetivo, como o fine-tuning de um modelo pré-treinado
para modelamento de idioma, ou como criar um cabeçalho personalizado para um modelo.</p></li>
<li><p><strong>GUIAS CONCEITUAIS</strong> te darão mais discussões e explicações dos conceitos fundamentais e idéias por trás dos modelos,
tarefas e da filosofia de design por trás do 🤗 Transformers.</p></li>
<li><p><strong>API</strong> descreve o funcionamento de cada classe e função, agrupada em:</p></li>
<li><p><strong>CLASSES PRINCIPAIS</strong> para as classes que expõe as APIs importantes da biblioteca.</p></li>
<li><p><strong>MODELOS</strong> para as classes e funções relacionadas à cada modelo implementado na biblioteca.</p></li>
<li><p><strong>AUXILIARES INTERNOS</strong> para as classes e funções usadas internamente.</p></li></ul>
<p>Atualmente a biblioteca contém implementações do PyTorch, TensorFlow e JAX, pesos para modelos pré-treinados e scripts de uso e conversão de utilidades para os seguintes modelos:</p>
<h3 class="relative group"><a id="modelos-atuais" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#modelos-atuais"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Modelos atuais
</span></h3>
<ol><li><strong><a href="model_doc/albert">ALBERT</a></strong> (from Google Research and the Toyota Technological Institute at Chicago) released with the paper <a href="https://arxiv.org/abs/1909.11942" rel="nofollow">ALBERT: A Lite BERT for Self-supervised Learning of Language Representations</a>, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.</li>
<li><strong><a href="model_doc/bart">BART</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension</a> by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.</li>
<li><strong><a href="model_doc/barthez">BARThez</a></strong> (from École polytechnique) released with the paper <a href="https://arxiv.org/abs/2010.12321" rel="nofollow">BARThez: a Skilled Pretrained French Sequence-to-Sequence Model</a> by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.</li>
<li><strong><a href="model_doc/bartpho">BARTpho</a></strong> (from VinAI Research) released with the paper <a href="https://arxiv.org/abs/2109.09701" rel="nofollow">BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese</a> by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.</li>
<li><strong><a href="model_doc/beit">BEiT</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2106.08254" rel="nofollow">BEiT: BERT Pre-Training of Image Transformers</a> by Hangbo Bao, Li Dong, Furu Wei.</li>
<li><strong><a href="model_doc/bert">BERT</a></strong> (from Google) released with the paper <a href="https://arxiv.org/abs/1810.04805" rel="nofollow">BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding</a> by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.</li>
<li><strong><a href="model_doc/bertweet">BERTweet</a></strong> (from VinAI Research) released with the paper <a href="https://aclanthology.org/2020.emnlp-demos.2/" rel="nofollow">BERTweet: A pre-trained language model for English Tweets</a> by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.</li>
<li><strong><a href="model_doc/bert-generation">BERT For Sequence Generation</a></strong> (from Google) released with the paper <a href="https://arxiv.org/abs/1907.12461" rel="nofollow">Leveraging Pre-trained Checkpoints for Sequence Generation Tasks</a> by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.</li>
<li><strong><a href="model_doc/big_bird">BigBird-RoBERTa</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2007.14062" rel="nofollow">Big Bird: Transformers for Longer Sequences</a> by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.</li>
<li><strong><a href="model_doc/bigbird_pegasus">BigBird-Pegasus</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2007.14062" rel="nofollow">Big Bird: Transformers for Longer Sequences</a> by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.</li>
<li><strong><a href="model_doc/blenderbot">Blenderbot</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2004.13637" rel="nofollow">Recipes for building an open-domain chatbot</a> by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.</li>
<li><strong><a href="model_doc/blenderbot-small">BlenderbotSmall</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2004.13637" rel="nofollow">Recipes for building an open-domain chatbot</a> by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.</li>
<li><strong><a href="model_doc/bort">BORT</a></strong> (from Alexa) released with the paper <a href="https://arxiv.org/abs/2010.10499" rel="nofollow">Optimal Subarchitecture Extraction For BERT</a> by Adrian de Wynter and Daniel J. Perry.</li>
<li><strong><a href="model_doc/byt5">ByT5</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2105.13626" rel="nofollow">ByT5: Towards a token-free future with pre-trained byte-to-byte models</a> by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.</li>
<li><strong><a href="model_doc/camembert">CamemBERT</a></strong> (from Inria/Facebook/Sorbonne) released with the paper <a href="https://arxiv.org/abs/1911.03894" rel="nofollow">CamemBERT: a Tasty French Language Model</a> by Louis Martin<em>, Benjamin Muller</em>, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.</li>
<li><strong><a href="model_doc/canine">CANINE</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2103.06874" rel="nofollow">CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation</a> by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.</li>
<li><strong><a href="model_doc/convnext">ConvNeXT</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2201.03545" rel="nofollow">A ConvNet for the 2020s</a> by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.</li>
<li><strong><a href="model_doc/clip">CLIP</a></strong> (from OpenAI) released with the paper <a href="https://arxiv.org/abs/2103.00020" rel="nofollow">Learning Transferable Visual Models From Natural Language Supervision</a> by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.</li>
<li><strong><a href="model_doc/convbert">ConvBERT</a></strong> (from YituTech) released with the paper <a href="https://arxiv.org/abs/2008.02496" rel="nofollow">ConvBERT: Improving BERT with Span-based Dynamic Convolution</a> by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.</li>
<li><strong><a href="model_doc/cpm">CPM</a></strong> (from Tsinghua University) released with the paper <a href="https://arxiv.org/abs/2012.00413" rel="nofollow">CPM: A Large-scale Generative Chinese Pre-trained Language Model</a> by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.</li>
<li><strong><a href="model_doc/ctrl">CTRL</a></strong> (from Salesforce) released with the paper <a href="https://arxiv.org/abs/1909.05858" rel="nofollow">CTRL: A Conditional Transformer Language Model for Controllable Generation</a> by Nitish Shirish Keskar<em>, Bryan McCann</em>, Lav R. Varshney, Caiming Xiong and Richard Socher.</li>
<li><strong><a href="model_doc/data2vec">Data2Vec</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2202.03555" rel="nofollow">Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language</a> by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.</li>
<li><strong><a href="model_doc/deberta">DeBERTa</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2006.03654" rel="nofollow">DeBERTa: Decoding-enhanced BERT with Disentangled Attention</a> by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.</li>
<li><strong><a href="model_doc/deberta-v2">DeBERTa-v2</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2006.03654" rel="nofollow">DeBERTa: Decoding-enhanced BERT with Disentangled Attention</a> by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.</li>
<li><strong><a href="model_doc/decision_transformer">Decision Transformer</a></strong> (from Berkeley/Facebook/Google) released with the paper <a href="https://arxiv.org/abs/2106.01345" rel="nofollow">Decision Transformer: Reinforcement Learning via Sequence Modeling</a> by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.</li>
<li><strong><a href="model_doc/dit">DiT</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2203.02378" rel="nofollow">DiT: Self-supervised Pre-training for Document Image Transformer</a> by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.</li>
<li><strong><a href="model_doc/deit">DeiT</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2012.12877" rel="nofollow">Training data-efficient image transformers & distillation through attention</a> by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.</li>
<li><strong><a href="model_doc/detr">DETR</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2005.12872" rel="nofollow">End-to-End Object Detection with Transformers</a> by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.</li>
<li><strong><a href="model_doc/dialogpt">DialoGPT</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/1911.00536" rel="nofollow">DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation</a> by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.</li>
<li><strong><a href="model_doc/distilbert">DistilBERT</a></strong> (from HuggingFace), released together with the paper <a href="https://arxiv.org/abs/1910.01108" rel="nofollow">DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter</a> by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">DistilGPT2</a>, RoBERTa into <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">DistilRoBERTa</a>, Multilingual BERT into <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">DistilmBERT</a> and a German version of DistilBERT.</li>
<li><strong><a href="model_doc/dpr">DPR</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2004.04906" rel="nofollow">Dense Passage Retrieval for Open-Domain Question Answering</a> by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.</li>
<li><strong><a href="master/model_doc/dpt">DPT</a></strong> (from Intel Labs) released with the paper <a href="https://arxiv.org/abs/2103.13413" rel="nofollow">Vision Transformers for Dense Prediction</a> by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.</li>
<li><strong><a href="model_doc/encoder-decoder">EncoderDecoder</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/1907.12461" rel="nofollow">Leveraging Pre-trained Checkpoints for Sequence Generation Tasks</a> by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.</li>
<li><strong><a href="model_doc/electra">ELECTRA</a></strong> (from Google Research/Stanford University) released with the paper <a href="https://arxiv.org/abs/2003.10555" rel="nofollow">ELECTRA: Pre-training text encoders as discriminators rather than generators</a> by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.</li>
<li><strong><a href="model_doc/flaubert">FlauBERT</a></strong> (from CNRS) released with the paper <a href="https://arxiv.org/abs/1912.05372" rel="nofollow">FlauBERT: Unsupervised Language Model Pre-training for French</a> by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.</li>
<li><strong><a href="model_doc/fnet">FNet</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2105.03824" rel="nofollow">FNet: Mixing Tokens with Fourier Transforms</a> by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.</li>
<li><strong><a href="model_doc/funnel">Funnel Transformer</a></strong> (from CMU/Google Brain) released with the paper <a href="https://arxiv.org/abs/2006.03236" rel="nofollow">Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing</a> by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.</li>
<li><strong><a href="model_doc/glpn">GLPN</a></strong> (from KAIST) released with the paper <a href="https://arxiv.org/abs/2201.07436" rel="nofollow">Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth</a> by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.</li>
<li><strong><a href="model_doc/openai-gpt">GPT</a></strong> (from OpenAI) released with the paper <a href="https://blog.openai.com/language-unsupervised/" rel="nofollow">Improving Language Understanding by Generative Pre-Training</a> by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.</li>
<li><strong><a href="model_doc/gpt2">GPT-2</a></strong> (from OpenAI) released with the paper <a href="https://blog.openai.com/better-language-models/" rel="nofollow">Language Models are Unsupervised Multitask Learners</a> by Alec Radford<em>, Jeffrey Wu</em>, Rewon Child, David Luan, Dario Amodei<strong>and Ilya Sutskever</strong>.</li>
<li><strong><a href="model_doc/gptj">GPT-J</a></strong> (from EleutherAI) released in the repository <a href="https://github.com/kingoflolz/mesh-transformer-jax/" rel="nofollow">kingoflolz/mesh-transformer-jax</a> by Ben Wang and Aran Komatsuzaki.</li>
<li><strong><a href="model_doc/gpt_neo">GPT Neo</a></strong> (from EleutherAI) released in the repository <a href="https://github.com/EleutherAI/gpt-neo" rel="nofollow">EleutherAI/gpt-neo</a> by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.</li>
<li><strong><a href="model_doc/hubert">Hubert</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2106.07447" rel="nofollow">HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units</a> by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.</li>
<li><strong><a href="model_doc/ibert">I-BERT</a></strong> (from Berkeley) released with the paper <a href="https://arxiv.org/abs/2101.01321" rel="nofollow">I-BERT: Integer-only BERT Quantization</a> by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.</li>
<li><strong><a href="model_doc/imagegpt">ImageGPT</a></strong> (from OpenAI) released with the paper <a href="https://openai.com/blog/image-gpt/" rel="nofollow">Generative Pretraining from Pixels</a> by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.</li>
<li><strong><a href="model_doc/layoutlm">LayoutLM</a></strong> (from Microsoft Research Asia) released with the paper <a href="https://arxiv.org/abs/1912.13318" rel="nofollow">LayoutLM: Pre-training of Text and Layout for Document Image Understanding</a> by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.</li>
<li><strong><a href="model_doc/layoutlmv2">LayoutLMv2</a></strong> (from Microsoft Research Asia) released with the paper <a href="https://arxiv.org/abs/2012.14740" rel="nofollow">LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding</a> by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.</li>
<li><strong><a href="model_doc/layoutxlm">LayoutXLM</a></strong> (from Microsoft Research Asia) released with the paper <a href="https://arxiv.org/abs/2104.08836" rel="nofollow">LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding</a> by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.</li>
<li><strong><a href="model_doc/led">LED</a></strong> (from AllenAI) released with the paper <a href="https://arxiv.org/abs/2004.05150" rel="nofollow">Longformer: The Long-Document Transformer</a> by Iz Beltagy, Matthew E. Peters, Arman Cohan.</li>
<li><strong><a href="model_doc/longformer">Longformer</a></strong> (from AllenAI) released with the paper <a href="https://arxiv.org/abs/2004.05150" rel="nofollow">Longformer: The Long-Document Transformer</a> by Iz Beltagy, Matthew E. Peters, Arman Cohan.</li>
<li><strong><a href="model_doc/luke">LUKE</a></strong> (from Studio Ousia) released with the paper <a href="https://arxiv.org/abs/2010.01057" rel="nofollow">LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention</a> by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.</li>
<li><strong><a href="model_doc/mluke">mLUKE</a></strong> (from Studio Ousia) released with the paper <a href="https://arxiv.org/abs/2110.08151" rel="nofollow">mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models</a> by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.</li>
<li><strong><a href="model_doc/lxmert">LXMERT</a></strong> (from UNC Chapel Hill) released with the paper <a href="https://arxiv.org/abs/1908.07490" rel="nofollow">LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering</a> by Hao Tan and Mohit Bansal.</li>
<li><strong><a href="model_doc/m2m_100">M2M100</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2010.11125" rel="nofollow">Beyond English-Centric Multilingual Machine Translation</a> by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.</li>
<li><strong><a href="model_doc/marian">MarianMT</a></strong> Machine translation models trained using <a href="http://opus.nlpl.eu/" rel="nofollow">OPUS</a> data by Jörg Tiedemann. The <a href="https://marian-nmt.github.io/" rel="nofollow">Marian Framework</a> is being developed by the Microsoft Translator Team.</li>
<li><strong><a href="model_doc/maskformer">MaskFormer</a></strong> (from Meta and UIUC) released with the paper <a href="https://arxiv.org/abs/2107.06278" rel="nofollow">Per-Pixel Classification is Not All You Need for Semantic Segmentation</a> by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.</li>
<li><strong><a href="model_doc/mbart">MBart</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2001.08210" rel="nofollow">Multilingual Denoising Pre-training for Neural Machine Translation</a> by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.</li>
<li><strong><a href="model_doc/mbart">MBart-50</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2008.00401" rel="nofollow">Multilingual Translation with Extensible Multilingual Pretraining and Finetuning</a> by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.</li>
<li><strong><a href="model_doc/megatron-bert">Megatron-BERT</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/1909.08053" rel="nofollow">Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism</a> by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.</li>
<li><strong><a href="model_doc/megatron_gpt2">Megatron-GPT2</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/1909.08053" rel="nofollow">Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism</a> by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.</li>
<li><strong><a href="model_doc/mpnet">MPNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2004.09297" rel="nofollow">MPNet: Masked and Permuted Pre-training for Language Understanding</a> by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.</li>
<li><strong><a href="model_doc/mt5">MT5</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2010.11934" rel="nofollow">mT5: A massively multilingual pre-trained text-to-text transformer</a> by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.</li>
<li><strong><a href="model_doc/nystromformer">Nyströmformer</a></strong> (from the University of Wisconsin - Madison) released with the paper <a href="https://arxiv.org/abs/2102.03902" rel="nofollow">Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention</a> by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.</li>
<li><strong><a href="model_doc/pegasus">Pegasus</a></strong> (from Google) released with the paper <a href="https://arxiv.org/abs/1912.08777" rel="nofollow">PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization</a> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.</li>
<li><strong><a href="model_doc/perceiver">Perceiver IO</a></strong> (from Deepmind) released with the paper <a href="https://arxiv.org/abs/2107.14795" rel="nofollow">Perceiver IO: A General Architecture for Structured Inputs & Outputs</a> by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.</li>
<li><strong><a href="model_doc/phobert">PhoBERT</a></strong> (from VinAI Research) released with the paper <a href="https://www.aclweb.org/anthology/2020.findings-emnlp.92/" rel="nofollow">PhoBERT: Pre-trained language models for Vietnamese</a> by Dat Quoc Nguyen and Anh Tuan Nguyen.</li>
<li><strong><a href="model_doc/plbart">PLBart</a></strong> (from UCLA NLP) released with the paper <a href="https://arxiv.org/abs/2103.06333" rel="nofollow">Unified Pre-training for Program Understanding and Generation</a> by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.</li>
<li><strong><a href="model_doc/poolformer">PoolFormer</a></strong> (from Sea AI Labs) released with the paper <a href="https://arxiv.org/abs/2111.11418" rel="nofollow">MetaFormer is Actually What You Need for Vision</a> by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.</li>
<li><strong><a href="model_doc/prophetnet">ProphetNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2001.04063" rel="nofollow">ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training</a> by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.</li>
<li><strong><a href="model_doc/qdqbert">QDQBert</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/2004.09602" rel="nofollow">Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation</a> by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.</li>
<li><strong><a href="model_doc/realm.html">REALM</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2002.08909" rel="nofollow">REALM: Retrieval-Augmented Language Model Pre-Training</a> by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.</li>
<li><strong><a href="model_doc/reformer">Reformer</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2001.04451" rel="nofollow">Reformer: The Efficient Transformer</a> by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.</li>
<li><strong><a href="model_doc/rembert">RemBERT</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2010.12821" rel="nofollow">Rethinking embedding coupling in pre-trained language models</a> by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.</li>
<li><strong><a href="model_doc/regnet">RegNet</a></strong> (from META Platforms) released with the paper <a href="https://arxiv.org/abs/2003.13678" rel="nofollow">Designing Network Design Space</a> by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.</li>
<li><strong><a href="model_doc/resnet">ResNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/1512.03385" rel="nofollow">Deep Residual Learning for Image Recognition</a> by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.</li>
<li><strong><a href="model_doc/roberta">RoBERTa</a></strong> (from Facebook), released together with the paper <a href="https://arxiv.org/abs/1907.11692" rel="nofollow">RoBERTa: A Robustly Optimized BERT Pretraining Approach</a> by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.</li>
<li><strong><a href="model_doc/roformer">RoFormer</a></strong> (from ZhuiyiTechnology), released together with the paper <a href="https://arxiv.org/abs/2104.09864" rel="nofollow">RoFormer: Enhanced Transformer with Rotary Position Embedding</a> by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.</li>
<li><strong><a href="model_doc/segformer">SegFormer</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/2105.15203" rel="nofollow">SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers</a> by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.</li>
<li><strong><a href="model_doc/sew">SEW</a></strong> (from ASAPP) released with the paper <a href="https://arxiv.org/abs/2109.06870" rel="nofollow">Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition</a> by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.</li>
<li><strong><a href="model_doc/sew_d">SEW-D</a></strong> (from ASAPP) released with the paper <a href="https://arxiv.org/abs/2109.06870" rel="nofollow">Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition</a> by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.</li>
<li><strong><a href="model_doc/speech_to_text">SpeechToTextTransformer</a></strong> (from Facebook), released together with the paper <a href="https://arxiv.org/abs/2010.05171" rel="nofollow">fairseq S2T: Fast Speech-to-Text Modeling with fairseq</a> by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.</li>
<li><strong><a href="model_doc/speech_to_text_2">SpeechToTextTransformer2</a></strong> (from Facebook), released together with the paper <a href="https://arxiv.org/abs/2104.06678" rel="nofollow">Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a> by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.</li>
<li><strong><a href="model_doc/splinter">Splinter</a></strong> (from Tel Aviv University), released together with the paper <a href="https://arxiv.org/abs/2101.00438" rel="nofollow">Few-Shot Question Answering by Pretraining Span Selection</a> by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.</li>
<li><strong><a href="model_doc/squeezebert">SqueezeBert</a></strong> (from Berkeley) released with the paper <a href="https://arxiv.org/abs/2006.11316" rel="nofollow">SqueezeBERT: What can computer vision teach NLP about efficient neural networks?</a> by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.</li>
<li><strong><a href="model_doc/swin">Swin Transformer</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2103.14030" rel="nofollow">Swin Transformer: Hierarchical Vision Transformer using Shifted Windows</a> by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.</li>
<li><strong><a href="model_doc/t5">T5</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/1910.10683" rel="nofollow">Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer</a> by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.</li>
<li><strong><a href="model_doc/t5v1.1">T5v1.1</a></strong> (from Google AI) released in the repository <a href="https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511" rel="nofollow">google-research/text-to-text-transfer-transformer</a> by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.</li>
<li><strong><a href="model_doc/tapas">TAPAS</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2004.02349" rel="nofollow">TAPAS: Weakly Supervised Table Parsing via Pre-training</a> by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.</li>
<li><strong><a href="model_doc/tapex">TAPEX</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2107.07653" rel="nofollow">TAPEX: Table Pre-training via Learning a Neural SQL Executor</a> by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.</li>
<li><strong><a href="model_doc/transfo-xl">Transformer-XL</a></strong> (from Google/CMU) released with the paper <a href="https://arxiv.org/abs/1901.02860" rel="nofollow">Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context</a> by Zihang Dai<em>, Zhilin Yang</em>, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.</li>
<li><strong><a href="model_doc/trocr">TrOCR</a></strong> (from Microsoft), released together with the paper <a href="https://arxiv.org/abs/2109.10282" rel="nofollow">TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.</li>
<li><strong><a href="model_doc/unispeech">UniSpeech</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2101.07597" rel="nofollow">UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data</a> by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.</li>
<li><strong><a href="model_doc/unispeech-sat">UniSpeechSat</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2110.05752" rel="nofollow">UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING</a> by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.</li>
<li><strong><a href="model_doc/van">VAN</a></strong> (from Tsinghua University and Nankai University) released with the paper <a href="https://arxiv.org/abs/2202.09741" rel="nofollow">Visual Attention Network</a> by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.</li>
<li><strong><a href="model_doc/vilt">ViLT</a></strong> (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper <a href="https://arxiv.org/abs/2102.03334" rel="nofollow">ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision</a> by Wonjae Kim, Bokyung Son, Ildoo Kim.</li>
<li><strong><a href="model_doc/vit">Vision Transformer (ViT)</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2010.11929" rel="nofollow">An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale</a> by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.</li>
<li><strong><a href="model_doc/vit_mae">ViTMAE</a></strong> (from Meta AI) released with the paper <a href="https://arxiv.org/abs/2111.06377" rel="nofollow">Masked Autoencoders Are Scalable Vision Learners</a> by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.</li>
<li><strong><a href="model_doc/visual_bert">VisualBERT</a></strong> (from UCLA NLP) released with the paper <a href="https://arxiv.org/pdf/1908.03557" rel="nofollow">VisualBERT: A Simple and Performant Baseline for Vision and Language</a> by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.</li>
<li><strong><a href="model_doc/wavlm">WavLM</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2110.13900" rel="nofollow">WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing</a> by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.</li>
<li><strong><a href="model_doc/wav2vec2">Wav2Vec2</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.</li>
<li><strong><a href="model_doc/wav2vec2_phoneme">Wav2Vec2Phoneme</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2109.11680" rel="nofollow">Simple and Effective Zero-shot Cross-lingual Phoneme Recognition</a> by Qiantong Xu, Alexei Baevski, Michael Auli.</li>
<li><strong><a href="model_doc/xglm">XGLM</a></strong> (From Facebook AI) released with the paper <a href="https://arxiv.org/abs/2112.10668" rel="nofollow">Few-shot Learning with Multilingual Language Models</a> by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.</li>
<li><strong><a href="model_doc/xlm">XLM</a></strong> (from Facebook) released together with the paper <a href="https://arxiv.org/abs/1901.07291" rel="nofollow">Cross-lingual Language Model Pretraining</a> by Guillaume Lample and Alexis Conneau.</li>
<li><strong><a href="model_doc/xlm-prophetnet">XLM-ProphetNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2001.04063" rel="nofollow">ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training</a> by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.</li>
<li><strong><a href="model_doc/xlm-roberta">XLM-RoBERTa</a></strong> (from Facebook AI), released together with the paper <a href="https://arxiv.org/abs/1911.02116" rel="nofollow">Unsupervised Cross-lingual Representation Learning at Scale</a> by Alexis Conneau<em>, Kartikay Khandelwal</em>, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.</li>
<li><strong><a href="model_doc/xlm-roberta-xl">XLM-RoBERTa-XL</a></strong> (from Facebook AI), released together with the paper <a href="https://arxiv.org/abs/2105.00572" rel="nofollow">Larger-Scale Transformers for Multilingual Masked Language Modeling</a> by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.</li>
<li><strong><a href="model_doc/xlnet">XLNet</a></strong> (from Google/CMU) released with the paper <a href="https://arxiv.org/abs/1906.08237" rel="nofollow">XLNet: Generalized Autoregressive Pretraining for Language Understanding</a> by Zhilin Yang<em>, Zihang Dai</em>, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.</li>
<li><strong><a href="model_doc/xlsr_wav2vec2">XLSR-Wav2Vec2</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2006.13979" rel="nofollow">Unsupervised Cross-Lingual Representation Learning For Speech Recognition</a> by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.</li>
<li><strong><a href="model_doc/xls_r">XLS-R</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2111.09296" rel="nofollow">XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale</a> by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.</li>
<li><strong><a href="model_doc/yoso">YOSO</a></strong> (from the University of Wisconsin - Madison) released with the paper <a href="https://arxiv.org/abs/2111.09714" rel="nofollow">You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling</a> by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.</li></ol>
<h3 class="relative group"><a id="frameworks-aceitos" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#frameworks-aceitos"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Frameworks aceitos
</span></h3>
<p>A tabela abaixo representa a lista de suporte na biblioteca para cada um dos seguintes modelos, caso tenham um tokenizer
do Python (chamado de “slow”), ou um tokenizer construído em cima da biblioteca 🤗 Tokenizers (chamado de “fast”). Além
disso, são diferenciados pelo suporte em diferentes frameworks: JAX (por meio do Flax); PyTorch; e/ou Tensorflow.</p>
<table><thead><tr><th align="center">Model</th>
<th align="center">Tokenizer slow</th>
<th align="center">Tokenizer fast</th>
<th align="center">PyTorch support</th>
<th align="center">TensorFlow support</th>
<th align="center">Flax Support</th></tr></thead>
<tbody><tr><td align="center">ALBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">BART</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">BEiT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">BERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Bert Generation</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">BigBird</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">BigBirdPegasus</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Blenderbot</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">BlenderbotSmall</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">CamemBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Canine</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">CLIP</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">ConvBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">ConvNext</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">CTRL</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Data2VecAudio</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Data2VecText</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Data2VecVision</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">DeBERTa</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">DeBERTa-v2</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Decision Transformer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">DeiT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">DETR</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">DistilBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">DPR</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">DPT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ELECTRA</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Encoder decoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">FairSeq Machine-Translation</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">FlauBERT</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">FNet</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Funnel Transformer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">GLPN</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">GPT Neo</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">GPT-J</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Hubert</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">I-BERT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ImageGPT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">LayoutLM</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">LayoutLMv2</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">LED</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Longformer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">LUKE</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">LXMERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">M2M100</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Marian</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">MaskFormer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">mBART</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">MegatronBert</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">MobileBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">MPNet</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">mT5</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Nystromformer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">OpenAI GPT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">OpenAI GPT-2</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Pegasus</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Perceiver</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">PLBart</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">PoolFormer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ProphetNet</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">QDQBert</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RAG</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Realm</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Reformer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RegNet</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RemBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">ResNet</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RetriBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RoBERTa</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">RoFormer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">SegFormer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">SEW</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">SEW-D</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Speech Encoder decoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">Speech2Text</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Speech2Text2</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Splinter</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">SqueezeBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Swin</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">T5</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">TAPAS</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">TAPEX</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Transformer-XL</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">TrOCR</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">UniSpeech</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">UniSpeechSat</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">VAN</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ViLT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Vision Encoder decoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">VisionTextDualEncoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">VisualBert</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ViT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">ViTMAE</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Wav2Vec2</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">WavLM</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">XGLM</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">XLM</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">XLM-RoBERTa</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">XLM-RoBERTa-XL</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">XLMProphetNet</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">XLNet</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">YOSO</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr></tbody></table>
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<h1 class="relative group"><a id="treinamento-distribudo-com-o-accelerate" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#treinamento-distribudo-com-o-accelerate"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Treinamento distribuído com o 🤗 Accelerate
</span></h1>
<p>O paralelismo surgiu como uma estratégia para treinar modelos grandes em hardware limitado e aumentar a velocidade
de treinamento em várias órdens de magnitude. Na Hugging Face criamos a biblioteca <a href="https://huggingface.co/docs/accelerate" rel="nofollow">🤗 Accelerate</a>
para ajudar os usuários a treinar modelos 🤗 Transformers com qualquer configuração distribuída, seja em uma máquina
com múltiplos GPUs ou em múltiplos GPUs distribuidos entre muitas máquinas. Neste tutorial, você irá aprender como
personalizar seu laço de treinamento de PyTorch para poder treinar em ambientes distribuídos.</p>
<h2 class="relative group"><a id="configurao" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#configurao"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Configuração
</span></h2>
<p>De início, instale o 🤗 Accelerate:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install accelerate<!-- HTML_TAG_END --></pre></div>
<p>Logo, devemos importar e criar um objeto <a href="https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator" rel="nofollow"><code>Accelerator</code></a>.
O <code>Accelerator</code> detectará automáticamente a configuração distribuída disponível e inicializará todos os
componentes necessários para o treinamento. Não há necessidade portanto de especificar o dispositivo onde deve colocar seu modelo.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> Accelerator
<span class="hljs-meta">>>> </span>accelerator = Accelerator()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="preparando-a-acelerao" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preparando-a-acelerao"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preparando a aceleração
</span></h2>
<p>Passe todos os objetos relevantes ao treinamento para o método <a href="https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.prepare" rel="nofollow"><code>prepare</code></a>.
Isto inclui os DataLoaders de treino e evaluação, um modelo e um otimizador:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
<span class="hljs-meta">... </span> train_dataloader, eval_dataloader, model, optimizer
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="backward" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#backward"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Backward
</span></h2>
<p>Por último, substitua o <code>loss.backward()</code> padrão em seu laço de treinamento com o método <a href="https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.backward" rel="nofollow"><code>backward</code></a> do 🤗 Accelerate:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_epochs):
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
<span class="hljs-meta">... </span> outputs = model(**batch)
<span class="hljs-meta">... </span> loss = outputs.loss
<span class="hljs-meta">... </span> accelerator.backward(loss)
<span class="hljs-meta">... </span> optimizer.step()
<span class="hljs-meta">... </span> lr_scheduler.step()
<span class="hljs-meta">... </span> optimizer.zero_grad()
<span class="hljs-meta">... </span> progress_bar.update(<span class="hljs-number">1</span>)<!-- HTML_TAG_END --></pre></div>
<p>Como se poder ver no seguinte código, só precisará adicionar quatro linhas de código ao seu laço de treinamento
para habilitar o treinamento distribuído!</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-addition">+ from accelerate import Accelerator</span>
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
<span class="hljs-addition">+ accelerator = Accelerator()</span>
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
<span class="hljs-deletion">- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")</span>
<span class="hljs-deletion">- model.to(device)</span>
<span class="hljs-addition">+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(</span>
<span class="hljs-addition">+ train_dataloader, eval_dataloader, model, optimizer</span>
<span class="hljs-addition">+ )</span>
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
<span class="hljs-deletion">- batch = {k: v.to(device) for k, v in batch.items()}</span>
outputs = model(**batch)
loss = outputs.loss
<span class="hljs-deletion">- loss.backward()</span>
<span class="hljs-addition">+ accelerator.backward(loss)</span>
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="treinamento" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#treinamento"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Treinamento
</span></h2>
<p>Quando tiver adicionado as linhas de código relevantes, inicie o treinamento por um script ou notebook como o Colab.</p>
<h3 class="relative group"><a id="treinamento-em-um-script" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#treinamento-em-um-script"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Treinamento em um Script
</span></h3>
<p>Se estiver rodando seu treinamento em um Script, execute o seguinte comando para criar e guardar um arquivo de configuração:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->accelerate config<!-- HTML_TAG_END --></pre></div>
<p>Comece o treinamento com:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->accelerate launch train.py<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="treinamento-em-um-notebook" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#treinamento-em-um-notebook"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Treinamento em um Notebook
</span></h3>
<p>O 🤗 Accelerate pode rodar em um notebook, por exemplo, se estiver planejando usar as TPUs do Google Colab.
Encapsule o código responsável pelo treinamento de uma função e passe-o ao <code>notebook_launcher</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> notebook_launcher
<span class="hljs-meta">>>> </span>notebook_launcher(training_function)<!-- HTML_TAG_END --></pre></div>
<p>Para obter mais informações sobre o 🤗 Accelerate e suas numerosas funções, consulte a <a href="https://huggingface.co/docs/accelerate/index" rel="nofollow">documentación</a>.</p>
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<h1 class="relative group"><a id="finetuning-de-um-modelo-prtreinado" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetuning-de-um-modelo-prtreinado"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tuning de um modelo pré-treinado
</span></h1>
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<p>O uso de um modelo pré-treinado tem importantes vantagens. Redução do custo computacional, a pegada de carbono, e te
permite utilizar modelos de última geração sem ter que treinar um novo desde o início.
O 🤗 Transformers proporciona acesso a milhares de modelos pré-treinados numa ampla gama de tarefas.
Quando utilizar um modelo pré-treinado, treine-o com um dataset específico para a sua tarefa.
Isto é chamado de fine-tuning, uma técnica de treinamento incrivelmente poderosa. Neste tutorial faremos o fine-tuning
de um modelo pré-treinado com um framework de Deep Learning da sua escolha:</p>
<ul><li>Fine-tuning de um modelo pré-treinado com o 🤗 Transformers <code>Trainer</code>.</li>
<li>Fine-tuning de um modelo pré-treinado no TensorFlow com o Keras.</li>
<li>Fine-tuning de um modelo pré-treinado em PyTorch nativo.</li></ul>
<a id="data-processing"></a>
<h2 class="relative group"><a id="preparando-um-dataset" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preparando-um-dataset"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Preparando um dataset
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/_BZearw7f0w" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Antes de aplicar o fine-tuning a um modelo pré-treinado, baixe um dataset e prepare-o para o treinamento.
O tutorial anterior ensinará a processar os dados para o treinamento, e então poderá ter a oportunidade de testar
esse novo conhecimento em algo prático.</p>
<p>Comece carregando o dataset <a href="https://huggingface.co/datasets/yelp_review_full" rel="nofollow">Yelp Reviews</a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"yelp_review_full"</span>)
<span class="hljs-meta">>>> </span>dataset[<span class="hljs-number">100</span>]
{<span class="hljs-string">'label'</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">'text'</span>: <span class="hljs-string">'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'</span>}<!-- HTML_TAG_END --></pre></div>
<p>Como já sabe, é necessário ter um tokenizador para processar o texto e incluir uma estratégia de padding e truncamento,
para manejar qualquer tamanho varíavel de sequência. Para processar o seu dataset em apenas um passo, utilize o método de
🤗 Datasets <a href="https://huggingface.co/docs/datasets/process.html#map" rel="nofollow"><code>map</code></a> para aplicar uma função de preprocessamento sobre
todo o dataset.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenizer(examples[<span class="hljs-string">"text"</span>], padding=<span class="hljs-string">"max_length"</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokenized_datasets = dataset.<span class="hljs-built_in">map</span>(tokenize_function, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Se desejar, é possível criar um subconjunto menor do dataset completo para aplicar o fine-tuning e assim reduzir o tempo necessário.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>small_train_dataset = tokenized_datasets[<span class="hljs-string">"train"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))
<span class="hljs-meta">>>> </span>small_eval_dataset = tokenized_datasets[<span class="hljs-string">"test"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))<!-- HTML_TAG_END --></pre></div>
<a id="trainer"></a>
<h2 class="relative group"><a id="finetuning-com-o-trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetuning-com-o-trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tuning com o <code>Trainer</code></span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/nvBXf7s7vTI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>O 🤗 Transformers proporciona uma classe <code>Trainer</code> otimizada para o treinamento de modelos de 🤗 Transformers,
facilitando os primeiros passos do treinamento sem a necessidade de escrever manualmente o seu próprio ciclo.
A API do <code>Trainer</code> suporta um grande conjunto de opções de treinamento e funcionalidades, como o logging,
o gradient accumulation e o mixed precision.</p>
<p>Comece carregando seu modelo e especifique o número de labels de previsão.
A partir do <a href="https://huggingface.co/datasets/yelp_review_full#data-fields" rel="nofollow">Card Dataset</a> do Yelp Reveiw, que ja
sabemos ter 5 labels usamos o seguinte código:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>, num_labels=<span class="hljs-number">5</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Você verá um alerta sobre alguns pesos pré-treinados que não estão sendo utilizados e que alguns pesos estão
sendo inicializados aleatoriamente. Não se preocupe, essa mensagem é completamente normal.
O header/cabeçário pré-treinado do modelo BERT é descartado e substitui-se por um header de classificação
inicializado aleatoriamente. Assim, pode aplicar o fine-tuning a este novo header do modelo em sua tarefa
de classificação de sequências fazendo um transfer learning do modelo pré-treinado.</p></div>
<h3 class="relative group"><a id="hiperparmetros-de-treinamento" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#hiperparmetros-de-treinamento"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Hiperparâmetros de treinamento
</span></h3>
<p>Em seguida, crie uma classe <code>TrainingArguments</code> que contenha todos os hiperparâmetros que possam ser ajustados, assim
como os indicadores para ativar as diferentes opções de treinamento. Para este tutorial, você pode começar o treinamento
usando os <a href="https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments" rel="nofollow">hiperparámetros</a> padrão,
porém, sinta-se livre para experimentar com eles e encontrar uma configuração ótima.</p>
<p>Especifique onde salvar os checkpoints do treinamento:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments
<span class="hljs-meta">>>> </span>training_args = TrainingArguments(output_dir=<span class="hljs-string">"test_trainer"</span>)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="mtricas" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#mtricas"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Métricas
</span></h3>
<p>O <code>Trainer</code> não avalia automaticamente o rendimento do modelo durante o treinamento. Será necessário passar ao
<code>Trainer</code> uma função para calcular e fazer um diagnóstico sobre as métricas. A biblioteca 🤗 Datasets proporciona
uma função de <a href="https://huggingface.co/metrics/accuracy" rel="nofollow"><code>accuracy</code></a> simples que pode ser carregada com a função
<code>load_metric</code> (ver este <a href="https://huggingface.co/docs/datasets/metrics.html" rel="nofollow">tutorial</a> para mais informações):</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric
<span class="hljs-meta">>>> </span>metric = load_metric(<span class="hljs-string">"accuracy"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Defina a função <code>compute</code> dentro de <code>metric</code> para calcular a precisão das suas predições.
Antes de passar as suas predições ao <code>compute</code>, é necessário converter as predições à logits (lembre-se que
todos os modelos de 🤗 Transformers retornam logits).</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_pred</span>):
<span class="hljs-meta">... </span> logits, labels = eval_pred
<span class="hljs-meta">... </span> predictions = np.argmax(logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> metric.compute(predictions=predictions, references=labels)<!-- HTML_TAG_END --></pre></div>
<p>Se quiser controlar as suas métricas de avaliação durante o fine-tuning, especifique o parâmetro <code>evaluation_strategy</code>
nos seus argumentos de treinamento para que o modelo considere a métrica de avaliação ao final de cada época:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments
<span class="hljs-meta">>>> </span>training_args = TrainingArguments(output_dir=<span class="hljs-string">"test_trainer"</span>, evaluation_strategy=<span class="hljs-string">"epoch"</span>)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="trainer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainer
</span></h3>
<p>Crie um objeto <code>Trainer</code> com o seu modelo, argumentos de treinamento, conjuntos de dados de treinamento e de teste, e a sua função de avaliação:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=small_train_dataset,
<span class="hljs-meta">... </span> eval_dataset=small_eval_dataset,
<span class="hljs-meta">... </span> compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Em seguida, aplique o fine-tuning a seu modelo chamado <code>train()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<a id="keras"></a>
<h2 class="relative group"><a id="finetuning-com-keras" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetuning-com-keras"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tuning com Keras
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/rnTGBy2ax1c" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Os modelos de 🤗 Transformers também permitem realizar o treinamento com o TensorFlow com a API do Keras.
Contudo, será necessário fazer algumas mudanças antes de realizar o fine-tuning.</p>
<h3 class="relative group"><a id="converso-do-dataset-ao-formato-do-tensorflow" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#converso-do-dataset-ao-formato-do-tensorflow"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Conversão do dataset ao formato do TensorFlow
</span></h3>
<p>O <code>DefaultDataCollator</code> junta os tensores em um batch para que o modelo possa ser treinado em cima deles.
Assegure-se de especificar os <code>return_tensors</code> para retornar os tensores do TensorFlow:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator
<span class="hljs-meta">>>> </span>data_collator = DefaultDataCollator(return_tensors=<span class="hljs-string">"tf"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>O <code>Trainer</code> utiliza <code>DataCollatorWithPadding</code> por padrão, então você não precisa especificar explicitamente um
colador de dados (data collator).</p></div>
<p>Em seguida, converta os datasets tokenizados em datasets do TensorFlow com o método
<a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>.
Especifique suas entradas em <code>columns</code> e seu rótulo em <code>label_cols</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_dataset = small_train_dataset.to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"token_type_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_dataset = small_eval_dataset.to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"token_type_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="compilao-e-ajustes" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#compilao-e-ajustes"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Compilação e ajustes
</span></h3>
<p>Carregue um modelo do TensorFlow com o número esperado de rótulos:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>, num_labels=<span class="hljs-number">5</span>)<!-- HTML_TAG_END --></pre></div>
<p>A seguir, compile e ajuste o fine-tuning a seu modelo com <a href="https://keras.io/api/models/model_training_apis/" rel="nofollow"><code>fit</code></a> como
faria com qualquer outro modelo do Keras:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(
<span class="hljs-meta">... </span> optimizer=tf.keras.optimizers.Adam(learning_rate=<span class="hljs-number">5e-5</span>),
<span class="hljs-meta">... </span> loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=<span class="hljs-literal">True</span>),
<span class="hljs-meta">... </span> metrics=tf.metrics.SparseCategoricalAccuracy(),
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
<a id="pytorch_native"></a>
<h2 class="relative group"><a id="finetune-em-pytorch-nativo" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#finetune-em-pytorch-nativo"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Fine-tune em PyTorch nativo
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/Dh9CL8fyG80" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>O <code>Trainer</code> se encarrega do ciclo de treinamento e permite aplicar o fine-tuning a um modelo em uma linha de código apenas.
Para os usuários que preferirem escrever o seu próprio ciclo de treinamento, também é possível aplicar o fine-tuning a um
modelo de 🤗 Transformers em PyTorch nativo.</p>
<p>Neste momento, talvez ocorra a necessidade de reinicar seu notebook ou executar a seguinte linha de código para liberar
memória:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-keyword">del</span> model
<span class="hljs-keyword">del</span> pytorch_model
<span class="hljs-keyword">del</span> trainer
torch.cuda.empty_cache()<!-- HTML_TAG_END --></pre></div>
<p>Em sequência, faremos um post-processing manual do <code>tokenized_dataset</code> e assim prepará-lo para o treinamento.</p>
<ol><li><p>Apague a coluna de <code>text</code> porque o modelo não aceita texto cru como entrada:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_datasets = tokenized_datasets.remove_columns([<span class="hljs-string">"text"</span>])<!-- HTML_TAG_END --></pre></div></li>
<li><p>Troque o nome da coluna <code>label</code> para <code>labels</code>, pois o modelo espera um argumento de mesmo nome:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_datasets = tokenized_datasets.rename_column(<span class="hljs-string">"label"</span>, <span class="hljs-string">"labels"</span>)<!-- HTML_TAG_END --></pre></div></li>
<li><p>Defina o formato do dataset para retornar tensores do PyTorch no lugar de listas:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_datasets.set_format(<span class="hljs-string">"torch"</span>)<!-- HTML_TAG_END --></pre></div></li></ol>
<p>Em sequência, crie um subconjunto menor do dataset, como foi mostrado anteriormente, para acelerá-lo o fine-tuning.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>small_train_dataset = tokenized_datasets[<span class="hljs-string">"train"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))
<span class="hljs-meta">>>> </span>small_eval_dataset = tokenized_datasets[<span class="hljs-string">"test"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="dataloader" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#dataloader"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>DataLoader
</span></h3>
<p>Crie um <code>DataLoader</code> para os seus datasets de treinamento e de teste para poder iterar sobre batches de dados:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torch.utils.data <span class="hljs-keyword">import</span> DataLoader
<span class="hljs-meta">>>> </span>train_dataloader = DataLoader(small_train_dataset, shuffle=<span class="hljs-literal">True</span>, batch_size=<span class="hljs-number">8</span>)
<span class="hljs-meta">>>> </span>eval_dataloader = DataLoader(small_eval_dataset, batch_size=<span class="hljs-number">8</span>)<!-- HTML_TAG_END --></pre></div>
<p>Carregue seu modelo com o número de labels esperados:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>, num_labels=<span class="hljs-number">5</span>)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="otimizao-e-configurao-do-learning-rate" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#otimizao-e-configurao-do-learning-rate"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Otimização e configuração do Learning Rate
</span></h3>
<p>Crie um otimizador e um learning rate para aplicar o fine-tuning ao modelo.
Iremos utilizar o otimizador <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html" rel="nofollow"><code>AdamW</code></a> do PyTorch:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torch.optim <span class="hljs-keyword">import</span> AdamW
<span class="hljs-meta">>>> </span>optimizer = AdamW(model.parameters(), lr=<span class="hljs-number">5e-5</span>)<!-- HTML_TAG_END --></pre></div>
<p>Defina o learning rate do <code>Trainer</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> get_scheduler
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span>num_training_steps = num_epochs * <span class="hljs-built_in">len</span>(train_dataloader)
<span class="hljs-meta">>>> </span>lr_scheduler = get_scheduler(
<span class="hljs-meta">... </span> name=<span class="hljs-string">"linear"</span>, optimizer=optimizer, num_warmup_steps=<span class="hljs-number">0</span>, num_training_steps=num_training_steps
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Por último, especifique o <code>device</code> do ambiente para utilizar uma GPU se tiver acesso à alguma. Caso contrário, o treinamento
em uma CPU pode acabar levando várias horas em vez de minutos.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>device = torch.device(<span class="hljs-string">"cuda"</span>) <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> torch.device(<span class="hljs-string">"cpu"</span>)
<span class="hljs-meta">>>> </span>model.to(device)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Se necessário, você pode obter o acesso gratuito a uma GPU na núvem por meio de um notebook no
<a href="https://colab.research.google.com/" rel="nofollow">Colaboratory</a> ou <a href="https://studiolab.sagemaker.aws/" rel="nofollow">SageMaker StudioLab</a>
se não tiver esse recurso de forma local.</p></div>
<p>Perfeito, agora estamos prontos para começar o treinamento! 🥳</p>
<h3 class="relative group"><a id="ciclo-de-treinamento" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#ciclo-de-treinamento"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Ciclo de treinamento
</span></h3>
<p>Para visualizar melhor o processo de treinamento, utilize a biblioteca <a href="https://tqdm.github.io/" rel="nofollow">tqdm</a> para adicionar
uma barra de progresso sobre o número de passos percorridos no treinamento atual:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm
<span class="hljs-meta">>>> </span>progress_bar = tqdm(<span class="hljs-built_in">range</span>(num_training_steps))
<span class="hljs-meta">>>> </span>model.train()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_epochs):
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
<span class="hljs-meta">... </span> batch = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
<span class="hljs-meta">... </span> outputs = model(**batch)
<span class="hljs-meta">... </span> loss = outputs.loss
<span class="hljs-meta">... </span> loss.backward()
<span class="hljs-meta">... </span> optimizer.step()
<span class="hljs-meta">... </span> lr_scheduler.step()
<span class="hljs-meta">... </span> optimizer.zero_grad()
<span class="hljs-meta">... </span> progress_bar.update(<span class="hljs-number">1</span>)<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="mtricas" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#mtricas"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Métricas
</span></h3>
<p>Da mesma forma que é necessário adicionar uma função de avaliação ao <code>Trainer</code>, é necessário fazer o mesmo quando
escrevendo o próprio ciclo de treinamento. Contudo, em vez de calcular e retornar a métrica final de cada época,
você deverá adicionar todos os batches com <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=add_batch#datasets.Metric.add_batch" rel="nofollow"><code>add_batch</code></a>
e calcular a métrica apenas no final.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>metric = load_metric(<span class="hljs-string">"accuracy"</span>)
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">eval</span>()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> eval_dataloader:
<span class="hljs-meta">... </span> batch = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span> outputs = model(**batch)
<span class="hljs-meta">... </span> logits = outputs.logits
<span class="hljs-meta">... </span> predictions = torch.argmax(logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> metric.add_batch(predictions=predictions, references=batch[<span class="hljs-string">"labels"</span>])
<span class="hljs-meta">>>> </span>metric.compute()<!-- HTML_TAG_END --></pre></div>
<a id="additional-resources"></a>
<h2 class="relative group"><a id="recursos-adicionais" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#recursos-adicionais"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Recursos adicionais
</span></h2>
<p>Para mais exemplos de fine-tuning acesse:</p>
<ul><li><p><a href="https://github.com/huggingface/transformers/tree/main/examples" rel="nofollow">🤗 Transformers Examples</a> inclui scripts
para treinas tarefas comuns de NLP em PyTorch e TensorFlow.</p></li>
<li><p><a href="notebooks">🤗 Transformers Notebooks</a> contém vários notebooks sobre como aplicar o fine-tuning a um modelo
para tarefas específicas no PyTorch e TensorFlow.</p></li></ul>
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<h1 class="relative group"><a id="criar-uma-arquitetura-customizada" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#criar-uma-arquitetura-customizada"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Criar uma arquitetura customizada
</span></h1>
<p>Uma <a href="model_doc/auto"><code>AutoClass</code></a> automaticamente infere a arquitetura do modelo e baixa configurações e pesos pré-treinados. Geralmente, nós recomendamos usar uma <code>AutoClass</code> para produzir um código independente de checkpoints. Mas usuários que querem mais contole sobre parâmetros específicos do modelo pode criar um modelo customizado 🤗 Transformers a partir de algumas classes bases. Isso pode ser particulamente útil para alguém que está interessado em estudar, treinar ou fazer experimentos com um modelo 🤗 Transformers. Nesse tutorial, será explicado como criar um modelo customizado sem uma <code>AutoClass</code>. Aprenda como:</p>
<ul><li>Carregar e customizar a configuração de um modelo.</li>
<li>Criar a arquitetura de um modelo.</li>
<li>Criar um tokenizer rápido e devagar para textos.</li>
<li>Criar extrator de features para tarefas envolvendo audio e imagem.</li>
<li>Criar um processador para tarefas multimodais.</li></ul>
<h2 class="relative group"><a id="configuration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#configuration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>configuration
</span></h2>
<p>A <a href="main_classes/configuration">configuration</a> refere-se a atributos específicos de um modelo. Cada configuração de modelo tem atributos diferentes; por exemplo, todos modelo de PLN possuem os atributos <code>hidden_size</code>, <code>num_attention_heads</code>, <code>num_hidden_layers</code> e <code>vocab_size</code> em comum. Esse atributos especificam o numero de ‘attention heads’ ou ‘hidden layers’ para construir um modelo.</p>
<p>Dê uma olhada a mais em <a href="model_doc/distilbert">DistilBERT</a> acessando <code>DistilBertConfig</code> para observar esses atributos:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertConfig
<span class="hljs-meta">>>> </span>config = DistilBertConfig()
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(config)
DistilBertConfig {
<span class="hljs-string">"activation"</span>: <span class="hljs-string">"gelu"</span>,
<span class="hljs-string">"attention_dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"dim"</span>: <span class="hljs-number">768</span>,
<span class="hljs-string">"dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"hidden_dim"</span>: <span class="hljs-number">3072</span>,
<span class="hljs-string">"initializer_range"</span>: <span class="hljs-number">0.02</span>,
<span class="hljs-string">"max_position_embeddings"</span>: <span class="hljs-number">512</span>,
<span class="hljs-string">"model_type"</span>: <span class="hljs-string">"distilbert"</span>,
<span class="hljs-string">"n_heads"</span>: <span class="hljs-number">12</span>,
<span class="hljs-string">"n_layers"</span>: <span class="hljs-number">6</span>,
<span class="hljs-string">"pad_token_id"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"qa_dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"seq_classif_dropout"</span>: <span class="hljs-number">0.2</span>,
<span class="hljs-string">"sinusoidal_pos_embds"</span>: false,
<span class="hljs-string">"transformers_version"</span>: <span class="hljs-string">"4.16.2"</span>,
<span class="hljs-string">"vocab_size"</span>: <span class="hljs-number">30522</span>
}<!-- HTML_TAG_END --></pre></div>
<p><code>DistilBertConfig</code> mostra todos os atributos padrões usados para construir um <code>DistilBertModel</code> base. Todos atributos são customizáveis, o que cria espaço para experimentos. Por exemplo, você pode customizar um modelo padrão para:</p>
<ul><li>Tentar uma função de ativação diferente com o parâmetro <code>activation</code>.</li>
<li>Usar uma taxa de desistência maior para as probabilidades de ‘attention’ com o parâmetro <code>attention_dropout</code>.</li></ul>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>my_config = DistilBertConfig(activation=<span class="hljs-string">"relu"</span>, attention_dropout=<span class="hljs-number">0.4</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(my_config)
DistilBertConfig {
<span class="hljs-string">"activation"</span>: <span class="hljs-string">"relu"</span>,
<span class="hljs-string">"attention_dropout"</span>: <span class="hljs-number">0.4</span>,
<span class="hljs-string">"dim"</span>: <span class="hljs-number">768</span>,
<span class="hljs-string">"dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"hidden_dim"</span>: <span class="hljs-number">3072</span>,
<span class="hljs-string">"initializer_range"</span>: <span class="hljs-number">0.02</span>,
<span class="hljs-string">"max_position_embeddings"</span>: <span class="hljs-number">512</span>,
<span class="hljs-string">"model_type"</span>: <span class="hljs-string">"distilbert"</span>,
<span class="hljs-string">"n_heads"</span>: <span class="hljs-number">12</span>,
<span class="hljs-string">"n_layers"</span>: <span class="hljs-number">6</span>,
<span class="hljs-string">"pad_token_id"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"qa_dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"seq_classif_dropout"</span>: <span class="hljs-number">0.2</span>,
<span class="hljs-string">"sinusoidal_pos_embds"</span>: false,
<span class="hljs-string">"transformers_version"</span>: <span class="hljs-string">"4.16.2"</span>,
<span class="hljs-string">"vocab_size"</span>: <span class="hljs-number">30522</span>
}<!-- HTML_TAG_END --></pre></div>
<p>Atributos de um modelo pré-treinado podem ser modificados na função <code>from_pretrained()</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, activation=<span class="hljs-string">"relu"</span>, attention_dropout=<span class="hljs-number">0.4</span>)<!-- HTML_TAG_END --></pre></div>
<p>Uma vez que você está satisfeito com as configurações do seu modelo, você consegue salvar elas com <code>save_pretrained()</code>. Seu arquivo de configurações está salvo como um arquivo JSON no diretório especificado:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>my_config.save_pretrained(save_directory=<span class="hljs-string">"./your_model_save_path"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Para reusar o arquivo de configurações, carregue com <code>from_pretrained()</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"./your_model_save_path/my_config.json"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Você pode também salvar seu arquivo de configurações como um dicionário ou até mesmo com a diferença entre as seus atributos de configuração customizados e os atributos de configuração padrões! Olhe a documentação <a href="main_classes/configuration">configuration</a> para mais detalhes.</p></div>
<h2 class="relative group"><a id="modelo" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#modelo"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Modelo
</span></h2>
<p>O próximo passo é criar um <a href="main_classes/models">model</a>. O modelo - também vagamente referido como arquitetura - define o que cada camada está fazendo e quais operações estão acontecendo. Atributos como <code>num_hidden_layers</code> das configurações são utilizados para definir a arquitetura. Todo modelo compartilha a classe base <code>PreTrainedModel</code> e alguns métodos em comum como redimensionar o tamanho dos embeddings de entrada e podar as ‘self-attention heads’. Além disso, todos os modelos também são subclasses de <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html" rel="nofollow"><code>torch.nn.Module</code></a>, <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow"><code>tf.keras.Model</code></a> ou <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" rel="nofollow"><code>flax.linen.Module</code></a>. Isso significa que os modelos são compatíveis com cada respectivo uso de framework.</p>
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<span>Pytorch</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
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<p>Carregar seus atributos de configuração customizados em um modelo:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertModel
<span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"./your_model_save_path/my_config.json"</span>)
<span class="hljs-meta">>>> </span>model = DistilBertModel(my_config)<!-- HTML_TAG_END --></pre></div>
<p>Isso cria um modelo com valores aleatórios ao invés de pré-treinar os pesos. Você não irá conseguir usar usar esse modelo para nada útil ainda, até você treinar ele. Treino é um processo caro e demorado. Geralmente é melhor utilizar um modelo pré-treinado para obter melhores resultados mais rápido, enquanto usa apenas uma fração dos recursos necessários para treinar.</p>
<p>Criar um modelo pré-treinado com <code>from_pretrained()</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Quando você carregar os pesos pré-treinados, a configuração padrão do modelo é automaticamente carregada se o modelo é provido pelo 🤗 Transformers. No entanto, você ainda consegue mudar - alguns ou todos - os atributos padrões de configuração do modelo com os seus próprio atributos, se você preferir: </p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, config=my_config)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Carregar os seus próprios atributos padrões de contiguração no modelo:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertModel
<span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"./your_model_save_path/my_config.json"</span>)
<span class="hljs-meta">>>> </span>tf_model = TFDistilBertModel(my_config)<!-- HTML_TAG_END --></pre></div>
<p>Isso cria um modelo com valores aleatórios ao invés de pré-treinar os pesos. Você não irá conseguir usar usar esse modelo para nada útil ainda, até você treinar ele. Treino é um processo caro e demorado. Geralmente é melhor utilizar um modelo pré-treinado para obter melhores resultados mais rápido, enquanto usa apenas uma fração dos recursos necessários para treinar.</p>
<p>Criar um modelo pré-treinado com <code>from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Quando você carregar os pesos pré-treinados, a configuração padrão do modelo é automaticamente carregada se o modelo é provido pelo 🤗 Transformers. No entanto, você ainda consegue mudar - alguns ou todos - os atributos padrões de configuração do modelo com os seus próprio atributos, se você preferir: </p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, config=my_config)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<h3 class="relative group"><a id="heads-do-modelo" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#heads-do-modelo"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Heads do modelo
</span></h3>
<p>Neste ponto, você tem um modelo básico do DistilBERT que gera os <em>estados ocultos</em>. Os estados ocultos são passados como entrada para a head do moelo para produzir a saída final. 🤗 Transformers fornece uma head de modelo diferente para cada tarefa desde que o modelo suporte essa tarefa (por exemplo, você não consegue utilizar o modelo DistilBERT para uma tarefa de ‘sequence-to-sequence’ como tradução).</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
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<span>Pytorch</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>Por exemplo, <code>DistilBertForSequenceClassification</code> é um modelo DistilBERT base com uma head de classificação de sequência. A head de calssificação de sequência é uma camada linear no topo das saídas agrupadas.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertForSequenceClassification
<span class="hljs-meta">>>> </span>model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Reutilize facilmente esse ponto de parada para outra tarefe mudando para uma head de modelo diferente. Para uma tarefe de responder questões, você usaria a head do modelo <code>DistilBertForQuestionAnswering</code>. A head de responder questões é similar com a de classificação de sequências exceto o fato de que ela é uma camada no topo dos estados das saídas ocultas.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertForQuestionAnswering
<span class="hljs-meta">>>> </span>model = DistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div></div></div>
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<span>TensorFlow</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Por exemplo, <code>TFDistilBertForSequenceClassification</code> é um modelo DistilBERT base com uma head de classificação de sequência. A head de calssificação de sequência é uma camada linear no topo das saídas agrupadas.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertForSequenceClassification
<span class="hljs-meta">>>> </span>tf_model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Reutilize facilmente esse ponto de parada para outra tarefe mudando para uma head de modelo diferente. Para uma tarefe de responder questões, você usaria a head do modelo <code>TFDistilBertForQuestionAnswering</code>. A head de responder questões é similar com a de classificação de sequências exceto o fato de que ela é uma camada no topo dos estados das saídas ocultas.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertForQuestionAnswering
<span class="hljs-meta">>>> </span>tf_model = TFDistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<h2 class="relative group"><a id="tokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#tokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Tokenizer
</span></h2>
<p>A útlima classe base que você precisa antes de usar um modelo para dados textuais é a <a href="main_classes/tokenizer">tokenizer</a> para converter textos originais para tensores. Existem dois tipos de tokenizers que você pode usar com 🤗 Transformers:</p>
<ul><li><code>PreTrainedTokenizer</code>: uma implementação em Python de um tokenizer.</li>
<li><code>PreTrainedTokenizerFast</code>: um tokenizer da nossa biblioteca <a href="https://huggingface.co/docs/tokenizers/python/latest/" rel="nofollow">🤗 Tokenizer</a> baseada em Rust. Esse tipo de tokenizer é significantemente mais rapido - especialmente durante tokenization de codificação - devido a implementação em Rust. O tokenizer rápido tambem oferece métodos adicionais como <em>offset mapping</em> que mapeia tokens para suar palavras ou caracteres originais.</li></ul>
<p>Os dois tokenizers suporta métodos comuns como os de codificar e decodificar, adicionar novos tokens, e gerenciar tokens especiais.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>Nem todo modelo suporta um ‘fast tokenizer’. De uma olhada aqui <a href="index#supported-frameworks">table</a> pra checar se um modelo suporta ‘fast tokenizer’.</p></div>
<p>Se você treinou seu prórpio tokenizer, você pode criar um a partir do seu arquivo <em>vocabulary</em>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer
<span class="hljs-meta">>>> </span>my_tokenizer = DistilBertTokenizer(vocab_file=<span class="hljs-string">"my_vocab_file.txt"</span>, do_lower_case=<span class="hljs-literal">False</span>, padding_side=<span class="hljs-string">"left"</span>)<!-- HTML_TAG_END --></pre></div>
<p>É importante lembrar que o vocabulário de um tokenizer customizado será diferente de um vocabulário gerado pelo tokenizer de um modelo pré treinado. Você precisa usar o vocabulário de um modelo pré treinado se você estiver usando um modelo pré treinado, caso contrário as entradas não farão sentido. Criando um tokenizer com um vocabulário de um modelo pré treinado com a classe <code>DistilBertTokenizer</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer
<span class="hljs-meta">>>> </span>slow_tokenizer = DistilBertTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Criando um ‘fast tokenizer’ com a classe <code>DistilBertTokenizerFast</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizerFast
<span class="hljs-meta">>>> </span>fast_tokenizer = DistilBertTokenizerFast.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Pos padrão, <code>AutoTokenizer</code> tentará carregar um ‘fast tokenizer’. Você pode disabilitar esse comportamento colocando <code>use_fast=False</code> no <code>from_pretrained</code>.</p></div>
<h2 class="relative group"><a id="extrator-de-features" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#extrator-de-features"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Extrator de features
</span></h2>
<p>Um extrator de features processa entradas de imagem ou áudio. Ele herda da classe base <code>FeatureExtractionMixin</code>, e pode também herdar da classe <code>ImageFeatureExtractionMixin</code> para processamento de features de imagem ou da classe <code>SequenceFeatureExtractor</code> para processamento de entradas de áudio.</p>
<p>Dependendo do que você está trabalhando em um audio ou uma tarefa de visão, crie um estrator de features associado com o modelo que você está usando. Por exemplo, crie um <code>ViTFeatureExtractor</code> padrão se você estiver usando <a href="model_doc/vit">ViT</a> para classificação de imagens:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTFeatureExtractor
<span class="hljs-meta">>>> </span>vit_extractor = ViTFeatureExtractor()
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(vit_extractor)
ViTFeatureExtractor {
<span class="hljs-string">"do_normalize"</span>: true,
<span class="hljs-string">"do_resize"</span>: true,
<span class="hljs-string">"feature_extractor_type"</span>: <span class="hljs-string">"ViTFeatureExtractor"</span>,
<span class="hljs-string">"image_mean"</span>: [
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>
],
<span class="hljs-string">"image_std"</span>: [
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>
],
<span class="hljs-string">"resample"</span>: <span class="hljs-number">2</span>,
<span class="hljs-string">"size"</span>: <span class="hljs-number">224</span>
}<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Se você não estiver procurando por nenhuma customização, apenas use o método <code>from_pretrained</code> para carregar parâmetros do modelo de extrator de features padrão.</p></div>
<p>Modifique qualquer parâmetro dentre os <code>ViTFeatureExtractor</code> para criar seu extrator de features customizado.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTFeatureExtractor
<span class="hljs-meta">>>> </span>my_vit_extractor = ViTFeatureExtractor(resample=<span class="hljs-string">"PIL.Image.BOX"</span>, do_normalize=<span class="hljs-literal">False</span>, image_mean=[<span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>])
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(my_vit_extractor)
ViTFeatureExtractor {
<span class="hljs-string">"do_normalize"</span>: false,
<span class="hljs-string">"do_resize"</span>: true,
<span class="hljs-string">"feature_extractor_type"</span>: <span class="hljs-string">"ViTFeatureExtractor"</span>,
<span class="hljs-string">"image_mean"</span>: [
<span class="hljs-number">0.3</span>,
<span class="hljs-number">0.3</span>,
<span class="hljs-number">0.3</span>
],
<span class="hljs-string">"image_std"</span>: [
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>
],
<span class="hljs-string">"resample"</span>: <span class="hljs-string">"PIL.Image.BOX"</span>,
<span class="hljs-string">"size"</span>: <span class="hljs-number">224</span>
}<!-- HTML_TAG_END --></pre></div>
<p>Para entradas de áutio, você pode criar um <code>Wav2Vec2FeatureExtractor</code> e customizar os parâmetros de uma forma similar:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor
<span class="hljs-meta">>>> </span>w2v2_extractor = Wav2Vec2FeatureExtractor()
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(w2v2_extractor)
Wav2Vec2FeatureExtractor {
<span class="hljs-string">"do_normalize"</span>: true,
<span class="hljs-string">"feature_extractor_type"</span>: <span class="hljs-string">"Wav2Vec2FeatureExtractor"</span>,
<span class="hljs-string">"feature_size"</span>: <span class="hljs-number">1</span>,
<span class="hljs-string">"padding_side"</span>: <span class="hljs-string">"right"</span>,
<span class="hljs-string">"padding_value"</span>: <span class="hljs-number">0.0</span>,
<span class="hljs-string">"return_attention_mask"</span>: false,
<span class="hljs-string">"sampling_rate"</span>: <span class="hljs-number">16000</span>
}<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="processor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#processor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Processor
</span></h2>
<p>Para modelos que suportam tarefas multimodais, 🤗 Transformers oferece uma classe processadora que convenientemente cobre um extrator de features e tokenizer dentro de um único objeto. Por exemplo, vamos usar o <code>Wav2Vec2Processor</code> para uma tarefa de reconhecimento de fala automática (ASR). ASR transcreve áudio para texto, então você irá precisar de um extrator de um features e um tokenizer.</p>
<p>Crie um extrator de features para lidar com as entradas de áudio.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor
<span class="hljs-meta">>>> </span>feature_extractor = Wav2Vec2FeatureExtractor(padding_value=<span class="hljs-number">1.0</span>, do_normalize=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Crie um tokenizer para lidar com a entrada de textos:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2CTCTokenizer
<span class="hljs-meta">>>> </span>tokenizer = Wav2Vec2CTCTokenizer(vocab_file=<span class="hljs-string">"my_vocab_file.txt"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Combine o extrator de features e o tokenizer no <code>Wav2Vec2Processor</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2Processor
<span class="hljs-meta">>>> </span>processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div>
<p>Com duas classes básicas - configuração e modelo - e um preprocessamento de classe adicional (tokenizer, extrator de features, ou processador), você pode criar qualquer modelo que suportado por 🤗 Transformers. Qualquer uma dessas classes base são configuráveis, te permitindo usar os atributos específicos que você queira. Você pode facilmente preparar um modelo para treinamento ou modificar um modelo pré-treinado com poucas mudanças.</p>
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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/accelerate.mdx-hf-doc-builder.js | import{S as Wo,i as Qo,s as Jo,e as t,k as m,w as u,t as c,M as Ro,c as s,d as a,m as d,a as l,x as f,h as p,b as n,G as o,g as i,y as h,L as Ko,q as v,o as _,B as $,v as Vo}from"../chunks/vendor-hf-doc-builder.js";import{I as ie}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as M}from"../chunks/CodeBlock-hf-doc-builder.js";function Xo(lo){let w,Oe,k,S,he,U,ga,ve,ba,De,q,wa,F,ka,Aa,He,A,C,_e,B,Ea,$e,ja,Ge,ne,Pa,Le,I,Me,g,ya,W,ge,za,Sa,be,qa,Ca,Ue,Q,Fe,E,N,we,J,Na,ke,Ta,Be,T,xa,R,Ae,Oa,Da,Ie,K,We,j,x,Ee,V,Ha,je,Ga,Qe,b,La,Pe,Ma,Ua,X,ye,Fa,Ba,Je,Y,Re,ce,Ia,Ke,Z,Ve,P,O,ze,ee,Wa,Se,Qa,Xe,pe,Ja,Ye,y,D,qe,ae,Ra,Ce,Ka,Ze,me,Va,ea,oe,aa,de,Xa,oa,re,ra,z,H,Ne,te,Ya,Te,Za,ta,G,eo,xe,ao,oo,sa,se,la,L,ro,le,to,so,ia;return U=new ie({}),B=new ie({}),I=new M({props:{code:"pip install accelerate",highlighted:"pip install accelerate"}}),Q=new M({props:{code:`from accelerate import Accelerator
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<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
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from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
+ accelerator = Accelerator()
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
- model.to(device)
+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
+ train_dataloader, eval_dataloader, model, optimizer
+ )
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
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optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
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progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
- batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
- loss.backward()
+ accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)`,highlighted:`<span class="hljs-addition">+ from accelerate import Accelerator</span>
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
<span class="hljs-addition">+ accelerator = Accelerator()</span>
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
<span class="hljs-deletion">- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")</span>
<span class="hljs-deletion">- model.to(device)</span>
<span class="hljs-addition">+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(</span>
<span class="hljs-addition">+ train_dataloader, eval_dataloader, model, optimizer</span>
<span class="hljs-addition">+ )</span>
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
<span class="hljs-deletion">- batch = {k: v.to(device) for k, v in batch.items()}</span>
outputs = model(**batch)
loss = outputs.loss
<span class="hljs-deletion">- loss.backward()</span>
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optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
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configurar o \u{1F917} Transformers para execu\xE7\xE3o em modo offline (opcional).`),k=p(),z=r("p"),Po=i(`\u{1F917} Transformers foi testado com Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, e Flax. Para instalar a biblioteca de
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Um ambiente virtual facilitar\xE1 a manipula\xE7\xE3o e organiza\xE7\xE3o de projetos e evita problemas de compatibilidade entre depend\xEAncias.`),Xa=p(),To=r("p"),zt=i("Comece criando um ambiente virtual no diret\xF3rio do seu projeto:"),Ja=p(),h(qe.$$.fragment),Ka=p(),qo=r("p"),Ut=i("E para ativar o ambiente virtual:"),Za=p(),h(ye.$$.fragment),er=p(),yo=r("p"),Bt=i("Agora \xC9 poss\xEDvel instalar o \u{1F917} Transformers com o comando a seguir:"),or=p(),h(Ae.$$.fragment),ar=p(),Ao=r("p"),Vt=i("Somente para a CPU, \xE9 poss\xEDvel instalar o \u{1F917} Transformers e a biblioteca de deep learning respectiva apenas numa linha."),rr=p(),Oo=r("p"),Gt=i("Por exemplo, para instalar o \u{1F917} Transformers e o PyTorch, digite:"),tr=p(),h(Oe.$$.fragment),sr=p(),xo=r("p"),Yt=i("\u{1F917} Transformers e TensorFlow 2.0:"),lr=p(),h(xe.$$.fragment),ir=p(),Co=r("p"),Qt=i("\u{1F917} Transformers e Flax:"),nr=p(),h(Ce.$$.fragment),fr=p(),ko=r("p"),Wt=i("Por \xFAltimo, verifique se o \u{1F917} Transformers foi instalado com sucesso usando o seguinte comando para baixar um modelo pr\xE9-treinado:"),pr=p(),h(ke.$$.fragment),mr=p(),So=r("p"),Xt=i("Em seguida, imprima um r\xF3tulo e sua pontua\xE7\xE3o:"),dr=p(),h(Se.$$.fragment),cr=p(),B=r("h2"),oe=r("a"),sa=r("span"),h(je.$$.fragment),Jt=p(),la=r("span"),Kt=i("Instala\xE7\xE3o usando a fonte"),ur=p(),jo=r("p"),Zt=i("Para instalar o \u{1F917} Transformers a partir da fonte use o seguinte comando:"),hr=p(),h(Ie.$$.fragment),vr=p(),x=r("p"),es=i("O comando acima instalar\xE1 a vers\xE3o "),ia=r("code"),os=i("master"),as=i(" mais atual em vez da \xFAltima vers\xE3o est\xE1vel. A vers\xE3o "),na=r("code"),rs=i("master"),ts=i(` \xE9 \xFAtil para
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Este \xE9 o diret\xF3rio padr\xE3o determinado pela vari\xE1vel `),Ta=r("code"),Hs=i("TRANSFORMERS_CACHE"),Ls=i(` dentro do shell.
No Windows, este diret\xF3rio pr\xE9-definido \xE9 dado por `),qa=r("code"),zs=i("C:\\Users\\username\\.cache\\huggingface\\transformers"),Us=i(`.
\xC9 poss\xEDvel mudar as vari\xE1veis dentro do shell em ordem de prioridade para especificar um diret\xF3rio de cach\xEA diferente:`),Ir=p(),L=r("ol"),Be=r("li"),Bs=i("Vari\xE1vel de ambiente do shell (por padr\xE3o): "),ya=r("code"),Vs=i("TRANSFORMERS_CACHE"),Gs=i("."),Ys=p(),Q=r("li"),Qs=i("Vari\xE1vel de ambiente do shell:"),Aa=r("code"),Ws=i("HF_HOME"),Xs=i(" + "),Oa=r("code"),Js=i("transformers/"),Ks=i("."),Zs=p(),W=r("li"),el=i("Vari\xE1vel de ambiente do shell: "),xa=r("code"),ol=i("XDG_CACHE_HOME"),al=i(" + "),Ca=r("code"),rl=i("/huggingface/transformers"),tl=i("."),Fr=p(),h(fe.$$.fragment),Mr=p(),X=r("h2"),pe=r("a"),ka=r("span"),h(Ve.$$.fragment),sl=p(),Sa=r("span"),ll=i("Modo Offline"),Rr=p(),me=r("p"),il=i(`O \u{1F917} Transformers tamb\xE9m pode ser executado num ambiente de firewall ou fora da rede (offline) usando arquivos locais.
Para tal, configure a vari\xE1vel de ambiente de modo que `),ja=r("code"),nl=i("TRANSFORMERS_OFFLINE=1"),fl=i("."),Nr=p(),h(de.$$.fragment),Dr=p(),No=r("p"),pl=i("Segue um exemplo de execu\xE7\xE3o do programa numa rede padr\xE3o com firewall para inst\xE2ncias externas, usando o seguinte comando:"),Hr=p(),h(Ge.$$.fragment),Lr=p(),Do=r("p"),ml=i("Execute esse mesmo programa numa inst\xE2ncia offline com o seguinte comando:"),zr=p(),h(Ye.$$.fragment),Ur=p(),Ho=r("p"),dl=i("O script agora deve ser executado sem travar ou expirar, pois procurar\xE1 apenas por arquivos locais."),Br=p(),J=r("h3"),ce=r("a"),Ia=r("span"),h(Qe.$$.fragment),cl=p(),Fa=r("span"),ul=i("Obtendo modelos e tokenizers para uso offline"),Vr=p(),Lo=r("p"),hl=i("Outra op\xE7\xE3o para usar o \u{1F917} Transformers offline \xE9 baixar os arquivos antes e depois apontar para o caminho local onde est\xE3o localizados. Existem tr\xEAs maneiras de fazer isso:"),Gr=p(),zo=r("ul"),We=r("li"),Xe=r("p"),vl=i("Baixe um arquivo por meio da interface de usu\xE1rio do "),Je=r("a"),_l=i("Model Hub"),$l=i(" clicando no \xEDcone \u2193."),gl=p(),Ma=r("p"),Uo=r("img"),Yr=p(),Bo=r("ul"),Ke=r("li"),K=r("p"),El=i("Use o pipeline do "),Ra=r("code"),bl=i("PreTrainedModel.from_pretrained()"),wl=i(" e "),Na=r("code"),Pl=i("PreTrainedModel.save_pretrained()"),Tl=i(":"),ql=p(),Da=r("ol"),Ze=r("li"),eo=r("p"),yl=i("Baixa os arquivos previamente com "),Ha=r("code"),Al=i("PreTrainedModel.from_pretrained()"),Ol=i(":"),xl=p(),h(oo.$$.fragment),Qr=p(),Z=r("ol"),ao=r("li"),ro=r("p"),Cl=i("Salve os arquivos em um diret\xF3rio espec\xEDfico com "),La=r("code"),kl=i("PreTrainedModel.save_pretrained()"),Sl=i(":"),jl=p(),h(to.$$.fragment),Il=p(),so=r("li"),lo=r("p"),Fl=i("Quando estiver offline, acesse os arquivos com "),za=r("code"),Ml=i("PreTrainedModel.from_pretrained()"),Rl=i(" do diret\xF3rio especificado:"),Nl=p(),h(io.$$.fragment),Wr=p(),Vo=r("ul"),no=r("li"),fo=r("p"),Dl=i("Baixando arquivos programaticamente com a biblioteca "),po=r("a"),Hl=i("huggingface_hub"),Ll=i(":"),zl=p(),mo=r("ol"),co=r("li"),uo=r("p"),Ul=i("Instale a biblioteca "),ho=r("a"),Bl=i("huggingface_hub"),Vl=i(" em seu ambiente virtual:"),Gl=p(),h(vo.$$.fragment),Yl=p(),_o=r("li"),M=r("p"),Ql=i("Utiliza a fun\xE7\xE3o "),$o=r("a"),Ua=r("code"),Wl=i("hf_hub_download"),Xl=i(" para baixar um arquivo para um caminho espec\xEDfico. Por exemplo, o comando a seguir baixar\xE1 o arquivo "),Ba=r("code"),Jl=i("config.json"),Kl=i(" para o modelo "),go=r("a"),Zl=i("T0"),ei=i(" no caminho desejado:"),oi=p(),h(Eo.$$.fragment),Xr=p(),Go=r("p"),ai=i("Depois que o arquivo for baixado e armazenado no cach\xEA local, especifique seu caminho local para carreg\xE1-lo e us\xE1-lo:"),Jr=p(),h(bo.$$.fragment),Kr=p(),h(ue.$$.fragment),this.h()},l(e){const l=Ln('[data-svelte="svelte-1phssyn"]',document.head);c=t(l,"META",{name:!0,content:!0}),l.forEach(o),y=m(e),u=t(e,"H1",{class:!0});var wo=s(u);P=t(wo,"A",{id:!0,class:!0,href:!0});var Va=s(P);T=t(Va,"SPAN",{});var Ga=s(T);v(b.$$.fragment,Ga),Ga.forEach(o),Va.forEach(o),w=m(wo),A=t(wo,"SPAN",{});var Ya=s(A);C=n(Ya,"Guia de Instala\xE7\xE3o"),Ya.forEach(o),wo.forEach(o),O=m(e),S=t(e,"P",{});var ti=s(S);j=n(ti,`Neste guia poder\xE1 encontrar informa\xE7\xF5es para a instala\xE7\xE3o do \u{1F917} Transformers para qualquer biblioteca de
Machine Learning com a qual esteja a trabalhar. Al\xE9m disso, poder\xE1 encontrar informa\xE7\xF5es sobre como gerar cach\xEAs e
configurar o \u{1F917} Transformers para execu\xE7\xE3o em modo offline (opcional).`),ti.forEach(o),k=m(e),z=t(e,"P",{});var si=s(z);Po=n(si,`\u{1F917} Transformers foi testado com Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, e Flax. Para instalar a biblioteca de
deep learning com que deseja trabalhar, siga as instru\xE7\xF5es correspondentes listadas a seguir:`),si.forEach(o),$e=m(e),N=t(e,"UL",{});var Yo=s(N);ea=t(Yo,"LI",{});var li=s(ea);ge=t(li,"A",{href:!0,rel:!0});var ii=s(ge);Ct=n(ii,"PyTorch"),ii.forEach(o),li.forEach(o),kt=m(Yo),oa=t(Yo,"LI",{});var ni=s(oa);Ee=t(ni,"A",{href:!0,rel:!0});var fi=s(Ee);St=n(fi,"TensorFlow 2.0"),fi.forEach(o),ni.forEach(o),jt=m(Yo),aa=t(Yo,"LI",{});var pi=s(aa);be=t(pi,"A",{href:!0,rel:!0});var mi=s(be);It=n(mi,"Flax"),mi.forEach(o),pi.forEach(o),Yo.forEach(o),Qa=m(e),U=t(e,"H2",{class:!0});var et=s(U);ee=t(et,"A",{id:!0,class:!0,href:!0});var di=s(ee);ra=t(di,"SPAN",{});var ci=s(ra);v(we.$$.fragment,ci),ci.forEach(o),di.forEach(o),Ft=m(et),ta=t(et,"SPAN",{});var ui=s(ta);Mt=n(ui,"Instala\xE7\xE3o pelo Pip"),ui.forEach(o),et.forEach(o),Wa=m(e),D=t(e,"P",{});var Qo=s(D);Rt=n(Qo,"\xC9 sugerido instalar o \u{1F917} Transformers num "),Pe=t(Qo,"A",{href:!0,rel:!0});var hi=s(Pe);Nt=n(hi,"ambiente virtual"),hi.forEach(o),Dt=n(Qo,`. Se precisar
de mais informa\xE7\xF5es sobre ambientes virtuais em Python, consulte este `),Te=t(Qo,"A",{href:!0,rel:!0});var vi=s(Te);Ht=n(vi,"guia"),vi.forEach(o),Lt=n(Qo,`.
Um ambiente virtual facilitar\xE1 a manipula\xE7\xE3o e organiza\xE7\xE3o de projetos e evita problemas de compatibilidade entre depend\xEAncias.`),Qo.forEach(o),Xa=m(e),To=t(e,"P",{});var _i=s(To);zt=n(_i,"Comece criando um ambiente virtual no diret\xF3rio do seu projeto:"),_i.forEach(o),Ja=m(e),v(qe.$$.fragment,e),Ka=m(e),qo=t(e,"P",{});var $i=s(qo);Ut=n($i,"E para ativar o ambiente virtual:"),$i.forEach(o),Za=m(e),v(ye.$$.fragment,e),er=m(e),yo=t(e,"P",{});var gi=s(yo);Bt=n(gi,"Agora \xC9 poss\xEDvel instalar o \u{1F917} Transformers com o comando a seguir:"),gi.forEach(o),or=m(e),v(Ae.$$.fragment,e),ar=m(e),Ao=t(e,"P",{});var Ei=s(Ao);Vt=n(Ei,"Somente para a CPU, \xE9 poss\xEDvel instalar o \u{1F917} Transformers e a biblioteca de deep learning respectiva apenas numa linha."),Ei.forEach(o),rr=m(e),Oo=t(e,"P",{});var bi=s(Oo);Gt=n(bi,"Por exemplo, para instalar o \u{1F917} Transformers e o PyTorch, digite:"),bi.forEach(o),tr=m(e),v(Oe.$$.fragment,e),sr=m(e),xo=t(e,"P",{});var wi=s(xo);Yt=n(wi,"\u{1F917} Transformers e TensorFlow 2.0:"),wi.forEach(o),lr=m(e),v(xe.$$.fragment,e),ir=m(e),Co=t(e,"P",{});var Pi=s(Co);Qt=n(Pi,"\u{1F917} Transformers e Flax:"),Pi.forEach(o),nr=m(e),v(Ce.$$.fragment,e),fr=m(e),ko=t(e,"P",{});var Ti=s(ko);Wt=n(Ti,"Por \xFAltimo, verifique se o \u{1F917} Transformers foi instalado com sucesso usando o seguinte comando para baixar um modelo pr\xE9-treinado:"),Ti.forEach(o),pr=m(e),v(ke.$$.fragment,e),mr=m(e),So=t(e,"P",{});var qi=s(So);Xt=n(qi,"Em seguida, imprima um r\xF3tulo e sua pontua\xE7\xE3o:"),qi.forEach(o),dr=m(e),v(Se.$$.fragment,e),cr=m(e),B=t(e,"H2",{class:!0});var ot=s(B);oe=t(ot,"A",{id:!0,class:!0,href:!0});var yi=s(oe);sa=t(yi,"SPAN",{});var Ai=s(sa);v(je.$$.fragment,Ai),Ai.forEach(o),yi.forEach(o),Jt=m(ot),la=t(ot,"SPAN",{});var Oi=s(la);Kt=n(Oi,"Instala\xE7\xE3o usando a fonte"),Oi.forEach(o),ot.forEach(o),ur=m(e),jo=t(e,"P",{});var xi=s(jo);Zt=n(xi,"Para instalar o \u{1F917} Transformers a partir da fonte use o seguinte comando:"),xi.forEach(o),hr=m(e),v(Ie.$$.fragment,e),vr=m(e),x=t(e,"P",{});var F=s(x);es=n(F,"O comando acima instalar\xE1 a vers\xE3o "),ia=t(F,"CODE",{});var Ci=s(ia);os=n(Ci,"master"),Ci.forEach(o),as=n(F," mais atual em vez da \xFAltima vers\xE3o est\xE1vel. A vers\xE3o "),na=t(F,"CODE",{});var ki=s(na);rs=n(ki,"master"),ki.forEach(o),ts=n(F,` \xE9 \xFAtil para
utilizar os \xFAltimos updates contidos em \u{1F917} Transformers. Por exemplo, um erro recente pode ter sido corrigido somente
ap\xF3s a \xFAltima vers\xE3o est\xE1vel, antes que houvesse um novo lan\xE7amento. No entanto, h\xE1 a possibilidade que a vers\xE3o `),fa=t(F,"CODE",{});var Si=s(fa);ss=n(Si,"master"),Si.forEach(o),ls=n(F,` n\xE3o esteja est\xE1vel.
A equipa trata de mant\xE9r a vers\xE3o `),pa=t(F,"CODE",{});var ji=s(pa);is=n(ji,"master"),ji.forEach(o),ns=n(F,` operacional e a maioria dos erros s\xE3o resolvidos em poucas horas ou dias.
Se encontrar quaisquer problemas, por favor abra um `),Fe=t(F,"A",{href:!0,rel:!0});var Ii=s(Fe);fs=n(Ii,"Issue"),Ii.forEach(o),ps=n(F,` para que o
mesmo possa ser corrigido o mais r\xE1pido poss\xEDvel.`),F.forEach(o),_r=m(e),Io=t(e,"P",{});var Fi=s(Io);ms=n(Fi,"Verifique que o \u{1F917} Transformers est\xE1 instalado corretamente usando o seguinte comando:"),Fi.forEach(o),$r=m(e),v(Me.$$.fragment,e),gr=m(e),V=t(e,"H2",{class:!0});var at=s(V);ae=t(at,"A",{id:!0,class:!0,href:!0});var Mi=s(ae);ma=t(Mi,"SPAN",{});var Ri=s(ma);v(Re.$$.fragment,Ri),Ri.forEach(o),Mi.forEach(o),ds=m(at),da=t(at,"SPAN",{});var Ni=s(da);cs=n(Ni,"Instala\xE7\xE3o edit\xE1vel"),Ni.forEach(o),at.forEach(o),Er=m(e),Fo=t(e,"P",{});var Di=s(Fo);us=n(Di,"Uma instala\xE7\xE3o edit\xE1vel ser\xE1 necess\xE1ria caso desejas um dos seguintes:"),Di.forEach(o),br=m(e),re=t(e,"UL",{});var rt=s(re);Ne=t(rt,"LI",{});var tt=s(Ne);hs=n(tt,"Usar a vers\xE3o "),ca=t(tt,"CODE",{});var Hi=s(ca);vs=n(Hi,"master"),Hi.forEach(o),_s=n(tt," do c\xF3digo fonte."),tt.forEach(o),$s=m(rt),ua=t(rt,"LI",{});var Li=s(ua);gs=n(Li,"Contribuir ao \u{1F917} Transformers e precisa testar mudan\xE7as ao c\xF3digo."),Li.forEach(o),rt.forEach(o),wr=m(e),Mo=t(e,"P",{});var zi=s(Mo);Es=n(zi,"Para tal, clone o reposit\xF3rio e instale o \u{1F917} Transformers com os seguintes comandos:"),zi.forEach(o),Pr=m(e),v(De.$$.fragment,e),Tr=m(e),H=t(e,"P",{});var Wo=s(H);bs=n(Wo,`Estes comandos v\xE3o ligar o diret\xF3rio para o qual foi clonado o reposit\xF3rio ao caminho de bibliotecas do Python.
O Python agora buscar\xE1 dentro dos arquivos que foram clonados al\xE9m dos caminhos normais da biblioteca.
Por exemplo, se os pacotes do Python se encontram instalados no caminho `),ha=t(Wo,"CODE",{});var Ui=s(ha);ws=n(Ui,"~/anaconda3/envs/main/lib/python3.7/site-packages/"),Ui.forEach(o),Ps=n(Wo,`,
o Python tamb\xE9m buscar\xE1 m\xF3dulos no diret\xF3rio onde clonamos o reposit\xF3rio `),va=t(Wo,"CODE",{});var Bi=s(va);Ts=n(Bi,"~/transformers/"),Bi.forEach(o),qs=n(Wo,"."),Wo.forEach(o),qr=m(e),v(te.$$.fragment,e),yr=m(e),Ro=t(e,"P",{});var Vi=s(Ro);ys=n(Vi,"Assim, \xC9 poss\xEDvel atualizar sua c\xF3pia local para com a \xFAltima vers\xE3o do \u{1F917} Transformers com o seguinte comando:"),Vi.forEach(o),Ar=m(e),v(He.$$.fragment,e),Or=m(e),se=t(e,"P",{});var st=s(se);As=n(st,"O ambiente de Python que foi criado para a instala\xE7\xE3o do \u{1F917} Transformers encontrar\xE1 a vers\xE3o "),_a=t(st,"CODE",{});var Gi=s(_a);Os=n(Gi,"master"),Gi.forEach(o),xs=n(st," em execu\xE7\xF5es seguintes."),st.forEach(o),xr=m(e),G=t(e,"H2",{class:!0});var lt=s(G);le=t(lt,"A",{id:!0,class:!0,href:!0});var Yi=s(le);$a=t(Yi,"SPAN",{});var Qi=s($a);v(Le.$$.fragment,Qi),Qi.forEach(o),Yi.forEach(o),Cs=m(lt),ga=t(lt,"SPAN",{});var Wi=s(ga);ks=n(Wi,"Instala\xE7\xE3o usando o Conda"),Wi.forEach(o),lt.forEach(o),Cr=m(e),ie=t(e,"P",{});var it=s(ie);Ss=n(it,"\xC9 poss\xEDvel instalar o \u{1F917} Transformers a partir do canal conda "),Ea=t(it,"CODE",{});var Xi=s(Ea);js=n(Xi,"huggingface"),Xi.forEach(o),Is=n(it," com o seguinte comando:"),it.forEach(o),kr=m(e),v(ze.$$.fragment,e),Sr=m(e),Y=t(e,"H2",{class:!0});var nt=s(Y);ne=t(nt,"A",{id:!0,class:!0,href:!0});var Ji=s(ne);ba=t(Ji,"SPAN",{});var Ki=s(ba);v(Ue.$$.fragment,Ki),Ki.forEach(o),Ji.forEach(o),Fs=m(nt),wa=t(nt,"SPAN",{});var Zi=s(wa);Ms=n(Zi,"Configura\xE7\xE3o do Cach\xEA"),Zi.forEach(o),nt.forEach(o),jr=m(e),I=t(e,"P",{});var he=s(I);Rs=n(he,"Os modelos pr\xE9-treinados s\xE3o baixados e armazenados no cach\xEA local, encontrado em "),Pa=t(he,"CODE",{});var en=s(Pa);Ns=n(en,"~/.cache/huggingface/transformers/"),en.forEach(o),Ds=n(he,`.
Este \xE9 o diret\xF3rio padr\xE3o determinado pela vari\xE1vel `),Ta=t(he,"CODE",{});var on=s(Ta);Hs=n(on,"TRANSFORMERS_CACHE"),on.forEach(o),Ls=n(he,` dentro do shell.
No Windows, este diret\xF3rio pr\xE9-definido \xE9 dado por `),qa=t(he,"CODE",{});var an=s(qa);zs=n(an,"C:\\Users\\username\\.cache\\huggingface\\transformers"),an.forEach(o),Us=n(he,`.
\xC9 poss\xEDvel mudar as vari\xE1veis dentro do shell em ordem de prioridade para especificar um diret\xF3rio de cach\xEA diferente:`),he.forEach(o),Ir=m(e),L=t(e,"OL",{});var Xo=s(L);Be=t(Xo,"LI",{});var ft=s(Be);Bs=n(ft,"Vari\xE1vel de ambiente do shell (por padr\xE3o): "),ya=t(ft,"CODE",{});var rn=s(ya);Vs=n(rn,"TRANSFORMERS_CACHE"),rn.forEach(o),Gs=n(ft,"."),ft.forEach(o),Ys=m(Xo),Q=t(Xo,"LI",{});var Jo=s(Q);Qs=n(Jo,"Vari\xE1vel de ambiente do shell:"),Aa=t(Jo,"CODE",{});var tn=s(Aa);Ws=n(tn,"HF_HOME"),tn.forEach(o),Xs=n(Jo," + "),Oa=t(Jo,"CODE",{});var sn=s(Oa);Js=n(sn,"transformers/"),sn.forEach(o),Ks=n(Jo,"."),Jo.forEach(o),Zs=m(Xo),W=t(Xo,"LI",{});var Ko=s(W);el=n(Ko,"Vari\xE1vel de ambiente do shell: "),xa=t(Ko,"CODE",{});var ln=s(xa);ol=n(ln,"XDG_CACHE_HOME"),ln.forEach(o),al=n(Ko," + "),Ca=t(Ko,"CODE",{});var nn=s(Ca);rl=n(nn,"/huggingface/transformers"),nn.forEach(o),tl=n(Ko,"."),Ko.forEach(o),Xo.forEach(o),Fr=m(e),v(fe.$$.fragment,e),Mr=m(e),X=t(e,"H2",{class:!0});var pt=s(X);pe=t(pt,"A",{id:!0,class:!0,href:!0});var fn=s(pe);ka=t(fn,"SPAN",{});var pn=s(ka);v(Ve.$$.fragment,pn),pn.forEach(o),fn.forEach(o),sl=m(pt),Sa=t(pt,"SPAN",{});var mn=s(Sa);ll=n(mn,"Modo Offline"),mn.forEach(o),pt.forEach(o),Rr=m(e),me=t(e,"P",{});var mt=s(me);il=n(mt,`O \u{1F917} Transformers tamb\xE9m pode ser executado num ambiente de firewall ou fora da rede (offline) usando arquivos locais.
Para tal, configure a vari\xE1vel de ambiente de modo que `),ja=t(mt,"CODE",{});var dn=s(ja);nl=n(dn,"TRANSFORMERS_OFFLINE=1"),dn.forEach(o),fl=n(mt,"."),mt.forEach(o),Nr=m(e),v(de.$$.fragment,e),Dr=m(e),No=t(e,"P",{});var cn=s(No);pl=n(cn,"Segue um exemplo de execu\xE7\xE3o do programa numa rede padr\xE3o com firewall para inst\xE2ncias externas, usando o seguinte comando:"),cn.forEach(o),Hr=m(e),v(Ge.$$.fragment,e),Lr=m(e),Do=t(e,"P",{});var un=s(Do);ml=n(un,"Execute esse mesmo programa numa inst\xE2ncia offline com o seguinte comando:"),un.forEach(o),zr=m(e),v(Ye.$$.fragment,e),Ur=m(e),Ho=t(e,"P",{});var hn=s(Ho);dl=n(hn,"O script agora deve ser executado sem travar ou expirar, pois procurar\xE1 apenas por arquivos locais."),hn.forEach(o),Br=m(e),J=t(e,"H3",{class:!0});var dt=s(J);ce=t(dt,"A",{id:!0,class:!0,href:!0});var vn=s(ce);Ia=t(vn,"SPAN",{});var _n=s(Ia);v(Qe.$$.fragment,_n),_n.forEach(o),vn.forEach(o),cl=m(dt),Fa=t(dt,"SPAN",{});var $n=s(Fa);ul=n($n,"Obtendo modelos e tokenizers para uso offline"),$n.forEach(o),dt.forEach(o),Vr=m(e),Lo=t(e,"P",{});var gn=s(Lo);hl=n(gn,"Outra op\xE7\xE3o para usar o \u{1F917} Transformers offline \xE9 baixar os arquivos antes e depois apontar para o caminho local onde est\xE3o localizados. 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Nn=new bp({}),Bn=new bp({}),xn=new bp({}),On=new bp({}),Xi=new bp({}),{c(){Da=r("meta"),Lp=d(),Aa=r("h1"),Ma=r("a"),F3=r("span"),mp(Nn.$$.fragment),jp=d(),W3=r("span"),Qp=n("\u{1F917} Transformers"),Sp=d(),$i=r("p"),$p=n(`Estado da Arte para Aprendizado de M\xE1quina em PyTorch, TensorFlow e JAX.
O \u{1F917} Transformers disponibiliza APIs para facilmente baixar e treinar modelos pr\xE9-treinados de \xFAltima gera\xE7\xE3o.
O uso de modelos pr\xE9-treinados pode diminuir os seus custos de computa\xE7\xE3o, a sua pegada de carbono, al\xE9m de economizar o
tempo necess\xE1rio para se treinar um modelo do zero. Os modelos podem ser usados para diversas tarefas:`),wp=d(),vt=r("ul"),J3=r("li"),e6=n("\u{1F4DD} Textos: classifica\xE7\xE3o, extra\xE7\xE3o de informa\xE7\xF5es, perguntas e respostas, resumir, traduzir e gerar textos em mais de 100 idiomas."),t6=d(),Y3=r("li"),r6=n("\u{1F5BC} Imagens: classifica\xE7\xE3o, dete\xE7\xE3o de objetos, e segmenta\xE7\xE3o."),a6=d(),K3=r("li"),l6=n("\u{1F5E3} Audio: reconhecimento de fala e classifica\xE7\xE3o de \xE1udio."),n6=d(),V3=r("li"),o6=n(`\u{1F419} Multimodal: perguntas tabeladas e respsostas, reconhecimento \xF3tico de charact\xE9res, extra\xE7\xE3o de informa\xE7\xE3o de
documentos escaneados, classifica\xE7\xE3o de v\xEDdeo, perguntas e respostas visuais.`),Mp=d(),Et=r("p"),i6=n(`Nossa biblioteca aceita integra\xE7\xE3o cont\xEDnua entre tr\xEAs das bibliotecas mais populares de aprendizado profundo:
Our library supports seamless integration between three of the most popular deep learning libraries:
`),kn=r("a"),d6=n("PyTorch"),s6=n(", "),Cn=r("a"),h6=n("TensorFlow"),c6=n(" e "),In=r("a"),g6=n("JAX"),f6=n(`.
Treine seu modelo em tr\xEAs linhas de c\xF3digo em um framework, e carregue-o para execu\xE7\xE3o em outro.`),Pp=d(),ed=r("p"),u6=n("Cada arquitetura \u{1F917} Transformers \xE9 definida em um m\xF3dulo individual do Python, para que seja facilmente customiz\xE1vel para pesquisa e experimentos."),Gp=d(),Ra=r("h2"),Pa=r("a"),Z3=r("span"),mp(Bn.$$.fragment),v6=d(),X3=r("span"),E6=n("Se voc\xEA estiver procurando suporte do time da Hugging Face, acesse"),Np=d(),ya=r("a"),ua=r("img"),T6=n(`</img>
`),kp=r("br"),Cp=d(),ba=r("h2"),Ga=r("a"),z3=r("span"),mp(xn.$$.fragment),_6=d(),H3=r("span"),m6=n("Conte\xFAdo"),Ip=d(),td=r("p"),p6=n("A documenta\xE7\xE3o \xE9 dividida em cinco partes:"),Bp=d(),T=r("ul"),U3=r("li"),rd=r("p"),q3=r("strong"),D6=n("IN\xCDCIO"),A6=n(" cont\xE9m um tour r\xE1pido de instala\xE7\xE3o e instru\xE7\xF5es para te dar um empurr\xE3o inicial com os \u{1F917} Transformers."),R6=d(),j3=r("li"),ad=r("p"),Q3=r("strong"),y6=n("TUTORIAIS"),b6=n(` s\xE3o perfeitos para come\xE7ar a aprender sobre a nossa biblioteca. Essa se\xE7\xE3o ir\xE1 te ajudar a desenvolver
habilidades b\xE1sicas necess\xE1rias para usar o \u{1F917} Transformers.`),L6=d(),$3=r("li"),ld=r("p"),e5=r("strong"),S6=n("GUIAS PR\xC1TICOS"),w6=n(` ir\xE3o te mostrar como alcan\xE7ar um certo objetivo, como o fine-tuning de um modelo pr\xE9-treinado
para modelamento de idioma, ou como criar um cabe\xE7alho personalizado para um modelo.`),M6=d(),t5=r("li"),nd=r("p"),r5=r("strong"),P6=n("GUIAS CONCEITUAIS"),G6=n(` te dar\xE3o mais discuss\xF5es e explica\xE7\xF5es dos conceitos fundamentais e id\xE9ias por tr\xE1s dos modelos,
tarefas e da filosofia de design por tr\xE1s do \u{1F917} Transformers.`),N6=d(),a5=r("li"),od=r("p"),l5=r("strong"),k6=n("API"),C6=n(" descreve o funcionamento de cada classe e fun\xE7\xE3o, agrupada em:"),I6=d(),n5=r("li"),id=r("p"),o5=r("strong"),B6=n("CLASSES PRINCIPAIS"),x6=n(" para as classes que exp\xF5e as APIs importantes da biblioteca."),O6=d(),i5=r("li"),dd=r("p"),d5=r("strong"),F6=n("MODELOS"),W6=n(" para as classes e fun\xE7\xF5es relacionadas \xE0 cada modelo implementado na biblioteca."),J6=d(),s5=r("li"),sd=r("p"),h5=r("strong"),Y6=n("AUXILIARES INTERNOS"),K6=n(" para as classes e fun\xE7\xF5es usadas internamente."),xp=d(),hd=r("p"),V6=n("Atualmente a biblioteca cont\xE9m implementa\xE7\xF5es do PyTorch, TensorFlow e JAX, pesos para modelos pr\xE9-treinados e scripts de uso e convers\xE3o de utilidades para os seguintes modelos:"),Op=d(),La=r("h3"),Na=r("a"),c5=r("span"),mp(On.$$.fragment),Z6=d(),g5=r("span"),X6=n("Modelos atuais"),Fp=d(),h=r("ol"),ka=r("li"),f5=r("strong"),cd=r("a"),z6=n("ALBERT"),H6=n(" (from Google Research and the Toyota Technological Institute at Chicago) released with the paper "),Fn=r("a"),U6=n("ALBERT: A Lite BERT for Self-supervised Learning of Language Representations"),q6=n(", by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."),j6=d(),Ca=r("li"),u5=r("strong"),gd=r("a"),Q6=n("BART"),$6=n(" (from Facebook) released with the paper "),Wn=r("a"),e7=n("BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"),t7=n(" by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer."),r7=d(),Ia=r("li"),v5=r("strong"),fd=r("a"),a7=n("BARThez"),l7=n(" (from \xC9cole polytechnique) released with the paper "),Jn=r("a"),n7=n("BARThez: a Skilled Pretrained French Sequence-to-Sequence Model"),o7=n(" by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis."),i7=d(),Ba=r("li"),E5=r("strong"),ud=r("a"),d7=n("BARTpho"),s7=n(" (from VinAI Research) released with the paper "),Yn=r("a"),h7=n("BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese"),c7=n(" by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen."),g7=d(),xa=r("li"),T5=r("strong"),vd=r("a"),f7=n("BEiT"),u7=n(" (from Microsoft) released with the paper "),Kn=r("a"),v7=n("BEiT: BERT Pre-Training of Image Transformers"),E7=n(" by Hangbo Bao, Li Dong, Furu Wei."),T7=d(),Oa=r("li"),_5=r("strong"),Ed=r("a"),_7=n("BERT"),m7=n(" (from Google) released with the paper "),Vn=r("a"),p7=n("BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"),D7=n(" by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova."),A7=d(),Fa=r("li"),m5=r("strong"),Td=r("a"),R7=n("BERTweet"),y7=n(" (from VinAI Research) released with the paper "),Zn=r("a"),b7=n("BERTweet: A pre-trained language model for English Tweets"),L7=n(" by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen."),S7=d(),Wa=r("li"),p5=r("strong"),_d=r("a"),w7=n("BERT For Sequence Generation"),M7=n(" (from Google) released with the paper "),Xn=r("a"),P7=n("Leveraging Pre-trained Checkpoints for Sequence Generation Tasks"),G7=n(" by Sascha Rothe, Shashi Narayan, Aliaksei Severyn."),N7=d(),Ja=r("li"),D5=r("strong"),md=r("a"),k7=n("BigBird-RoBERTa"),C7=n(" (from Google Research) released with the paper "),zn=r("a"),I7=n("Big Bird: Transformers for Longer Sequences"),B7=n(" by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed."),x7=d(),Ya=r("li"),A5=r("strong"),pd=r("a"),O7=n("BigBird-Pegasus"),F7=n(" (from Google Research) released with the paper "),Hn=r("a"),W7=n("Big Bird: Transformers for Longer Sequences"),J7=n(" by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed."),Y7=d(),Ka=r("li"),R5=r("strong"),Dd=r("a"),K7=n("Blenderbot"),V7=n(" (from Facebook) released with the paper "),Un=r("a"),Z7=n("Recipes for building an open-domain chatbot"),X7=n(" by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston."),z7=d(),Va=r("li"),y5=r("strong"),Ad=r("a"),H7=n("BlenderbotSmall"),U7=n(" (from Facebook) released with the paper "),qn=r("a"),q7=n("Recipes for building an open-domain chatbot"),j7=n(" by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston."),Q7=d(),Za=r("li"),b5=r("strong"),Rd=r("a"),$7=n("BORT"),e8=n(" (from Alexa) released with the paper "),jn=r("a"),t8=n("Optimal Subarchitecture Extraction For BERT"),r8=n(" by Adrian de Wynter and Daniel J. Perry."),a8=d(),Xa=r("li"),L5=r("strong"),yd=r("a"),l8=n("ByT5"),n8=n(" (from Google Research) released with the paper "),Qn=r("a"),o8=n("ByT5: Towards a token-free future with pre-trained byte-to-byte models"),i8=n(" by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel."),d8=d(),va=r("li"),S5=r("strong"),bd=r("a"),s8=n("CamemBERT"),h8=n(" (from Inria/Facebook/Sorbonne) released with the paper "),$n=r("a"),c8=n("CamemBERT: a Tasty French Language Model"),g8=n(" by Louis Martin"),w5=r("em"),f8=n(", Benjamin Muller"),u8=n(", Pedro Javier Ortiz Su\xE1rez*, Yoann Dupont, Laurent Romary, \xC9ric Villemonte de la Clergerie, Djam\xE9 Seddah and Beno\xEEt Sagot."),v8=d(),za=r("li"),M5=r("strong"),Ld=r("a"),E8=n("CANINE"),T8=n(" (from Google Research) released with the paper "),eo=r("a"),_8=n("CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation"),m8=n(" by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting."),p8=d(),Ha=r("li"),P5=r("strong"),Sd=r("a"),D8=n("ConvNeXT"),A8=n(" (from Facebook AI) released with the paper "),to=r("a"),R8=n("A ConvNet for the 2020s"),y8=n(" by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie."),b8=d(),Ua=r("li"),G5=r("strong"),wd=r("a"),L8=n("CLIP"),S8=n(" (from OpenAI) released with the paper "),ro=r("a"),w8=n("Learning Transferable Visual Models From Natural Language Supervision"),M8=n(" by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever."),P8=d(),qa=r("li"),N5=r("strong"),Md=r("a"),G8=n("ConvBERT"),N8=n(" (from YituTech) released with the paper "),ao=r("a"),k8=n("ConvBERT: Improving BERT with Span-based Dynamic Convolution"),C8=n(" by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan."),I8=d(),ja=r("li"),k5=r("strong"),Pd=r("a"),B8=n("CPM"),x8=n(" (from Tsinghua University) released with the paper "),lo=r("a"),O8=n("CPM: A Large-scale Generative Chinese Pre-trained Language Model"),F8=n(" by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun."),W8=d(),Ea=r("li"),C5=r("strong"),Gd=r("a"),J8=n("CTRL"),Y8=n(" (from Salesforce) released with the paper "),no=r("a"),K8=n("CTRL: A Conditional Transformer Language Model for Controllable Generation"),V8=n(" by Nitish Shirish Keskar"),I5=r("em"),Z8=n(", Bryan McCann"),X8=n(", Lav R. Varshney, Caiming Xiong and Richard Socher."),z8=d(),Qa=r("li"),B5=r("strong"),Nd=r("a"),H8=n("Data2Vec"),U8=n(" (from Facebook) released with the paper "),oo=r("a"),q8=n("Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language"),j8=n(" by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli."),Q8=d(),$a=r("li"),x5=r("strong"),kd=r("a"),$8=n("DeBERTa"),e9=n(" (from Microsoft) released with the paper "),io=r("a"),t9=n("DeBERTa: Decoding-enhanced BERT with Disentangled Attention"),r9=n(" by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen."),a9=d(),el=r("li"),O5=r("strong"),Cd=r("a"),l9=n("DeBERTa-v2"),n9=n(" (from Microsoft) released with the paper "),so=r("a"),o9=n("DeBERTa: Decoding-enhanced BERT with Disentangled Attention"),i9=n(" by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen."),d9=d(),tl=r("li"),F5=r("strong"),Id=r("a"),s9=n("Decision Transformer"),h9=n(" (from Berkeley/Facebook/Google) released with the paper "),ho=r("a"),c9=n("Decision Transformer: Reinforcement Learning via Sequence Modeling"),g9=n(" by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch."),f9=d(),rl=r("li"),W5=r("strong"),Bd=r("a"),u9=n("DiT"),v9=n(" (from Microsoft Research) released with the paper "),co=r("a"),E9=n("DiT: Self-supervised Pre-training for Document Image Transformer"),T9=n(" by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei."),_9=d(),al=r("li"),J5=r("strong"),xd=r("a"),m9=n("DeiT"),p9=n(" (from Facebook) released with the paper "),go=r("a"),D9=n("Training data-efficient image transformers & distillation through attention"),A9=n(" by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Herv\xE9 J\xE9gou."),R9=d(),ll=r("li"),Y5=r("strong"),Od=r("a"),y9=n("DETR"),b9=n(" (from Facebook) released with the paper "),fo=r("a"),L9=n("End-to-End Object Detection with Transformers"),S9=n(" by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko."),w9=d(),nl=r("li"),K5=r("strong"),Fd=r("a"),M9=n("DialoGPT"),P9=n(" (from Microsoft Research) released with the paper "),uo=r("a"),G9=n("DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation"),N9=n(" by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan."),k9=d(),ut=r("li"),V5=r("strong"),Wd=r("a"),C9=n("DistilBERT"),I9=n(" (from HuggingFace), released together with the paper "),vo=r("a"),B9=n("DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"),x9=n(" by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into "),Eo=r("a"),O9=n("DistilGPT2"),F9=n(", RoBERTa into "),To=r("a"),W9=n("DistilRoBERTa"),J9=n(", Multilingual BERT into "),_o=r("a"),Y9=n("DistilmBERT"),K9=n(" and a German version of DistilBERT."),V9=d(),ol=r("li"),Z5=r("strong"),Jd=r("a"),Z9=n("DPR"),X9=n(" (from Facebook) released with the paper "),mo=r("a"),z9=n("Dense Passage Retrieval for Open-Domain Question Answering"),H9=n(" by Vladimir Karpukhin, Barlas O\u011Fuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih."),U9=d(),il=r("li"),X5=r("strong"),Yd=r("a"),q9=n("DPT"),j9=n(" (from Intel Labs) released with the paper "),po=r("a"),Q9=n("Vision Transformers for Dense Prediction"),$9=n(" by Ren\xE9 Ranftl, Alexey Bochkovskiy, Vladlen Koltun."),eD=d(),dl=r("li"),z5=r("strong"),Kd=r("a"),tD=n("EncoderDecoder"),rD=n(" (from Google Research) released with the paper "),Do=r("a"),aD=n("Leveraging Pre-trained Checkpoints for Sequence Generation Tasks"),lD=n(" by Sascha Rothe, Shashi Narayan, Aliaksei Severyn."),nD=d(),sl=r("li"),H5=r("strong"),Vd=r("a"),oD=n("ELECTRA"),iD=n(" (from Google Research/Stanford University) released with the paper "),Ao=r("a"),dD=n("ELECTRA: Pre-training text encoders as discriminators rather than generators"),sD=n(" by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning."),hD=d(),hl=r("li"),U5=r("strong"),Zd=r("a"),cD=n("FlauBERT"),gD=n(" (from CNRS) released with the paper "),Ro=r("a"),fD=n("FlauBERT: Unsupervised Language Model Pre-training for French"),uD=n(" by Hang Le, Lo\xEFc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Beno\xEEt Crabb\xE9, Laurent Besacier, Didier Schwab."),vD=d(),cl=r("li"),q5=r("strong"),Xd=r("a"),ED=n("FNet"),TD=n(" (from Google Research) released with the paper "),yo=r("a"),_D=n("FNet: Mixing Tokens with Fourier Transforms"),mD=n(" by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon."),pD=d(),gl=r("li"),j5=r("strong"),zd=r("a"),DD=n("Funnel Transformer"),AD=n(" (from CMU/Google Brain) released with the paper "),bo=r("a"),RD=n("Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing"),yD=n(" by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le."),bD=d(),fl=r("li"),Q5=r("strong"),Hd=r("a"),LD=n("GLPN"),SD=n(" (from KAIST) released with the paper "),Lo=r("a"),wD=n("Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth"),MD=n(" by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim."),PD=d(),ul=r("li"),$5=r("strong"),Ud=r("a"),GD=n("GPT"),ND=n(" (from OpenAI) released with the paper "),So=r("a"),kD=n("Improving Language Understanding by Generative Pre-Training"),CD=n(" by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever."),ID=d(),Tt=r("li"),e4=r("strong"),qd=r("a"),BD=n("GPT-2"),xD=n(" (from OpenAI) released with the paper "),wo=r("a"),OD=n("Language Models are Unsupervised Multitask Learners"),FD=n(" by Alec Radford"),t4=r("em"),WD=n(", Jeffrey Wu"),JD=n(", Rewon Child, David Luan, Dario Amodei"),r4=r("strong"),YD=n("and Ilya Sutskever"),KD=n("."),VD=d(),vl=r("li"),a4=r("strong"),jd=r("a"),ZD=n("GPT-J"),XD=n(" (from EleutherAI) released in the repository "),Mo=r("a"),zD=n("kingoflolz/mesh-transformer-jax"),HD=n(" by Ben Wang and Aran Komatsuzaki."),UD=d(),El=r("li"),l4=r("strong"),Qd=r("a"),qD=n("GPT Neo"),jD=n(" (from EleutherAI) released in the repository "),Po=r("a"),QD=n("EleutherAI/gpt-neo"),$D=n(" by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy."),eA=d(),Tl=r("li"),n4=r("strong"),$d=r("a"),tA=n("Hubert"),rA=n(" (from Facebook) released with the paper "),Go=r("a"),aA=n("HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units"),lA=n(" by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed."),nA=d(),_l=r("li"),o4=r("strong"),es=r("a"),oA=n("I-BERT"),iA=n(" (from Berkeley) released with the paper "),No=r("a"),dA=n("I-BERT: Integer-only BERT Quantization"),sA=n(" by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer."),hA=d(),ml=r("li"),i4=r("strong"),ts=r("a"),cA=n("ImageGPT"),gA=n(" (from OpenAI) released with the paper "),ko=r("a"),fA=n("Generative Pretraining from Pixels"),uA=n(" by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever."),vA=d(),pl=r("li"),d4=r("strong"),rs=r("a"),EA=n("LayoutLM"),TA=n(" (from Microsoft Research Asia) released with the paper "),Co=r("a"),_A=n("LayoutLM: Pre-training of Text and Layout for Document Image Understanding"),mA=n(" by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou."),pA=d(),Dl=r("li"),s4=r("strong"),as=r("a"),DA=n("LayoutLMv2"),AA=n(" (from Microsoft Research Asia) released with the paper "),Io=r("a"),RA=n("LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding"),yA=n(" by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou."),bA=d(),Al=r("li"),h4=r("strong"),ls=r("a"),LA=n("LayoutXLM"),SA=n(" (from Microsoft Research Asia) released with the paper "),Bo=r("a"),wA=n("LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding"),MA=n(" by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei."),PA=d(),Rl=r("li"),c4=r("strong"),ns=r("a"),GA=n("LED"),NA=n(" (from AllenAI) released with the paper "),xo=r("a"),kA=n("Longformer: The Long-Document Transformer"),CA=n(" by Iz Beltagy, Matthew E. Peters, Arman Cohan."),IA=d(),yl=r("li"),g4=r("strong"),os=r("a"),BA=n("Longformer"),xA=n(" (from AllenAI) released with the paper "),Oo=r("a"),OA=n("Longformer: The Long-Document Transformer"),FA=n(" by Iz Beltagy, Matthew E. Peters, Arman Cohan."),WA=d(),bl=r("li"),f4=r("strong"),is=r("a"),JA=n("LUKE"),YA=n(" (from Studio Ousia) released with the paper "),Fo=r("a"),KA=n("LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention"),VA=n(" by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto."),ZA=d(),Ll=r("li"),u4=r("strong"),ds=r("a"),XA=n("mLUKE"),zA=n(" (from Studio Ousia) released with the paper "),Wo=r("a"),HA=n("mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models"),UA=n(" by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka."),qA=d(),Sl=r("li"),v4=r("strong"),ss=r("a"),jA=n("LXMERT"),QA=n(" (from UNC Chapel Hill) released with the paper "),Jo=r("a"),$A=n("LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering"),eR=n(" by Hao Tan and Mohit Bansal."),tR=d(),wl=r("li"),E4=r("strong"),hs=r("a"),rR=n("M2M100"),aR=n(" (from Facebook) released with the paper "),Yo=r("a"),lR=n("Beyond English-Centric Multilingual Machine Translation"),nR=n(" by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin."),oR=d(),Ta=r("li"),T4=r("strong"),cs=r("a"),iR=n("MarianMT"),dR=n(" Machine translation models trained using "),Ko=r("a"),sR=n("OPUS"),hR=n(" data by J\xF6rg Tiedemann. The "),Vo=r("a"),cR=n("Marian Framework"),gR=n(" is being developed by the Microsoft Translator Team."),fR=d(),Ml=r("li"),_4=r("strong"),gs=r("a"),uR=n("MaskFormer"),vR=n(" (from Meta and UIUC) released with the paper "),Zo=r("a"),ER=n("Per-Pixel Classification is Not All You Need for Semantic Segmentation"),TR=n(" by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov."),_R=d(),Pl=r("li"),m4=r("strong"),fs=r("a"),mR=n("MBart"),pR=n(" (from Facebook) released with the paper "),Xo=r("a"),DR=n("Multilingual Denoising Pre-training for Neural Machine Translation"),AR=n(" by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer."),RR=d(),Gl=r("li"),p4=r("strong"),us=r("a"),yR=n("MBart-50"),bR=n(" (from Facebook) released with the paper "),zo=r("a"),LR=n("Multilingual Translation with Extensible Multilingual Pretraining and Finetuning"),SR=n(" by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan."),wR=d(),Nl=r("li"),D4=r("strong"),vs=r("a"),MR=n("Megatron-BERT"),PR=n(" (from NVIDIA) released with the paper "),Ho=r("a"),GR=n("Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism"),NR=n(" by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro."),kR=d(),kl=r("li"),A4=r("strong"),Es=r("a"),CR=n("Megatron-GPT2"),IR=n(" (from NVIDIA) released with the paper "),Uo=r("a"),BR=n("Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism"),xR=n(" by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro."),OR=d(),Cl=r("li"),R4=r("strong"),Ts=r("a"),FR=n("MPNet"),WR=n(" (from Microsoft Research) released with the paper "),qo=r("a"),JR=n("MPNet: Masked and Permuted Pre-training for Language Understanding"),YR=n(" by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu."),KR=d(),Il=r("li"),y4=r("strong"),_s=r("a"),VR=n("MT5"),ZR=n(" (from Google AI) released with the paper "),jo=r("a"),XR=n("mT5: A massively multilingual pre-trained text-to-text transformer"),zR=n(" by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel."),HR=d(),Bl=r("li"),b4=r("strong"),ms=r("a"),UR=n("Nystr\xF6mformer"),qR=n(" (from the University of Wisconsin - Madison) released with the paper "),Qo=r("a"),jR=n("Nystr\xF6mformer: A Nystr\xF6m-Based Algorithm for Approximating Self-Attention"),QR=n(" by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh."),$R=d(),xl=r("li"),L4=r("strong"),ps=r("a"),ey=n("Pegasus"),ty=n(" (from Google) released with the paper "),$o=r("a"),ry=n("PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization"),ay=n(" by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu."),ly=d(),Ol=r("li"),S4=r("strong"),Ds=r("a"),ny=n("Perceiver IO"),oy=n(" (from Deepmind) released with the paper "),ei=r("a"),iy=n("Perceiver IO: A General Architecture for Structured Inputs & Outputs"),dy=n(" by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier H\xE9naff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, Jo\xE3o Carreira."),sy=d(),Fl=r("li"),w4=r("strong"),As=r("a"),hy=n("PhoBERT"),cy=n(" (from VinAI Research) released with the paper "),ti=r("a"),gy=n("PhoBERT: Pre-trained language models for Vietnamese"),fy=n(" by Dat Quoc Nguyen and Anh Tuan Nguyen."),uy=d(),Wl=r("li"),M4=r("strong"),Rs=r("a"),vy=n("PLBart"),Ey=n(" (from UCLA NLP) released with the paper "),ri=r("a"),Ty=n("Unified Pre-training for Program Understanding and Generation"),_y=n(" by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang."),my=d(),Jl=r("li"),P4=r("strong"),ys=r("a"),py=n("PoolFormer"),Dy=n(" (from Sea AI Labs) released with the paper "),ai=r("a"),Ay=n("MetaFormer is Actually What You Need for Vision"),Ry=n(" by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng."),yy=d(),Yl=r("li"),G4=r("strong"),bs=r("a"),by=n("ProphetNet"),Ly=n(" (from Microsoft Research) released with the paper "),li=r("a"),Sy=n("ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training"),wy=n(" by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou."),My=d(),Kl=r("li"),N4=r("strong"),Ls=r("a"),Py=n("QDQBert"),Gy=n(" (from NVIDIA) released with the paper "),ni=r("a"),Ny=n("Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation"),ky=n(" by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius."),Cy=d(),Vl=r("li"),k4=r("strong"),Ss=r("a"),Iy=n("REALM"),By=n(" (from Google Research) released with the paper "),oi=r("a"),xy=n("REALM: Retrieval-Augmented Language Model Pre-Training"),Oy=n(" by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang."),Fy=d(),Zl=r("li"),C4=r("strong"),ws=r("a"),Wy=n("Reformer"),Jy=n(" (from Google Research) released with the paper "),ii=r("a"),Yy=n("Reformer: The Efficient Transformer"),Ky=n(" by Nikita Kitaev, \u0141ukasz Kaiser, Anselm Levskaya."),Vy=d(),Xl=r("li"),I4=r("strong"),Ms=r("a"),Zy=n("RemBERT"),Xy=n(" (from Google Research) released with the paper "),di=r("a"),zy=n("Rethinking embedding coupling in pre-trained language models"),Hy=n(" by Hyung Won Chung, Thibault F\xE9vry, Henry Tsai, M. Johnson, Sebastian Ruder."),Uy=d(),zl=r("li"),B4=r("strong"),Ps=r("a"),qy=n("RegNet"),jy=n(" (from META Platforms) released with the paper "),si=r("a"),Qy=n("Designing Network Design Space"),$y=n(" by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Doll\xE1r."),eb=d(),Hl=r("li"),x4=r("strong"),Gs=r("a"),tb=n("ResNet"),rb=n(" (from Microsoft Research) released with the paper "),hi=r("a"),ab=n("Deep Residual Learning for Image Recognition"),lb=n(" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun."),nb=d(),Ul=r("li"),O4=r("strong"),Ns=r("a"),ob=n("RoBERTa"),ib=n(" (from Facebook), released together with the paper "),ci=r("a"),db=n("RoBERTa: A Robustly Optimized BERT Pretraining Approach"),sb=n(" by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov."),hb=d(),ql=r("li"),F4=r("strong"),ks=r("a"),cb=n("RoFormer"),gb=n(" (from ZhuiyiTechnology), released together with the paper "),gi=r("a"),fb=n("RoFormer: Enhanced Transformer with Rotary Position Embedding"),ub=n(" by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu."),vb=d(),jl=r("li"),W4=r("strong"),Cs=r("a"),Eb=n("SegFormer"),Tb=n(" (from NVIDIA) released with the paper "),fi=r("a"),_b=n("SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers"),mb=n(" by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo."),pb=d(),Ql=r("li"),J4=r("strong"),Is=r("a"),Db=n("SEW"),Ab=n(" (from ASAPP) released with the paper "),ui=r("a"),Rb=n("Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition"),yb=n(" by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi."),bb=d(),$l=r("li"),Y4=r("strong"),Bs=r("a"),Lb=n("SEW-D"),Sb=n(" (from ASAPP) released with the paper "),vi=r("a"),wb=n("Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition"),Mb=n(" by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi."),Pb=d(),en=r("li"),K4=r("strong"),xs=r("a"),Gb=n("SpeechToTextTransformer"),Nb=n(" (from Facebook), released together with the paper "),Ei=r("a"),kb=n("fairseq S2T: Fast Speech-to-Text Modeling with fairseq"),Cb=n(" by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino."),Ib=d(),tn=r("li"),V4=r("strong"),Os=r("a"),Bb=n("SpeechToTextTransformer2"),xb=n(" (from Facebook), released together with the paper "),Ti=r("a"),Ob=n("Large-Scale Self- and Semi-Supervised Learning for Speech Translation"),Fb=n(" by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau."),Wb=d(),rn=r("li"),Z4=r("strong"),Fs=r("a"),Jb=n("Splinter"),Yb=n(" (from Tel Aviv University), released together with the paper "),_i=r("a"),Kb=n("Few-Shot Question Answering by Pretraining Span Selection"),Vb=n(" by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy."),Zb=d(),an=r("li"),X4=r("strong"),Ws=r("a"),Xb=n("SqueezeBert"),zb=n(" (from Berkeley) released with the paper "),mi=r("a"),Hb=n("SqueezeBERT: What can computer vision teach NLP about efficient neural networks?"),Ub=n(" by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer."),qb=d(),ln=r("li"),z4=r("strong"),Js=r("a"),jb=n("Swin Transformer"),Qb=n(" (from Microsoft) released with the paper "),pi=r("a"),$b=n("Swin Transformer: Hierarchical Vision Transformer using Shifted Windows"),eL=n(" by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo."),tL=d(),nn=r("li"),H4=r("strong"),Ys=r("a"),rL=n("T5"),aL=n(" (from Google AI) released with the paper "),Di=r("a"),lL=n("Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"),nL=n(" by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu."),oL=d(),on=r("li"),U4=r("strong"),Ks=r("a"),iL=n("T5v1.1"),dL=n(" (from Google AI) released in the repository "),Ai=r("a"),sL=n("google-research/text-to-text-transfer-transformer"),hL=n(" by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu."),cL=d(),dn=r("li"),q4=r("strong"),Vs=r("a"),gL=n("TAPAS"),fL=n(" (from Google AI) released with the paper "),Ri=r("a"),uL=n("TAPAS: Weakly Supervised Table Parsing via Pre-training"),vL=n(" by Jonathan Herzig, Pawe\u0142 Krzysztof Nowak, Thomas M\xFCller, Francesco Piccinno and Julian Martin Eisenschlos."),EL=d(),sn=r("li"),j4=r("strong"),Zs=r("a"),TL=n("TAPEX"),_L=n(" (from Microsoft Research) released with the paper "),yi=r("a"),mL=n("TAPEX: Table Pre-training via Learning a Neural SQL Executor"),pL=n(" by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou."),DL=d(),_a=r("li"),Q4=r("strong"),Xs=r("a"),AL=n("Transformer-XL"),RL=n(" (from Google/CMU) released with the paper "),bi=r("a"),yL=n("Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"),bL=n(" by Zihang Dai"),$4=r("em"),LL=n(", Zhilin Yang"),SL=n(", Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov."),wL=d(),hn=r("li"),em=r("strong"),zs=r("a"),ML=n("TrOCR"),PL=n(" (from Microsoft), released together with the paper "),Li=r("a"),GL=n("TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models"),NL=n(" by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei."),kL=d(),cn=r("li"),tm=r("strong"),Hs=r("a"),CL=n("UniSpeech"),IL=n(" (from Microsoft Research) released with the paper "),Si=r("a"),BL=n("UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data"),xL=n(" by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang."),OL=d(),gn=r("li"),rm=r("strong"),Us=r("a"),FL=n("UniSpeechSat"),WL=n(" (from Microsoft Research) released with the paper "),wi=r("a"),JL=n("UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING"),YL=n(" by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu."),KL=d(),fn=r("li"),am=r("strong"),qs=r("a"),VL=n("VAN"),ZL=n(" (from Tsinghua University and Nankai University) released with the paper "),Mi=r("a"),XL=n("Visual Attention Network"),zL=n(" by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu."),HL=d(),un=r("li"),lm=r("strong"),js=r("a"),UL=n("ViLT"),qL=n(" (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper "),Pi=r("a"),jL=n("ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"),QL=n(" by Wonjae Kim, Bokyung Son, Ildoo Kim."),$L=d(),vn=r("li"),nm=r("strong"),Qs=r("a"),eS=n("Vision Transformer (ViT)"),tS=n(" (from Google AI) released with the paper "),Gi=r("a"),rS=n("An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"),aS=n(" by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby."),lS=d(),En=r("li"),om=r("strong"),$s=r("a"),nS=n("ViTMAE"),oS=n(" (from Meta AI) released with the paper "),Ni=r("a"),iS=n("Masked Autoencoders Are Scalable Vision Learners"),dS=n(" by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll\xE1r, Ross Girshick."),sS=d(),Tn=r("li"),im=r("strong"),eh=r("a"),hS=n("VisualBERT"),cS=n(" (from UCLA NLP) released with the paper "),ki=r("a"),gS=n("VisualBERT: A Simple and Performant Baseline for Vision and Language"),fS=n(" by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang."),uS=d(),_n=r("li"),dm=r("strong"),th=r("a"),vS=n("WavLM"),ES=n(" (from Microsoft Research) released with the paper "),Ci=r("a"),TS=n("WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing"),_S=n(" by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei."),mS=d(),mn=r("li"),sm=r("strong"),rh=r("a"),pS=n("Wav2Vec2"),DS=n(" (from Facebook AI) released with the paper "),Ii=r("a"),AS=n("wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations"),RS=n(" by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli."),yS=d(),pn=r("li"),hm=r("strong"),ah=r("a"),bS=n("Wav2Vec2Phoneme"),LS=n(" (from Facebook AI) released with the paper "),Bi=r("a"),SS=n("Simple and Effective Zero-shot Cross-lingual Phoneme Recognition"),wS=n(" by Qiantong Xu, Alexei Baevski, Michael Auli."),MS=d(),Dn=r("li"),cm=r("strong"),lh=r("a"),PS=n("XGLM"),GS=n(" (From Facebook AI) released with the paper "),xi=r("a"),NS=n("Few-shot Learning with Multilingual Language Models"),kS=n(" by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O\u2019Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li."),CS=d(),An=r("li"),gm=r("strong"),nh=r("a"),IS=n("XLM"),BS=n(" (from Facebook) released together with the paper "),Oi=r("a"),xS=n("Cross-lingual Language Model Pretraining"),OS=n(" by Guillaume Lample and Alexis Conneau."),FS=d(),Rn=r("li"),fm=r("strong"),oh=r("a"),WS=n("XLM-ProphetNet"),JS=n(" (from Microsoft Research) released with the paper "),Fi=r("a"),YS=n("ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training"),KS=n(" by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou."),VS=d(),ma=r("li"),um=r("strong"),ih=r("a"),ZS=n("XLM-RoBERTa"),XS=n(" (from Facebook AI), released together with the paper "),Wi=r("a"),zS=n("Unsupervised Cross-lingual Representation Learning at Scale"),HS=n(" by Alexis Conneau"),vm=r("em"),US=n(", Kartikay Khandelwal"),qS=n(", Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzm\xE1n, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov."),jS=d(),yn=r("li"),Em=r("strong"),dh=r("a"),QS=n("XLM-RoBERTa-XL"),$S=n(" (from Facebook AI), released together with the paper "),Ji=r("a"),ew=n("Larger-Scale Transformers for Multilingual Masked Language Modeling"),tw=n(" by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau."),rw=d(),pa=r("li"),Tm=r("strong"),sh=r("a"),aw=n("XLNet"),lw=n(" (from Google/CMU) released with the paper "),Yi=r("a"),nw=n("\u200BXLNet: Generalized Autoregressive Pretraining for Language Understanding"),ow=n(" by Zhilin Yang"),_m=r("em"),iw=n(", Zihang Dai"),dw=n(", Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le."),sw=d(),bn=r("li"),mm=r("strong"),hh=r("a"),hw=n("XLSR-Wav2Vec2"),cw=n(" (from Facebook AI) released with the paper "),Ki=r("a"),gw=n("Unsupervised Cross-Lingual Representation Learning For Speech Recognition"),fw=n(" by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli."),uw=d(),Ln=r("li"),pm=r("strong"),ch=r("a"),vw=n("XLS-R"),Ew=n(" (from Facebook AI) released with the paper "),Vi=r("a"),Tw=n("XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale"),_w=n(" by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli."),mw=d(),Sn=r("li"),Dm=r("strong"),gh=r("a"),pw=n("YOSO"),Dw=n(" (from the University of Wisconsin - Madison) released with the paper "),Zi=r("a"),Aw=n("You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling"),Rw=n(" by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh."),Wp=d(),Sa=r("h3"),wn=r("a"),Am=r("span"),mp(Xi.$$.fragment),yw=d(),Rm=r("span"),bw=n("Frameworks aceitos"),Jp=d(),fh=r("p"),Lw=n(`A tabela abaixo representa a lista de suporte na biblioteca para cada um dos seguintes modelos, caso tenham um tokenizer
do Python (chamado de \u201Cslow\u201D), ou um tokenizer constru\xEDdo em cima da biblioteca \u{1F917} Tokenizers (chamado de \u201Cfast\u201D). Al\xE9m
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Eve={local:"transformers",sections:[{local:"se-voc-estiver-procurando-suporte-do-time-da-hugging-face-acesse",title:"Se voc\xEA estiver procurando suporte do time da Hugging Face, acesse"},{local:"contedo",sections:[{local:"modelos-atuais",title:"Modelos atuais"},{local:"frameworks-aceitos",title:"Frameworks aceitos"}],title:"Conte\xFAdo"}],title:"\u{1F917} Transformers"};function Tve(Mj){return uve(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class pve extends dve{constructor(Da){super();sve(this,Da,Tve,vve,hve,{})}}export{pve as default,Eve as metadata};
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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/fast_tokenizers.mdx-hf-doc-builder.js | import{S as bo,i as Po,s as wo,e as r,k as m,w as C,t as i,M as qo,c as t,d as o,m as d,a as n,x as O,h as l,b as f,G as a,g as p,y as F,L as yo,q as x,o as B,B as J,v as Ao}from"../chunks/vendor-hf-doc-builder.js";import{I as ao}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as be}from"../chunks/CodeBlock-hf-doc-builder.js";function So(so){let h,re,z,_,I,b,Pe,G,we,te,c,qe,R,ye,Ae,P,Se,Ne,ne,U,Ce,ie,w,le,D,Oe,pe,v,$,Q,q,Fe,X,xe,me,u,Be,Y,Je,Ue,Z,De,Ke,de,y,fe,j,Me,K,Ve,Le,ce,g,T,ee,A,We,oe,He,ue,M,Ie,ke,S,he,k,Ge,ae,Re,Qe,se,Xe,Ye,ze,N,ve,E,Ze,V,eo,oo,ge;return b=new ao({}),w=new be({props:{code:`from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import Whitespace
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.pre_tokenizer = Whitespace()
files = [...]
tokenizer.train(files, trainer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers <span class="hljs-keyword">import</span> Tokenizer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers.models <span class="hljs-keyword">import</span> BPE
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers.trainers <span class="hljs-keyword">import</span> BpeTrainer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tokenizers.pre_tokenizers <span class="hljs-keyword">import</span> Whitespace
<span class="hljs-meta">>>> </span>tokenizer = Tokenizer(BPE(unk_token=<span class="hljs-string">"[UNK]"</span>))
<span class="hljs-meta">>>> </span>trainer = BpeTrainer(special_tokens=[<span class="hljs-string">"[UNK]"</span>, <span class="hljs-string">"[CLS]"</span>, <span class="hljs-string">"[SEP]"</span>, <span class="hljs-string">"[PAD]"</span>, <span class="hljs-string">"[MASK]"</span>])
<span class="hljs-meta">>>> </span>tokenizer.pre_tokenizer = Whitespace()
<span class="hljs-meta">>>> </span>files = [...]
<span class="hljs-meta">>>> </span>tokenizer.train(files, trainer)`}}),q=new ao({}),y=new be({props:{code:`from transformers import PreTrainedTokenizerFast
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PreTrainedTokenizerFast
<span class="hljs-meta">>>> </span>fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)`}}),A=new ao({}),S=new be({props:{code:'tokenizer.save("tokenizer.json")',highlighted:'<span class="hljs-meta">>>> </span>tokenizer.save(<span class="hljs-string">"tokenizer.json"</span>)'}}),N=new be({props:{code:`from transformers import PreTrainedTokenizerFast
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PreTrainedTokenizerFast
<span class="hljs-meta">>>> </span>fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file=<span class="hljs-string">"tokenizer.json"</span>)`}}),{c(){h=r("meta"),re=m(),z=r("h1"),_=r("a"),I=r("span"),C(b.$$.fragment),Pe=m(),G=r("span"),we=i("Usando os Tokenizers do \u{1F917} Tokenizers"),te=m(),c=r("p"),qe=i("O "),R=r("code"),ye=i("PreTrainedTokenizerFast"),Ae=i(" depende da biblioteca "),P=r("a"),Se=i("\u{1F917} Tokenizers"),Ne=i(". O Tokenizer obtido da biblioteca \u{1F917} Tokenizers pode ser carregado facilmente pelo \u{1F917} Transformers."),ne=m(),U=r("p"),Ce=i("Antes de entrar nos detalhes, vamos come\xE7ar criando um tokenizer fict\xEDcio em algumas linhas:"),ie=m(),C(w.$$.fragment),le=m(),D=r("p"),Oe=i("Agora temos um tokenizer treinado nos arquivos que foram definidos. N\xF3s podemos continuar usando nessa execu\xE7\xE3o ou salvar em um arquivo JSON para re-utilizar no futuro."),pe=m(),v=r("h2"),$=r("a"),Q=r("span"),C(q.$$.fragment),Fe=m(),X=r("span"),xe=i("Carregando diretamente de um objeto tokenizer"),me=m(),u=r("p"),Be=i("Vamos ver como aproveitar esse objeto tokenizer na biblioteca \u{1F917} Transformers. A classe "),Y=r("code"),Je=i("PreTrainedTokenizerFast"),Ue=i(" permite uma instancia\xE7\xE3o f\xE1cil, aceitando o objeto "),Z=r("em"),De=i("tokenizer"),Ke=i(" instanciado como um argumento:"),de=m(),C(y.$$.fragment),fe=m(),j=r("p"),Me=i("Esse objeto pode ser utilizado com todos os m\xE9todos compartilhados pelos tokenizers dos \u{1F917} Transformers! V\xE1 para "),K=r("a"),Ve=i("a p\xE1gina do tokenizer"),Le=i(" para mais informa\xE7\xF5es."),ce=m(),g=r("h2"),T=r("a"),ee=r("span"),C(A.$$.fragment),We=m(),oe=r("span"),He=i("Carregando de um arquivo JSON"),ue=m(),M=r("p"),Ie=i("Para carregar um tokenizer de um arquivo JSON vamos primeiro come\xE7ar salvando nosso tokenizer:"),ke=m(),C(S.$$.fragment),he=m(),k=r("p"),Ge=i("A pasta para qual salvamos esse arquivo pode ser passada para o m\xE9todo de inicializa\xE7\xE3o do "),ae=r("code"),Re=i("PreTrainedTokenizerFast"),Qe=i(" usando o "),se=r("code"),Xe=i("tokenizer_file"),Ye=i(" par\xE2metro:"),ze=m(),C(N.$$.fragment),ve=m(),E=r("p"),Ze=i("Esse objeto pode ser utilizado com todos os m\xE9todos compartilhados pelos tokenizers dos \u{1F917} Transformers! 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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/pipeline_tutorial.mdx-hf-doc-builder.js | import{S as no,i as io,s as po,e as r,k as m,w as u,t,M as mo,c as n,d as s,m as c,a as i,x as h,h as o,b as d,N as co,G as a,g as p,y as g,q as v,o as $,B as _,v as fo}from"../chunks/vendor-hf-doc-builder.js";import{T as uo}from"../chunks/Tip-hf-doc-builder.js";import{I as ta}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as D}from"../chunks/CodeBlock-hf-doc-builder.js";function ho(oa){let f,H,x,w,M;return{c(){f=r("p"),H=t("Acesse a documenta\xE7\xE3o do "),x=r("code"),w=t("pipeline()"),M=t(" para obter uma lista completa de tarefas poss\xEDveis.")},l(k){f=n(k,"P",{});var b=i(f);H=o(b,"Acesse a documenta\xE7\xE3o do "),x=n(b,"CODE",{});var U=i(x);w=o(U,"pipeline()"),U.forEach(s),M=o(b," para obter uma lista completa de tarefas poss\xEDveis."),b.forEach(s)},m(k,b){p(k,f,b),a(f,H),a(f,x),a(x,w),a(f,M)},d(k){k&&s(f)}}}function go(oa){let f,H,x,w,M,k,b,U,Va,la,N,Wa,X,Xa,Ya,ra,P,Y,Za,Pe,es,as,ss,ye,ts,os,Z,ls,Ae,rs,ns,na,F,ia,O,I,ze,ee,is,Ce,ps,pa,q,ms,De,cs,ds,Me,fs,us,Oe,hs,gs,ma,xe,ae,vs,Se,$s,_s,ca,se,da,te,oe,xs,Le,Es,js,fa,le,ua,Ee,ks,ha,re,ga,E,ws,Te,qs,bs,He,Ps,ys,Ue,As,zs,Ne,Cs,Ds,va,ne,$a,S,R,Fe,ie,Ms,Ie,Os,_a,j,Ss,Re,Ls,Ts,pe,Hs,Us,Je,Ns,Fs,Ke,Is,Rs,xa,me,Ea,J,Js,Be,Ks,Bs,ja,ce,ka,K,Gs,Ge,Qs,Vs,wa,de,qa,L,B,Qe,fe,Ws,Ve,Xs,ba,y,Ys,We,Zs,et,Xe,at,st,Pa,A,tt,ue,ot,lt,Ye,rt,nt,ya,he,Aa,G,it,Ze,pt,mt,za,ge,Ca,T,Q,ea,ve,ct,aa,dt,Da,V,ft,sa,ut,ht,Ma,je,ke,gt,Oa,$e,Sa;return k=new ta({}),F=new uo({props:{$$slots:{default:[ho]},$$scope:{ctx:oa}}}),ee=new ta({}),se=new D({props:{code:`from transformers import pipeline
generator = pipeline(task="text-generation")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>generator = pipeline(task=<span class="hljs-string">"text-generation"</span>)`}}),le=new D({props:{code:'generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")',highlighted:`<span class="hljs-meta">>>> </span>generator(<span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>)
[{<span class="hljs-string">'generated_text'</span>: <span class="hljs-string">'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'</span>}]`}}),re=new D({props:{code:`generator(
[
"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
"Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
]
)`,highlighted:`<span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>,
<span class="hljs-meta">... </span> <span class="hljs-string">"Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne"</span>,
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>)`}}),ne=new D({props:{code:`generator(
"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
num_return_sequences=2,
)`,highlighted:`<span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>,
<span class="hljs-meta">... </span> num_return_sequences=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>)`}}),ie=new ta({}),me=new D({props:{code:`from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)`}}),ce=new D({props:{code:`from transformers import pipeline
generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>generator = pipeline(task=<span class="hljs-string">"text-generation"</span>, model=model, tokenizer=tokenizer)`}}),de=new D({props:{code:'generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")',highlighted:`<span class="hljs-meta">>>> </span>generator(<span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>)
[{<span class="hljs-string">'generated_text'</span>: <span class="hljs-string">'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'</span>}]`}}),fe=new ta({}),he=new D({props:{code:`from transformers import pipeline
audio_classifier = pipeline(
task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>audio_classifier = pipeline(
<span class="hljs-meta">... </span> task=<span class="hljs-string">"audio-classification"</span>, model=<span class="hljs-string">"ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"</span>
<span class="hljs-meta">... </span>)`}}),ge=new D({props:{code:'audio_classifier("jfk_moon_speech.wav")',highlighted:`<span class="hljs-meta">>>> </span>audio_classifier(<span class="hljs-string">"jfk_moon_speech.wav"</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'calm'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.13856211304664612</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'disgust'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.13148026168346405</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'happy'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.12635163962841034</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'angry'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.12439591437578201</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'fearful'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.12404385954141617</span>}]`}}),ve=new ta({}),$e=new D({props:{code:`from transformers import pipeline
vision_classifier = pipeline(task="image-classification")
vision_classifier(
images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>vision_classifier = pipeline(task=<span class="hljs-string">"image-classification"</span>)
<span class="hljs-meta">>>> </span>vision_classifier(
<span class="hljs-meta">... </span> images=<span class="hljs-string">"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"</span>
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'lynx, catamount'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.4403027892112732</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'</span>,
<span class="hljs-string">'score'</span>: <span class="hljs-number">0.03433405980467796</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'snow leopard, ounce, Panthera uncia'</span>,
<span class="hljs-string">'score'</span>: <span class="hljs-number">0.032148055732250214</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'Egyptian cat'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.02353910356760025</span>},
{<span class="hljs-string">'label'</span>: <span class="hljs-string">'tiger cat'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.023034192621707916</span>}]`}}),{c(){f=r("meta"),H=m(),x=r("h1"),w=r("a"),M=r("span"),u(k.$$.fragment),b=m(),U=r("span"),Va=t("Pipelines para infer\xEAncia"),la=m(),N=r("p"),Wa=t("Um [pipeline] simplifica o uso dos modelos no "),X=r("a"),Xa=t("Model Hub"),Ya=t(` para a infer\xEAncia de uma diversidade de tarefas,
como a gera\xE7\xE3o de texto, a segmenta\xE7\xE3o de imagens e a classifica\xE7\xE3o de \xE1udio.
Inclusive, se n\xE3o tem experi\xEAncia com alguma modalidade espec\xEDfica ou n\xE3o compreende o c\xF3digo que forma os modelos,
pode usar eles mesmo assim com o [pipeline]! Este tutorial te ensinar\xE1 a:`),ra=m(),P=r("ul"),Y=r("li"),Za=t("Utilizar um "),Pe=r("code"),es=t("pipeline()"),as=t(" para infer\xEAncia."),ss=m(),ye=r("li"),ts=t("Utilizar um tokenizador ou model espec\xEDfico."),os=m(),Z=r("li"),ls=t("Utilizar um "),Ae=r("code"),rs=t("pipeline()"),ns=t(" para tarefas de \xE1udio e vis\xE3o computacional."),na=m(),u(F.$$.fragment),ia=m(),O=r("h2"),I=r("a"),ze=r("span"),u(ee.$$.fragment),is=m(),Ce=r("span"),ps=t("Uso do pipeline"),pa=m(),q=r("p"),ms=t("Mesmo que cada tarefa tenha um "),De=r("code"),cs=t("pipeline()"),ds=t(" associado, \xE9 mais simples usar a abstra\xE7\xE3o geral do "),Me=r("code"),fs=t("pipeline()"),us=t(` que
cont\xE9m todos os pipelines das tarefas mais espec\xEDficas.
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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/create_a_model.mdx-hf-doc-builder.js | import{S as wn,i as zn,s as xn,e as r,k as c,w as j,t as o,M as Dn,c as i,d as s,m as f,a as n,x as k,h as t,b,G as a,g as p,y as w,q as z,o as x,B as D,v as Cn,L as Qo}from"../chunks/vendor-hf-doc-builder.js";import{T as Lo}from"../chunks/Tip-hf-doc-builder.js";import{I as da}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as I}from"../chunks/CodeBlock-hf-doc-builder.js";import{F as kn,M as Uo}from"../chunks/Markdown-hf-doc-builder.js";function yn(S){let m,g,u,_,q;return{c(){m=r("p"),g=o("Voc\xEA pode tamb\xE9m salvar seu arquivo de configura\xE7\xF5es como um dicion\xE1rio ou at\xE9 mesmo com a diferen\xE7a entre as seus atributos de configura\xE7\xE3o customizados e os atributos de configura\xE7\xE3o padr\xF5es! Olhe a documenta\xE7\xE3o "),u=r("a"),_=o("configuration"),q=o(" para mais detalhes."),this.h()},l(v){m=i(v,"P",{});var $=n(m);g=t($,"Voc\xEA pode tamb\xE9m salvar seu arquivo de configura\xE7\xF5es como um dicion\xE1rio ou at\xE9 mesmo com a diferen\xE7a entre as seus atributos de configura\xE7\xE3o customizados e os atributos de configura\xE7\xE3o padr\xF5es! Olhe a documenta\xE7\xE3o "),u=i($,"A",{href:!0});var y=n(u);_=t(y,"configuration"),y.forEach(s),q=t($," para mais detalhes."),$.forEach(s),this.h()},h(){b(u,"href","main_classes/configuration")},m(v,$){p(v,m,$),a(m,g),a(m,u),a(u,_),a(m,q)},d(v){v&&s(m)}}}function Tn(S){let m,g,u,_,q,v,$,y,E,N,C,M,B,O,T,V,h,F,R,A,L;return _=new I({props:{code:`from transformers import DistilBertModel
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model = DistilBertModel(my_config)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertModel
<span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"./your_model_save_path/my_config.json"</span>)
<span class="hljs-meta">>>> </span>model = DistilBertModel(my_config)`}}),T=new I({props:{code:'model = DistilBertModel.from_pretrained("distilbert-base-uncased")',highlighted:'<span class="hljs-meta">>>> </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)'}}),A=new I({props:{code:'model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)',highlighted:'<span class="hljs-meta">>>> </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, config=my_config)'}}),{c(){m=r("p"),g=o("Carregar seus atributos de configura\xE7\xE3o customizados em um modelo:"),u=c(),j(_.$$.fragment),q=c(),v=r("p"),$=o("Isso cria um modelo com valores aleat\xF3rios ao inv\xE9s de pr\xE9-treinar os pesos. Voc\xEA n\xE3o ir\xE1 conseguir usar usar esse modelo para nada \xFAtil ainda, at\xE9 voc\xEA treinar ele. Treino \xE9 um processo caro e demorado. Geralmente \xE9 melhor utilizar um modelo pr\xE9-treinado para obter melhores resultados mais r\xE1pido, enquanto usa apenas uma fra\xE7\xE3o dos recursos necess\xE1rios para treinar."),y=c(),E=r("p"),N=o("Criar um modelo pr\xE9-treinado com "),C=r("code"),M=o("from_pretrained()"),B=o(":"),O=c(),j(T.$$.fragment),V=c(),h=r("p"),F=o("Quando voc\xEA carregar os pesos pr\xE9-treinados, a configura\xE7\xE3o padr\xE3o do modelo \xE9 automaticamente carregada se o modelo \xE9 provido pelo \u{1F917} Transformers. No entanto, voc\xEA ainda consegue mudar - alguns ou todos - os atributos padr\xF5es de configura\xE7\xE3o do modelo com os seus pr\xF3prio atributos, se voc\xEA preferir:"),R=c(),j(A.$$.fragment)},l(d){m=i(d,"P",{});var P=n(m);g=t(P,"Carregar seus atributos de configura\xE7\xE3o customizados em um modelo:"),P.forEach(s),u=f(d),k(_.$$.fragment,d),q=f(d),v=i(d,"P",{});var W=n(v);$=t(W,"Isso cria um modelo com valores aleat\xF3rios ao inv\xE9s de pr\xE9-treinar os pesos. Voc\xEA n\xE3o ir\xE1 conseguir usar usar esse modelo para nada \xFAtil ainda, at\xE9 voc\xEA treinar ele. Treino \xE9 um processo caro e demorado. Geralmente \xE9 melhor utilizar um modelo pr\xE9-treinado para obter melhores resultados mais r\xE1pido, enquanto usa apenas uma fra\xE7\xE3o dos recursos necess\xE1rios para treinar."),W.forEach(s),y=f(d),E=i(d,"P",{});var H=n(E);N=t(H,"Criar um modelo pr\xE9-treinado com "),C=i(H,"CODE",{});var se=n(C);M=t(se,"from_pretrained()"),se.forEach(s),B=t(H,":"),H.forEach(s),O=f(d),k(T.$$.fragment,d),V=f(d),h=i(d,"P",{});var oe=n(h);F=t(oe,"Quando voc\xEA carregar os pesos pr\xE9-treinados, a configura\xE7\xE3o padr\xE3o do modelo \xE9 automaticamente carregada se o modelo \xE9 provido pelo \u{1F917} Transformers. No entanto, voc\xEA ainda consegue mudar - alguns ou todos - os atributos padr\xF5es de configura\xE7\xE3o do modelo com os seus pr\xF3prio atributos, se voc\xEA preferir:"),oe.forEach(s),R=f(d),k(A.$$.fragment,d)},m(d,P){p(d,m,P),a(m,g),p(d,u,P),w(_,d,P),p(d,q,P),p(d,v,P),a(v,$),p(d,y,P),p(d,E,P),a(E,N),a(E,C),a(C,M),a(E,B),p(d,O,P),w(T,d,P),p(d,V,P),p(d,h,P),a(h,F),p(d,R,P),w(A,d,P),L=!0},p:Qo,i(d){L||(z(_.$$.fragment,d),z(T.$$.fragment,d),z(A.$$.fragment,d),L=!0)},o(d){x(_.$$.fragment,d),x(T.$$.fragment,d),x(A.$$.fragment,d),L=!1},d(d){d&&s(m),d&&s(u),D(_,d),d&&s(q),d&&s(v),d&&s(y),d&&s(E),d&&s(O),D(T,d),d&&s(V),d&&s(h),d&&s(R),D(A,d)}}}function Pn(S){let m,g;return m=new Uo({props:{$$slots:{default:[Tn]},$$scope:{ctx:S}}}),{c(){j(m.$$.fragment)},l(u){k(m.$$.fragment,u)},m(u,_){w(m,u,_),g=!0},p(u,_){const q={};_&2&&(q.$$scope={dirty:_,ctx:u}),m.$set(q)},i(u){g||(z(m.$$.fragment,u),g=!0)},o(u){x(m.$$.fragment,u),g=!1},d(u){D(m,u)}}}function Fn(S){let m,g,u,_,q,v,$,y,E,N,C,M,B,O,T,V,h,F,R,A,L;return _=new I({props:{code:`from transformers import TFDistilBertModel
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tf_model = TFDistilBertModel(my_config)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertModel
<span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"./your_model_save_path/my_config.json"</span>)
<span class="hljs-meta">>>> </span>tf_model = TFDistilBertModel(my_config)`}}),T=new I({props:{code:'tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")',highlighted:'<span class="hljs-meta">>>> </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)'}}),A=new I({props:{code:'tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)',highlighted:'<span class="hljs-meta">>>> </span>tf_model = TFDistilBertModel.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, config=my_config)'}}),{c(){m=r("p"),g=o("Carregar os seus pr\xF3prios atributos padr\xF5es de contigura\xE7\xE3o no modelo:"),u=c(),j(_.$$.fragment),q=c(),v=r("p"),$=o("Isso cria um modelo com valores aleat\xF3rios ao inv\xE9s de pr\xE9-treinar os pesos. Voc\xEA n\xE3o ir\xE1 conseguir usar usar esse modelo para nada \xFAtil ainda, at\xE9 voc\xEA treinar ele. Treino \xE9 um processo caro e demorado. Geralmente \xE9 melhor utilizar um modelo pr\xE9-treinado para obter melhores resultados mais r\xE1pido, enquanto usa apenas uma fra\xE7\xE3o dos recursos necess\xE1rios para treinar."),y=c(),E=r("p"),N=o("Criar um modelo pr\xE9-treinado com "),C=r("code"),M=o("from_pretrained()"),B=o(":"),O=c(),j(T.$$.fragment),V=c(),h=r("p"),F=o("Quando voc\xEA carregar os pesos pr\xE9-treinados, a configura\xE7\xE3o padr\xE3o do modelo \xE9 automaticamente carregada se o modelo \xE9 provido pelo \u{1F917} Transformers. No entanto, voc\xEA ainda consegue mudar - alguns ou todos - os atributos padr\xF5es de configura\xE7\xE3o do modelo com os seus pr\xF3prio atributos, se voc\xEA preferir:"),R=c(),j(A.$$.fragment)},l(d){m=i(d,"P",{});var P=n(m);g=t(P,"Carregar os seus pr\xF3prios atributos padr\xF5es de contigura\xE7\xE3o no modelo:"),P.forEach(s),u=f(d),k(_.$$.fragment,d),q=f(d),v=i(d,"P",{});var W=n(v);$=t(W,"Isso cria um modelo com valores aleat\xF3rios ao inv\xE9s de pr\xE9-treinar os pesos. Voc\xEA n\xE3o ir\xE1 conseguir usar usar esse modelo para nada \xFAtil ainda, at\xE9 voc\xEA treinar ele. Treino \xE9 um processo caro e demorado. Geralmente \xE9 melhor utilizar um modelo pr\xE9-treinado para obter melhores resultados mais r\xE1pido, enquanto usa apenas uma fra\xE7\xE3o dos recursos necess\xE1rios para treinar."),W.forEach(s),y=f(d),E=i(d,"P",{});var H=n(E);N=t(H,"Criar um modelo pr\xE9-treinado com "),C=i(H,"CODE",{});var se=n(C);M=t(se,"from_pretrained()"),se.forEach(s),B=t(H,":"),H.forEach(s),O=f(d),k(T.$$.fragment,d),V=f(d),h=i(d,"P",{});var oe=n(h);F=t(oe,"Quando voc\xEA carregar os pesos pr\xE9-treinados, a configura\xE7\xE3o padr\xE3o do modelo \xE9 automaticamente carregada se o modelo \xE9 provido pelo \u{1F917} Transformers. No entanto, voc\xEA ainda consegue mudar - alguns ou todos - os atributos padr\xF5es de configura\xE7\xE3o do modelo com os seus pr\xF3prio atributos, se voc\xEA preferir:"),oe.forEach(s),R=f(d),k(A.$$.fragment,d)},m(d,P){p(d,m,P),a(m,g),p(d,u,P),w(_,d,P),p(d,q,P),p(d,v,P),a(v,$),p(d,y,P),p(d,E,P),a(E,N),a(E,C),a(C,M),a(E,B),p(d,O,P),w(T,d,P),p(d,V,P),p(d,h,P),a(h,F),p(d,R,P),w(A,d,P),L=!0},p:Qo,i(d){L||(z(_.$$.fragment,d),z(T.$$.fragment,d),z(A.$$.fragment,d),L=!0)},o(d){x(_.$$.fragment,d),x(T.$$.fragment,d),x(A.$$.fragment,d),L=!1},d(d){d&&s(m),d&&s(u),D(_,d),d&&s(q),d&&s(v),d&&s(y),d&&s(E),d&&s(O),D(T,d),d&&s(V),d&&s(h),d&&s(R),D(A,d)}}}function Bn(S){let m,g;return m=new Uo({props:{$$slots:{default:[Fn]},$$scope:{ctx:S}}}),{c(){j(m.$$.fragment)},l(u){k(m.$$.fragment,u)},m(u,_){w(m,u,_),g=!0},p(u,_){const q={};_&2&&(q.$$scope={dirty:_,ctx:u}),m.$set(q)},i(u){g||(z(m.$$.fragment,u),g=!0)},o(u){x(m.$$.fragment,u),g=!1},d(u){D(m,u)}}}function An(S){let m,g,u,_,q,v,$,y,E,N,C,M,B,O,T,V;return $=new I({props:{code:`from transformers import DistilBertForSequenceClassification
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<span class="hljs-meta">>>> </span>model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),T=new I({props:{code:`from transformers import DistilBertForQuestionAnswering
model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertForQuestionAnswering
<span class="hljs-meta">>>> </span>model = DistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),{c(){m=r("p"),g=o("Por exemplo, "),u=r("code"),_=o("DistilBertForSequenceClassification"),q=o(" \xE9 um modelo DistilBERT base com uma head de classifica\xE7\xE3o de sequ\xEAncia. A head de calssifica\xE7\xE3o de sequ\xEAncia \xE9 uma camada linear no topo das sa\xEDdas agrupadas."),v=c(),j($.$$.fragment),y=c(),E=r("p"),N=o("Reutilize facilmente esse ponto de parada para outra tarefe mudando para uma head de modelo diferente. Para uma tarefe de responder quest\xF5es, voc\xEA usaria a head do modelo "),C=r("code"),M=o("DistilBertForQuestionAnswering"),B=o(". A head de responder quest\xF5es \xE9 similar com a de classifica\xE7\xE3o de sequ\xEAncias exceto o fato de que ela \xE9 uma camada no topo dos estados das sa\xEDdas ocultas."),O=c(),j(T.$$.fragment)},l(h){m=i(h,"P",{});var F=n(m);g=t(F,"Por exemplo, "),u=i(F,"CODE",{});var R=n(u);_=t(R,"DistilBertForSequenceClassification"),R.forEach(s),q=t(F," \xE9 um modelo DistilBERT base com uma head de classifica\xE7\xE3o de sequ\xEAncia. A head de calssifica\xE7\xE3o de sequ\xEAncia \xE9 uma camada linear no topo das sa\xEDdas agrupadas."),F.forEach(s),v=f(h),k($.$$.fragment,h),y=f(h),E=i(h,"P",{});var A=n(E);N=t(A,"Reutilize facilmente esse ponto de parada para outra tarefe mudando para uma head de modelo diferente. Para uma tarefe de responder quest\xF5es, voc\xEA usaria a head do modelo "),C=i(A,"CODE",{});var L=n(C);M=t(L,"DistilBertForQuestionAnswering"),L.forEach(s),B=t(A,". A head de responder quest\xF5es \xE9 similar com a de classifica\xE7\xE3o de sequ\xEAncias exceto o fato de que ela \xE9 uma camada no topo dos estados das sa\xEDdas ocultas."),A.forEach(s),O=f(h),k(T.$$.fragment,h)},m(h,F){p(h,m,F),a(m,g),a(m,u),a(u,_),a(m,q),p(h,v,F),w($,h,F),p(h,y,F),p(h,E,F),a(E,N),a(E,C),a(C,M),a(E,B),p(h,O,F),w(T,h,F),V=!0},p:Qo,i(h){V||(z($.$$.fragment,h),z(T.$$.fragment,h),V=!0)},o(h){x($.$$.fragment,h),x(T.$$.fragment,h),V=!1},d(h){h&&s(m),h&&s(v),D($,h),h&&s(y),h&&s(E),h&&s(O),D(T,h)}}}function On(S){let m,g;return m=new Uo({props:{$$slots:{default:[An]},$$scope:{ctx:S}}}),{c(){j(m.$$.fragment)},l(u){k(m.$$.fragment,u)},m(u,_){w(m,u,_),g=!0},p(u,_){const q={};_&2&&(q.$$scope={dirty:_,ctx:u}),m.$set(q)},i(u){g||(z(m.$$.fragment,u),g=!0)},o(u){x(m.$$.fragment,u),g=!1},d(u){D(m,u)}}}function Vn(S){let m,g,u,_,q,v,$,y,E,N,C,M,B,O,T,V;return $=new I({props:{code:`from transformers import TFDistilBertForSequenceClassification
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<span class="hljs-meta">>>> </span>tf_model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),T=new I({props:{code:`from transformers import TFDistilBertForQuestionAnswering
tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFDistilBertForQuestionAnswering
<span class="hljs-meta">>>> </span>tf_model = TFDistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),{c(){m=r("p"),g=o("Por exemplo, "),u=r("code"),_=o("TFDistilBertForSequenceClassification"),q=o(" \xE9 um modelo DistilBERT base com uma head de classifica\xE7\xE3o de sequ\xEAncia. A head de calssifica\xE7\xE3o de sequ\xEAncia \xE9 uma camada linear no topo das sa\xEDdas agrupadas."),v=c(),j($.$$.fragment),y=c(),E=r("p"),N=o("Reutilize facilmente esse ponto de parada para outra tarefe mudando para uma head de modelo diferente. Para uma tarefe de responder quest\xF5es, voc\xEA usaria a head do modelo "),C=r("code"),M=o("TFDistilBertForQuestionAnswering"),B=o(". 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config = DistilBertConfig()
print(config)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertConfig
<span class="hljs-meta">>>> </span>config = DistilBertConfig()
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(config)
DistilBertConfig {
<span class="hljs-string">"activation"</span>: <span class="hljs-string">"gelu"</span>,
<span class="hljs-string">"attention_dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"dim"</span>: <span class="hljs-number">768</span>,
<span class="hljs-string">"dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"hidden_dim"</span>: <span class="hljs-number">3072</span>,
<span class="hljs-string">"initializer_range"</span>: <span class="hljs-number">0.02</span>,
<span class="hljs-string">"max_position_embeddings"</span>: <span class="hljs-number">512</span>,
<span class="hljs-string">"model_type"</span>: <span class="hljs-string">"distilbert"</span>,
<span class="hljs-string">"n_heads"</span>: <span class="hljs-number">12</span>,
<span class="hljs-string">"n_layers"</span>: <span class="hljs-number">6</span>,
<span class="hljs-string">"pad_token_id"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"qa_dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"seq_classif_dropout"</span>: <span class="hljs-number">0.2</span>,
<span class="hljs-string">"sinusoidal_pos_embds"</span>: false,
<span class="hljs-string">"transformers_version"</span>: <span class="hljs-string">"4.16.2"</span>,
<span class="hljs-string">"vocab_size"</span>: <span class="hljs-number">30522</span>
}`}}),We=new I({props:{code:`my_config = DistilBertConfig(activation="relu", attention_dropout=0.4)
print(my_config)`,highlighted:`<span class="hljs-meta">>>> </span>my_config = DistilBertConfig(activation=<span class="hljs-string">"relu"</span>, attention_dropout=<span class="hljs-number">0.4</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(my_config)
DistilBertConfig {
<span class="hljs-string">"activation"</span>: <span class="hljs-string">"relu"</span>,
<span class="hljs-string">"attention_dropout"</span>: <span class="hljs-number">0.4</span>,
<span class="hljs-string">"dim"</span>: <span class="hljs-number">768</span>,
<span class="hljs-string">"dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"hidden_dim"</span>: <span class="hljs-number">3072</span>,
<span class="hljs-string">"initializer_range"</span>: <span class="hljs-number">0.02</span>,
<span class="hljs-string">"max_position_embeddings"</span>: <span class="hljs-number">512</span>,
<span class="hljs-string">"model_type"</span>: <span class="hljs-string">"distilbert"</span>,
<span class="hljs-string">"n_heads"</span>: <span class="hljs-number">12</span>,
<span class="hljs-string">"n_layers"</span>: <span class="hljs-number">6</span>,
<span class="hljs-string">"pad_token_id"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"qa_dropout"</span>: <span class="hljs-number">0.1</span>,
<span class="hljs-string">"seq_classif_dropout"</span>: <span class="hljs-number">0.2</span>,
<span class="hljs-string">"sinusoidal_pos_embds"</span>: false,
<span class="hljs-string">"transformers_version"</span>: <span class="hljs-string">"4.16.2"</span>,
<span class="hljs-string">"vocab_size"</span>: <span class="hljs-number">30522</span>
}`}}),Re=new I({props:{code:'my_config = DistilBertConfig.from_pretrained("distilbert-base-uncased", activation="relu", attention_dropout=0.4)',highlighted:'<span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, activation=<span class="hljs-string">"relu"</span>, attention_dropout=<span class="hljs-number">0.4</span>)'}}),Le=new I({props:{code:'my_config.save_pretrained(save_directory="./your_model_save_path")',highlighted:'<span class="hljs-meta">>>> </span>my_config.save_pretrained(save_directory=<span class="hljs-string">"./your_model_save_path"</span>)'}}),Qe=new I({props:{code:'my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")',highlighted:'<span class="hljs-meta">>>> </span>my_config = DistilBertConfig.from_pretrained(<span class="hljs-string">"./your_model_save_path/my_config.json"</span>)'}}),_e=new Lo({props:{$$slots:{default:[yn]},$$scope:{ctx:S}}}),Ue=new da({}),ve=new kn({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Bn],pytorch:[Pn]},$$scope:{ctx:S}}}),Xe=new da({}),be=new kn({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Mn],pytorch:[On]},$$scope:{ctx:S}}}),Ke=new da({}),we=new Lo({props:{warning:!0,$$slots:{default:[Sn]},$$scope:{ctx:S}}}),Ze=new I({props:{code:`from transformers import DistilBertTokenizer
my_tokenizer = DistilBertTokenizer(vocab_file="my_vocab_file.txt", do_lower_case=False, padding_side="left")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer
<span class="hljs-meta">>>> </span>my_tokenizer = DistilBertTokenizer(vocab_file=<span class="hljs-string">"my_vocab_file.txt"</span>, do_lower_case=<span class="hljs-literal">False</span>, padding_side=<span class="hljs-string">"left"</span>)`}}),ea=new I({props:{code:`from transformers import DistilBertTokenizer
slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizer
<span class="hljs-meta">>>> </span>slow_tokenizer = DistilBertTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),aa=new I({props:{code:`from transformers import DistilBertTokenizerFast
fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertTokenizerFast
<span class="hljs-meta">>>> </span>fast_tokenizer = DistilBertTokenizerFast.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),Ce=new Lo({props:{$$slots:{default:[Nn]},$$scope:{ctx:S}}}),sa=new da({}),oa=new I({props:{code:`from transformers import ViTFeatureExtractor
vit_extractor = ViTFeatureExtractor()
print(vit_extractor)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTFeatureExtractor
<span class="hljs-meta">>>> </span>vit_extractor = ViTFeatureExtractor()
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(vit_extractor)
ViTFeatureExtractor {
<span class="hljs-string">"do_normalize"</span>: true,
<span class="hljs-string">"do_resize"</span>: true,
<span class="hljs-string">"feature_extractor_type"</span>: <span class="hljs-string">"ViTFeatureExtractor"</span>,
<span class="hljs-string">"image_mean"</span>: [
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>
],
<span class="hljs-string">"image_std"</span>: [
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>
],
<span class="hljs-string">"resample"</span>: <span class="hljs-number">2</span>,
<span class="hljs-string">"size"</span>: <span class="hljs-number">224</span>
}`}}),Te=new Lo({props:{$$slots:{default:[In]},$$scope:{ctx:S}}}),ta=new I({props:{code:`from transformers import ViTFeatureExtractor
my_vit_extractor = ViTFeatureExtractor(resample="PIL.Image.BOX", do_normalize=False, image_mean=[0.3, 0.3, 0.3])
print(my_vit_extractor)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViTFeatureExtractor
<span class="hljs-meta">>>> </span>my_vit_extractor = ViTFeatureExtractor(resample=<span class="hljs-string">"PIL.Image.BOX"</span>, do_normalize=<span class="hljs-literal">False</span>, image_mean=[<span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>, <span class="hljs-number">0.3</span>])
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(my_vit_extractor)
ViTFeatureExtractor {
<span class="hljs-string">"do_normalize"</span>: false,
<span class="hljs-string">"do_resize"</span>: true,
<span class="hljs-string">"feature_extractor_type"</span>: <span class="hljs-string">"ViTFeatureExtractor"</span>,
<span class="hljs-string">"image_mean"</span>: [
<span class="hljs-number">0.3</span>,
<span class="hljs-number">0.3</span>,
<span class="hljs-number">0.3</span>
],
<span class="hljs-string">"image_std"</span>: [
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>,
<span class="hljs-number">0.5</span>
],
<span class="hljs-string">"resample"</span>: <span class="hljs-string">"PIL.Image.BOX"</span>,
<span class="hljs-string">"size"</span>: <span class="hljs-number">224</span>
}`}}),ra=new I({props:{code:`from transformers import Wav2Vec2FeatureExtractor
w2v2_extractor = Wav2Vec2FeatureExtractor()
print(w2v2_extractor)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor
<span class="hljs-meta">>>> </span>w2v2_extractor = Wav2Vec2FeatureExtractor()
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(w2v2_extractor)
Wav2Vec2FeatureExtractor {
<span class="hljs-string">"do_normalize"</span>: true,
<span class="hljs-string">"feature_extractor_type"</span>: <span class="hljs-string">"Wav2Vec2FeatureExtractor"</span>,
<span class="hljs-string">"feature_size"</span>: <span class="hljs-number">1</span>,
<span class="hljs-string">"padding_side"</span>: <span class="hljs-string">"right"</span>,
<span class="hljs-string">"padding_value"</span>: <span class="hljs-number">0.0</span>,
<span class="hljs-string">"return_attention_mask"</span>: false,
<span class="hljs-string">"sampling_rate"</span>: <span class="hljs-number">16000</span>
}`}}),ia=new da({}),na=new I({props:{code:`from transformers import Wav2Vec2FeatureExtractor
feature_extractor = Wav2Vec2FeatureExtractor(padding_value=1.0, do_normalize=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2FeatureExtractor
<span class="hljs-meta">>>> </span>feature_extractor = Wav2Vec2FeatureExtractor(padding_value=<span class="hljs-number">1.0</span>, do_normalize=<span class="hljs-literal">True</span>)`}}),la=new I({props:{code:`from transformers import Wav2Vec2CTCTokenizer
tokenizer = Wav2Vec2CTCTokenizer(vocab_file="my_vocab_file.txt")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2CTCTokenizer
<span class="hljs-meta">>>> </span>tokenizer = Wav2Vec2CTCTokenizer(vocab_file=<span class="hljs-string">"my_vocab_file.txt"</span>)`}}),pa=new I({props:{code:`from transformers import Wav2Vec2Processor
processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Wav2Vec2Processor
<span class="hljs-meta">>>> </span>processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)`}}),{c(){m=r("meta"),g=c(),u=r("h1"),_=r("a"),q=r("span"),j(v.$$.fragment),$=c(),y=r("span"),E=o("Criar uma arquitetura customizada"),N=c(),C=r("p"),M=o("Uma "),B=r("a"),O=r("code"),T=o("AutoClass"),V=o(" automaticamente infere a arquitetura do modelo e baixa configura\xE7\xF5es e pesos pr\xE9-treinados. Geralmente, n\xF3s recomendamos usar uma "),h=r("code"),F=o("AutoClass"),R=o(" para produzir um c\xF3digo independente de checkpoints. Mas usu\xE1rios que querem mais contole sobre par\xE2metros espec\xEDficos do modelo pode criar um modelo customizado \u{1F917} Transformers a partir de algumas classes bases. Isso pode ser particulamente \xFAtil para algu\xE9m que est\xE1 interessado em estudar, treinar ou fazer experimentos com um modelo \u{1F917} Transformers. Nesse tutorial, ser\xE1 explicado como criar um modelo customizado sem uma "),A=r("code"),L=o("AutoClass"),d=o(". Aprenda como:"),P=c(),W=r("ul"),H=r("li"),se=o("Carregar e customizar a configura\xE7\xE3o de um modelo."),oe=c(),wa=r("li"),Ho=o("Criar a arquitetura de um modelo."),Go=c(),za=r("li"),Jo=o("Criar um tokenizer r\xE1pido e devagar para textos."),Xo=c(),xa=r("li"),Ko=o("Criar extrator de features para tarefas envolvendo audio e imagem."),Yo=c(),Da=r("li"),Zo=o("Criar um processador para tarefas multimodais."),ks=c(),te=r("h2"),ue=r("a"),Ca=r("span"),j(Me.$$.fragment),et=c(),ya=r("span"),at=o("configuration"),ws=c(),U=r("p"),st=o("A "),ca=r("a"),ot=o("configuration"),tt=o(" refere-se a atributos espec\xEDficos de um modelo. Cada configura\xE7\xE3o de modelo tem atributos diferentes; por exemplo, todos modelo de PLN possuem os atributos "),Ta=r("code"),rt=o("hidden_size"),it=o(", "),Pa=r("code"),nt=o("num_attention_heads"),lt=o(", "),Fa=r("code"),pt=o("num_hidden_layers"),mt=o(" e "),Ba=r("code"),ut=o("vocab_size"),dt=o(" em comum. Esse atributos especificam o numero de \u2018attention heads\u2019 ou \u2018hidden layers\u2019 para construir um modelo."),zs=c(),Y=r("p"),ct=o("D\xEA uma olhada a mais em "),fa=r("a"),ft=o("DistilBERT"),ht=o(" acessando "),Aa=r("code"),_t=o("DistilBertConfig"),gt=o(" para observar esses atributos:"),xs=c(),j(Se.$$.fragment),Ds=c(),re=r("p"),Oa=r("code"),vt=o("DistilBertConfig"),$t=o(" mostra todos os atributos padr\xF5es usados para construir um "),Va=r("code"),qt=o("DistilBertModel"),bt=o(" base. Todos atributos s\xE3o customiz\xE1veis, o que cria espa\xE7o para experimentos. Por exemplo, voc\xEA pode customizar um modelo padr\xE3o para:"),Cs=c(),de=r("ul"),Ne=r("li"),Et=o("Tentar uma fun\xE7\xE3o de ativa\xE7\xE3o diferente com o par\xE2metro "),Ma=r("code"),jt=o("activation"),kt=o("."),wt=c(),Ie=r("li"),zt=o("Usar uma taxa de desist\xEAncia maior para as probabilidades de \u2018attention\u2019 com o par\xE2metro "),Sa=r("code"),xt=o("attention_dropout"),Dt=o("."),ys=c(),j(We.$$.fragment),Ts=c(),ce=r("p"),Ct=o("Atributos de um modelo pr\xE9-treinado podem ser modificados na fun\xE7\xE3o "),Na=r("code"),yt=o("from_pretrained()"),Tt=o(":"),Ps=c(),j(Re.$$.fragment),Fs=c(),fe=r("p"),Pt=o("Uma vez que voc\xEA est\xE1 satisfeito com as configura\xE7\xF5es do seu modelo, voc\xEA consegue salvar elas com "),Ia=r("code"),Ft=o("save_pretrained()"),Bt=o(". Seu arquivo de configura\xE7\xF5es est\xE1 salvo como um arquivo JSON no diret\xF3rio especificado:"),Bs=c(),j(Le.$$.fragment),As=c(),he=r("p"),At=o("Para reusar o arquivo de configura\xE7\xF5es, carregue com "),Wa=r("code"),Ot=o("from_pretrained()"),Vt=o(":"),Os=c(),j(Qe.$$.fragment),Vs=c(),j(_e.$$.fragment),Ms=c(),ie=r("h2"),ge=r("a"),Ra=r("span"),j(Ue.$$.fragment),Mt=c(),La=r("span"),St=o("Modelo"),Ss=c(),Q=r("p"),Nt=o("O pr\xF3ximo passo \xE9 criar um "),ha=r("a"),It=o("model"),Wt=o(". O modelo - tamb\xE9m vagamente referido como arquitetura - define o que cada camada est\xE1 fazendo e quais opera\xE7\xF5es est\xE3o acontecendo. Atributos como "),Qa=r("code"),Rt=o("num_hidden_layers"),Lt=o(" das configura\xE7\xF5es s\xE3o utilizados para definir a arquitetura. Todo modelo compartilha a classe base "),Ua=r("code"),Qt=o("PreTrainedModel"),Ut=o(" e alguns m\xE9todos em comum como redimensionar o tamanho dos embeddings de entrada e podar as \u2018self-attention heads\u2019. 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Qualquer uma dessas classes base s\xE3o configur\xE1veis, te permitindo usar os atributos espec\xEDficos que voc\xEA queira. Voc\xEA pode facilmente preparar um modelo para treinamento ou modificar um modelo pr\xE9-treinado com poucas mudan\xE7as."),this.h()},l(e){const l=Dn('[data-svelte="svelte-1phssyn"]',document.head);m=i(l,"META",{name:!0,content:!0}),l.forEach(s),g=f(e),u=i(e,"H1",{class:!0});var ma=n(u);_=i(ma,"A",{id:!0,class:!0,href:!0});var $s=n(_);q=i($s,"SPAN",{});var qs=n(q);k(v.$$.fragment,qs),qs.forEach(s),$s.forEach(s),$=f(ma),y=i(ma,"SPAN",{});var bs=n(y);E=t(bs,"Criar uma arquitetura customizada"),bs.forEach(s),ma.forEach(s),N=f(e),C=i(e,"P",{});var K=n(C);M=t(K,"Uma "),B=i(K,"A",{href:!0});var Es=n(B);O=i(Es,"CODE",{});var ii=n(O);T=t(ii,"AutoClass"),ii.forEach(s),Es.forEach(s),V=t(K," automaticamente infere a arquitetura do modelo e baixa configura\xE7\xF5es e pesos pr\xE9-treinados. 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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/quicktour.mdx-hf-doc-builder.js | import{S as yp,i as Cp,s as zp,e as n,k as d,w as E,t as r,M as Pp,c as i,d as t,m as $,a as p,x as k,h as l,b,G as a,g as c,y as w,q as j,o as A,B as q,v as Op,L as ye}from"../chunks/vendor-hf-doc-builder.js";import{T as vt}from"../chunks/Tip-hf-doc-builder.js";import{Y as xp}from"../chunks/Youtube-hf-doc-builder.js";import{I as fa}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as L}from"../chunks/CodeBlock-hf-doc-builder.js";import{D as Mp}from"../chunks/DocNotebookDropdown-hf-doc-builder.js";import{F as gt,M as _e}from"../chunks/Markdown-hf-doc-builder.js";function Dp(P){let s,u;return{c(){s=n("p"),u=r("Todos os exemplos de c\xF3digo apresentados na documenta\xE7\xE3o t\xEAm um bot\xE3o no canto superior direito para escolher se voc\xEA deseja ocultar ou mostrar o c\xF3digo no Pytorch ou no TensorFlow. Caso contr\xE1rio, \xE9 esperado que funcione para ambos back-ends sem nenhuma altera\xE7\xE3o.")},l(o){s=i(o,"P",{});var f=p(s);u=l(f,"Todos os exemplos de c\xF3digo apresentados na documenta\xE7\xE3o t\xEAm um bot\xE3o no canto superior direito para escolher se voc\xEA deseja ocultar ou mostrar o c\xF3digo no Pytorch ou no TensorFlow. Caso contr\xE1rio, \xE9 esperado que funcione para ambos back-ends sem nenhuma altera\xE7\xE3o."),f.forEach(t)},m(o,f){c(o,s,f),a(s,u)},d(o){o&&t(s)}}}function Sp(P){let s,u,o,f,h,g,C,M;return{c(){s=n("p"),u=r("Para mais detalhes sobre a "),o=n("code"),f=r("pipeline()"),h=r(" e tarefas associadas, siga a documenta\xE7\xE3o "),g=n("a"),C=r("aqui"),M=r("."),this.h()},l(x){s=i(x,"P",{});var O=p(s);u=l(O,"Para mais detalhes sobre a "),o=i(O,"CODE",{});var S=p(o);f=l(S,"pipeline()"),S.forEach(t),h=l(O," e tarefas associadas, siga a documenta\xE7\xE3o "),g=i(O,"A",{href:!0});var I=p(g);C=l(I,"aqui"),I.forEach(t),M=l(O,"."),O.forEach(t),this.h()},h(){b(g,"href","./main_classes/pipelines")},m(x,O){c(x,s,O),a(s,u),a(s,o),a(o,f),a(s,h),a(s,g),a(g,C),a(s,M)},d(x){x&&t(s)}}}function Ip(P){let s,u;return s=new L({props:{code:"pip install torch",highlighted:"pip install torch"}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p:ye,i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Fp(P){let s,u;return s=new _e({props:{$$slots:{default:[Ip]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Np(P){let s,u;return s=new L({props:{code:"pip install tensorflow",highlighted:"pip install tensorflow"}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p:ye,i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Up(P){let s,u;return s=new _e({props:{$$slots:{default:[Np]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Hp(P){let s,u,o,f,h,g,C,M,x,O,S,I,F,U;return F=new L({props:{code:`from transformers import AutoTokenizer, AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),{c(){s=n("p"),u=r("Use o "),o=n("code"),f=r("AutoModelForSequenceClassification"),h=r(" e "),g=n("code"),C=r("AutoTokenizer"),M=r(" para carregar o modelo pr\xE9-treinado e seu tokenizer associado (mais em "),x=n("code"),O=r("AutoClass"),S=r(" abaixo):"),I=d(),E(F.$$.fragment)},l(z){s=i(z,"P",{});var _=p(s);u=l(_,"Use o "),o=i(_,"CODE",{});var y=p(o);f=l(y,"AutoModelForSequenceClassification"),y.forEach(t),h=l(_," e "),g=i(_,"CODE",{});var D=p(g);C=l(D,"AutoTokenizer"),D.forEach(t),M=l(_," para carregar o modelo pr\xE9-treinado e seu tokenizer associado (mais em "),x=i(_,"CODE",{});var W=p(x);O=l(W,"AutoClass"),W.forEach(t),S=l(_," abaixo):"),_.forEach(t),I=$(z),k(F.$$.fragment,z)},m(z,_){c(z,s,_),a(s,u),a(s,o),a(o,f),a(s,h),a(s,g),a(g,C),a(s,M),a(s,x),a(x,O),a(s,S),c(z,I,_),w(F,z,_),U=!0},p:ye,i(z){U||(j(F.$$.fragment,z),U=!0)},o(z){A(F.$$.fragment,z),U=!1},d(z){z&&t(s),z&&t(I),q(F,z)}}}function Lp(P){let s,u;return s=new _e({props:{$$slots:{default:[Hp]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Rp(P){let s,u,o,f,h,g,C,M,x,O,S,I,F,U;return F=new L({props:{code:`from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),{c(){s=n("p"),u=r("Use o "),o=n("code"),f=r("TFAutoModelForSequenceClassification"),h=r(" and "),g=n("code"),C=r("AutoTokenizer"),M=r(" para carregar o modelo pr\xE9-treinado e o tokenizer associado (mais em "),x=n("code"),O=r("TFAutoClass"),S=r(" abaixo):"),I=d(),E(F.$$.fragment)},l(z){s=i(z,"P",{});var _=p(s);u=l(_,"Use o "),o=i(_,"CODE",{});var y=p(o);f=l(y,"TFAutoModelForSequenceClassification"),y.forEach(t),h=l(_," and "),g=i(_,"CODE",{});var D=p(g);C=l(D,"AutoTokenizer"),D.forEach(t),M=l(_," para carregar o modelo pr\xE9-treinado e o tokenizer associado (mais em "),x=i(_,"CODE",{});var W=p(x);O=l(W,"TFAutoClass"),W.forEach(t),S=l(_," abaixo):"),_.forEach(t),I=$(z),k(F.$$.fragment,z)},m(z,_){c(z,s,_),a(s,u),a(s,o),a(o,f),a(s,h),a(s,g),a(g,C),a(s,M),a(s,x),a(x,O),a(s,S),c(z,I,_),w(F,z,_),U=!0},p:ye,i(z){U||(j(F.$$.fragment,z),U=!0)},o(z){A(F.$$.fragment,z),U=!1},d(z){z&&t(s),z&&t(I),q(F,z)}}}function Wp(P){let s,u;return s=new _e({props:{$$slots:{default:[Rp]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Vp(P){let s,u;return s=new L({props:{code:`pt_batch = tokenizer(
["We are very happy to show you the \u{1F917} transformers library.", "We hope you don't hate it."],
padding=True,
truncation=True,
max_length=512,
return_tensors="pt",
)`,highlighted:`<span class="hljs-meta">>>> </span>pt_batch = tokenizer(
<span class="hljs-meta">... </span> [<span class="hljs-string">"We are very happy to show you the \u{1F917} transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>],
<span class="hljs-meta">... </span> padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span>)`}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p:ye,i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Gp(P){let s,u;return s=new _e({props:{$$slots:{default:[Vp]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Bp(P){let s,u;return s=new L({props:{code:`tf_batch = tokenizer(
["We are very happy to show you the \u{1F917} Transformers library.", "We hope you don't hate it."],
padding=True,
truncation=True,
max_length=512,
return_tensors="tf",
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_batch = tokenizer(
<span class="hljs-meta">... </span> [<span class="hljs-string">"We are very happy to show you the \u{1F917} Transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>],
<span class="hljs-meta">... </span> padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"tf"</span>,
<span class="hljs-meta">... </span>)`}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p:ye,i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Yp(P){let s,u;return s=new _e({props:{$$slots:{default:[Bp]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Jp(P){let s,u,o,f,h,g,C,M;return{c(){s=n("p"),u=r("Veja o "),o=n("a"),f=r("sum\xE1rio de tarefas"),h=r(" para qual classe de "),g=n("code"),C=r("AutoModel"),M=r(" usar para cada tarefa."),this.h()},l(x){s=i(x,"P",{});var O=p(s);u=l(O,"Veja o "),o=i(O,"A",{href:!0});var S=p(o);f=l(S,"sum\xE1rio de tarefas"),S.forEach(t),h=l(O," para qual classe de "),g=i(O,"CODE",{});var I=p(g);C=l(I,"AutoModel"),I.forEach(t),M=l(O," usar para cada tarefa."),O.forEach(t),this.h()},h(){b(o,"href","./task_summary")},m(x,O){c(x,s,O),a(s,u),a(s,o),a(o,f),a(s,h),a(s,g),a(g,C),a(s,M)},d(x){x&&t(s)}}}function Qp(P){let s,u,o,f,h,g,C,M,x,O,S,I,F,U,z,_,y,D,W,R,oe,Y,ee,J,V,X,Q,K,ue,re,de,le,ae,ne,$e,T,N,ie;return _=new L({props:{code:`from transformers import AutoModelForSequenceClassification
model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)`}}),D=new vt({props:{$$slots:{default:[Jp]},$$scope:{ctx:P}}}),X=new L({props:{code:"pt_outputs = pt_model(**pt_batch)",highlighted:'<span class="hljs-meta">>>> </span>pt_outputs = pt_model(**pt_batch)'}}),N=new L({props:{code:`from torch import nn
pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
print(pt_predictions)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn
<span class="hljs-meta">>>> </span>pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(pt_predictions)
tensor([[<span class="hljs-number">0.0021</span>, <span class="hljs-number">0.0018</span>, <span class="hljs-number">0.0115</span>, <span class="hljs-number">0.2121</span>, <span class="hljs-number">0.7725</span>],
[<span class="hljs-number">0.2084</span>, <span class="hljs-number">0.1826</span>, <span class="hljs-number">0.1969</span>, <span class="hljs-number">0.1755</span>, <span class="hljs-number">0.2365</span>]], grad_fn=<SoftmaxBackward0>)`}}),{c(){s=n("p"),u=r("\u{1F917} Transformers fornecem uma maneira simples e unificada de carregar inst\xE2ncias pr\xE9-treinadas. Isso significa que voc\xEA pode carregar um "),o=n("code"),f=r("AutoModel"),h=r(" como carregaria um "),g=n("code"),C=r("AutoTokenizer"),M=r(". A \xFAnica diferen\xE7a \xE9 selecionar o "),x=n("code"),O=r("AutoModel"),S=r(" correto para a tarefa. Como voc\xEA est\xE1 fazendo classifica\xE7\xE3o de texto ou sequ\xEAncia, carregue "),I=n("code"),F=r("AutoModelForSequenceClassification"),U=r(":"),z=d(),E(_.$$.fragment),y=d(),E(D.$$.fragment),W=d(),R=n("p"),oe=r("Agora voc\xEA pode passar seu grupo de entradas pr\xE9-processadas diretamente para o modelo. Voc\xEA apenas tem que descompactar o dicion\xE1rio usando "),Y=n("code"),ee=r("**"),J=r(":"),V=d(),E(X.$$.fragment),Q=d(),K=n("p"),ue=r("O modelo gera as ativa\xE7\xF5es finais no atributo "),re=n("code"),de=r("logits"),le=r(". Aplique a fun\xE7\xE3o softmax aos "),ae=n("code"),ne=r("logits"),$e=r(" para recuperar as probabilidades:"),T=d(),E(N.$$.fragment)},l(v){s=i(v,"P",{});var H=p(s);u=l(H,"\u{1F917} Transformers fornecem uma maneira simples e unificada de carregar inst\xE2ncias pr\xE9-treinadas. Isso significa que voc\xEA pode carregar um "),o=i(H,"CODE",{});var me=p(o);f=l(me,"AutoModel"),me.forEach(t),h=l(H," como carregaria um "),g=i(H,"CODE",{});var Ce=p(g);C=l(Ce,"AutoTokenizer"),Ce.forEach(t),M=l(H,". A \xFAnica diferen\xE7a \xE9 selecionar o "),x=i(H,"CODE",{});var fe=p(x);O=l(fe,"AutoModel"),fe.forEach(t),S=l(H," correto para a tarefa. Como voc\xEA est\xE1 fazendo classifica\xE7\xE3o de texto ou sequ\xEAncia, carregue "),I=i(H,"CODE",{});var ge=p(I);F=l(ge,"AutoModelForSequenceClassification"),ge.forEach(t),U=l(H,":"),H.forEach(t),z=$(v),k(_.$$.fragment,v),y=$(v),k(D.$$.fragment,v),W=$(v),R=i(v,"P",{});var pe=p(R);oe=l(pe,"Agora voc\xEA pode passar seu grupo de entradas pr\xE9-processadas diretamente para o modelo. Voc\xEA apenas tem que descompactar o dicion\xE1rio usando "),Y=i(pe,"CODE",{});var Fe=p(Y);ee=l(Fe,"**"),Fe.forEach(t),J=l(pe,":"),pe.forEach(t),V=$(v),k(X.$$.fragment,v),Q=$(v),K=i(v,"P",{});var ve=p(K);ue=l(ve,"O modelo gera as ativa\xE7\xF5es finais no atributo "),re=i(ve,"CODE",{});var Ba=p(re);de=l(Ba,"logits"),Ba.forEach(t),le=l(ve,". Aplique a fun\xE7\xE3o softmax aos "),ae=i(ve,"CODE",{});var da=p(ae);ne=l(da,"logits"),da.forEach(t),$e=l(ve," para recuperar as probabilidades:"),ve.forEach(t),T=$(v),k(N.$$.fragment,v)},m(v,H){c(v,s,H),a(s,u),a(s,o),a(o,f),a(s,h),a(s,g),a(g,C),a(s,M),a(s,x),a(x,O),a(s,S),a(s,I),a(I,F),a(s,U),c(v,z,H),w(_,v,H),c(v,y,H),w(D,v,H),c(v,W,H),c(v,R,H),a(R,oe),a(R,Y),a(Y,ee),a(R,J),c(v,V,H),w(X,v,H),c(v,Q,H),c(v,K,H),a(K,ue),a(K,re),a(re,de),a(K,le),a(K,ae),a(ae,ne),a(K,$e),c(v,T,H),w(N,v,H),ie=!0},p(v,H){const me={};H&2&&(me.$$scope={dirty:H,ctx:v}),D.$set(me)},i(v){ie||(j(_.$$.fragment,v),j(D.$$.fragment,v),j(X.$$.fragment,v),j(N.$$.fragment,v),ie=!0)},o(v){A(_.$$.fragment,v),A(D.$$.fragment,v),A(X.$$.fragment,v),A(N.$$.fragment,v),ie=!1},d(v){v&&t(s),v&&t(z),q(_,v),v&&t(y),q(D,v),v&&t(W),v&&t(R),v&&t(V),q(X,v),v&&t(Q),v&&t(K),v&&t(T),q(N,v)}}}function Kp(P){let s,u;return s=new _e({props:{$$slots:{default:[Qp]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function Zp(P){let s,u,o,f,h,g,C,M;return{c(){s=n("p"),u=r("Veja o "),o=n("a"),f=r("sum\xE1rio de tarefas"),h=r(" para qual classe de "),g=n("code"),C=r("AutoModel"),M=r(" usar para cada tarefa."),this.h()},l(x){s=i(x,"P",{});var O=p(s);u=l(O,"Veja o "),o=i(O,"A",{href:!0});var S=p(o);f=l(S,"sum\xE1rio de tarefas"),S.forEach(t),h=l(O," para qual classe de "),g=i(O,"CODE",{});var I=p(g);C=l(I,"AutoModel"),I.forEach(t),M=l(O," usar para cada tarefa."),O.forEach(t),this.h()},h(){b(o,"href","./task_summary")},m(x,O){c(x,s,O),a(s,u),a(s,o),a(o,f),a(s,h),a(s,g),a(g,C),a(s,M)},d(x){x&&t(s)}}}function Xp(P){let s,u,o,f,h,g,C,M,x,O,S,I,F,U,z,_,y,D,W,R,oe,Y,ee,J,V,X,Q,K,ue,re,de,le,ae,ne,$e;return _=new L({props:{code:`from transformers import TFAutoModelForSequenceClassification
model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)`}}),D=new vt({props:{$$slots:{default:[Zp]},$$scope:{ctx:P}}}),ee=new L({props:{code:"tf_outputs = tf_model(tf_batch)",highlighted:'<span class="hljs-meta">>>> </span>tf_outputs = tf_model(tf_batch)'}}),ne=new L({props:{code:`import tensorflow as tf
tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
tf_predictions`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">>>> </span>tf_predictions`}}),{c(){s=n("p"),u=r("\u{1F917} Transformers fornecem uma maneira simples e unificada de carregar inst\xE2ncias pr\xE9-treinadas. Isso significa que voc\xEA pode carregar um "),o=n("code"),f=r("TFAutoModel"),h=r(" como carregaria um "),g=n("code"),C=r("AutoTokenizer"),M=r(". A \xFAnica diferen\xE7a \xE9 selecionar o "),x=n("code"),O=r("TFAutoModel"),S=r(" correto para a tarefa. Como voc\xEA est\xE1 fazendo classifica\xE7\xE3o de texto ou sequ\xEAncia, carregue "),I=n("code"),F=r("TFAutoModelForSequenceClassification"),U=r(":"),z=d(),E(_.$$.fragment),y=d(),E(D.$$.fragment),W=d(),R=n("p"),oe=r("Agora voc\xEA pode passar seu grupo de entradas pr\xE9-processadas diretamente para o modelo atrav\xE9s da passagem de chaves de dicion\xE1rios ao tensor."),Y=d(),E(ee.$$.fragment),J=d(),V=n("p"),X=r("O modelo gera as ativa\xE7\xF5es finais no atributo "),Q=n("code"),K=r("logits"),ue=r(". Aplique a fun\xE7\xE3o softmax aos "),re=n("code"),de=r("logits"),le=r(" para recuperar as probabilidades:"),ae=d(),E(ne.$$.fragment)},l(T){s=i(T,"P",{});var N=p(s);u=l(N,"\u{1F917} Transformers fornecem uma maneira simples e unificada de carregar inst\xE2ncias pr\xE9-treinadas. Isso significa que voc\xEA pode carregar um "),o=i(N,"CODE",{});var ie=p(o);f=l(ie,"TFAutoModel"),ie.forEach(t),h=l(N," como carregaria um "),g=i(N,"CODE",{});var v=p(g);C=l(v,"AutoTokenizer"),v.forEach(t),M=l(N,". A \xFAnica diferen\xE7a \xE9 selecionar o "),x=i(N,"CODE",{});var H=p(x);O=l(H,"TFAutoModel"),H.forEach(t),S=l(N," correto para a tarefa. Como voc\xEA est\xE1 fazendo classifica\xE7\xE3o de texto ou sequ\xEAncia, carregue "),I=i(N,"CODE",{});var me=p(I);F=l(me,"TFAutoModelForSequenceClassification"),me.forEach(t),U=l(N,":"),N.forEach(t),z=$(T),k(_.$$.fragment,T),y=$(T),k(D.$$.fragment,T),W=$(T),R=i(T,"P",{});var Ce=p(R);oe=l(Ce,"Agora voc\xEA pode passar seu grupo de entradas pr\xE9-processadas diretamente para o modelo atrav\xE9s da passagem de chaves de dicion\xE1rios ao tensor."),Ce.forEach(t),Y=$(T),k(ee.$$.fragment,T),J=$(T),V=i(T,"P",{});var fe=p(V);X=l(fe,"O modelo gera as ativa\xE7\xF5es finais no atributo "),Q=i(fe,"CODE",{});var ge=p(Q);K=l(ge,"logits"),ge.forEach(t),ue=l(fe,". Aplique a fun\xE7\xE3o softmax aos "),re=i(fe,"CODE",{});var pe=p(re);de=l(pe,"logits"),pe.forEach(t),le=l(fe," para recuperar as probabilidades:"),fe.forEach(t),ae=$(T),k(ne.$$.fragment,T)},m(T,N){c(T,s,N),a(s,u),a(s,o),a(o,f),a(s,h),a(s,g),a(g,C),a(s,M),a(s,x),a(x,O),a(s,S),a(s,I),a(I,F),a(s,U),c(T,z,N),w(_,T,N),c(T,y,N),w(D,T,N),c(T,W,N),c(T,R,N),a(R,oe),c(T,Y,N),w(ee,T,N),c(T,J,N),c(T,V,N),a(V,X),a(V,Q),a(Q,K),a(V,ue),a(V,re),a(re,de),a(V,le),c(T,ae,N),w(ne,T,N),$e=!0},p(T,N){const ie={};N&2&&(ie.$$scope={dirty:N,ctx:T}),D.$set(ie)},i(T){$e||(j(_.$$.fragment,T),j(D.$$.fragment,T),j(ee.$$.fragment,T),j(ne.$$.fragment,T),$e=!0)},o(T){A(_.$$.fragment,T),A(D.$$.fragment,T),A(ee.$$.fragment,T),A(ne.$$.fragment,T),$e=!1},d(T){T&&t(s),T&&t(z),q(_,T),T&&t(y),q(D,T),T&&t(W),T&&t(R),T&&t(Y),q(ee,T),T&&t(J),T&&t(V),T&&t(ae),q(ne,T)}}}function em(P){let s,u;return s=new _e({props:{$$slots:{default:[Xp]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function am(P){let s,u,o,f,h;return{c(){s=n("p"),u=r("Todos os modelos de \u{1F917} Transformers (PyTorch ou TensorFlow) geram tensores "),o=n("em"),f=r("antes"),h=r(" da fun\xE7\xE3o de ativa\xE7\xE3o final (como softmax) pois essa fun\xE7\xE3o algumas vezes \xE9 fundida com a perda.")},l(g){s=i(g,"P",{});var C=p(s);u=l(C,"Todos os modelos de \u{1F917} Transformers (PyTorch ou TensorFlow) geram tensores "),o=i(C,"EM",{});var M=p(o);f=l(M,"antes"),M.forEach(t),h=l(C," da fun\xE7\xE3o de ativa\xE7\xE3o final (como softmax) pois essa fun\xE7\xE3o algumas vezes \xE9 fundida com a perda."),C.forEach(t)},m(g,C){c(g,s,C),a(s,u),a(s,o),a(o,f),a(s,h)},d(g){g&&t(s)}}}function tm(P){let s,u,o,f,h;return{c(){s=n("p"),u=r(`As sa\xEDdas do modelo \u{1F917} Transformers s\xE3o classes de dados especiais para que seus atributos sejam preenchidos automaticamente em um IDE.
As sa\xEDdas do modelo tamb\xE9m se comportam como uma tupla ou um dicion\xE1rio (por exemplo, voc\xEA pode indexar com um inteiro, uma parte ou uma string), caso em que os atributos `),o=n("code"),f=r("None"),h=r(" s\xE3o ignorados.")},l(g){s=i(g,"P",{});var C=p(s);u=l(C,`As sa\xEDdas do modelo \u{1F917} Transformers s\xE3o classes de dados especiais para que seus atributos sejam preenchidos automaticamente em um IDE.
As sa\xEDdas do modelo tamb\xE9m se comportam como uma tupla ou um dicion\xE1rio (por exemplo, voc\xEA pode indexar com um inteiro, uma parte ou uma string), caso em que os atributos `),o=i(C,"CODE",{});var M=p(o);f=l(M,"None"),M.forEach(t),h=l(C," s\xE3o ignorados."),C.forEach(t)},m(g,C){c(g,s,C),a(s,u),a(s,o),a(o,f),a(s,h)},d(g){g&&t(s)}}}function sm(P){let s,u,o,f,h,g,C,M,x,O,S,I,F,U,z,_;return C=new L({props:{code:`pt_save_directory = "./pt_save_pretrained"
tokenizer.save_pretrained(pt_save_directory)
pt_model.save_pretrained(pt_save_directory)`,highlighted:`<span class="hljs-meta">>>> </span>pt_save_directory = <span class="hljs-string">"./pt_save_pretrained"</span>
<span class="hljs-meta">>>> </span>tokenizer.save_pretrained(pt_save_directory)
<span class="hljs-meta">>>> </span>pt_model.save_pretrained(pt_save_directory)`}}),z=new L({props:{code:'pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained")',highlighted:'<span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"./pt_save_pretrained"</span>)'}}),{c(){s=n("p"),u=r("Uma vez que seu modelo estiver afinado, voc\xEA pode salv\xE1-lo com seu Tokenizer usando "),o=n("code"),f=r("PreTrainedModel.save_pretrained()"),h=r(":"),g=d(),E(C.$$.fragment),M=d(),x=n("p"),O=r("Quando voc\xEA estiver pronto para us\xE1-lo novamente, recarregue com "),S=n("code"),I=r("PreTrainedModel.from_pretrained()"),F=r(":"),U=d(),E(z.$$.fragment)},l(y){s=i(y,"P",{});var D=p(s);u=l(D,"Uma vez que seu modelo estiver afinado, voc\xEA pode salv\xE1-lo com seu Tokenizer usando "),o=i(D,"CODE",{});var W=p(o);f=l(W,"PreTrainedModel.save_pretrained()"),W.forEach(t),h=l(D,":"),D.forEach(t),g=$(y),k(C.$$.fragment,y),M=$(y),x=i(y,"P",{});var R=p(x);O=l(R,"Quando voc\xEA estiver pronto para us\xE1-lo novamente, recarregue com "),S=i(R,"CODE",{});var oe=p(S);I=l(oe,"PreTrainedModel.from_pretrained()"),oe.forEach(t),F=l(R,":"),R.forEach(t),U=$(y),k(z.$$.fragment,y)},m(y,D){c(y,s,D),a(s,u),a(s,o),a(o,f),a(s,h),c(y,g,D),w(C,y,D),c(y,M,D),c(y,x,D),a(x,O),a(x,S),a(S,I),a(x,F),c(y,U,D),w(z,y,D),_=!0},p:ye,i(y){_||(j(C.$$.fragment,y),j(z.$$.fragment,y),_=!0)},o(y){A(C.$$.fragment,y),A(z.$$.fragment,y),_=!1},d(y){y&&t(s),y&&t(g),q(C,y),y&&t(M),y&&t(x),y&&t(U),q(z,y)}}}function om(P){let s,u;return s=new _e({props:{$$slots:{default:[sm]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function rm(P){let s,u,o,f,h,g,C,M,x,O,S,I,F,U,z;return C=new L({props:{code:`tf_save_directory = "./tf_save_pretrained"
tokenizer.save_pretrained(tf_save_directory)
tf_model.save_pretrained(tf_save_directory)`,highlighted:`<span class="hljs-meta">>>> </span>tf_save_directory = <span class="hljs-string">"./tf_save_pretrained"</span>
<span class="hljs-meta">>>> </span>tokenizer.save_pretrained(tf_save_directory)
<span class="hljs-meta">>>> </span>tf_model.save_pretrained(tf_save_directory)`}}),U=new L({props:{code:'tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained")',highlighted:'<span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"./tf_save_pretrained"</span>)'}}),{c(){s=n("p"),u=r("Uma vez que seu modelo estiver afinado, voc\xEA pode salv\xE1-lo com seu Tokenizer usando "),o=n("code"),f=r("TFPreTrainedModel.save_pretrained()"),h=r(":"),g=d(),E(C.$$.fragment),M=d(),x=n("p"),O=r("Quando voc\xEA estiver pronto para us\xE1-lo novamente, recarregue com "),S=n("code"),I=r("TFPreTrainedModel.from_pretrained()"),F=d(),E(U.$$.fragment)},l(_){s=i(_,"P",{});var y=p(s);u=l(y,"Uma vez que seu modelo estiver afinado, voc\xEA pode salv\xE1-lo com seu Tokenizer usando "),o=i(y,"CODE",{});var D=p(o);f=l(D,"TFPreTrainedModel.save_pretrained()"),D.forEach(t),h=l(y,":"),y.forEach(t),g=$(_),k(C.$$.fragment,_),M=$(_),x=i(_,"P",{});var W=p(x);O=l(W,"Quando voc\xEA estiver pronto para us\xE1-lo novamente, recarregue com "),S=i(W,"CODE",{});var R=p(S);I=l(R,"TFPreTrainedModel.from_pretrained()"),R.forEach(t),W.forEach(t),F=$(_),k(U.$$.fragment,_)},m(_,y){c(_,s,y),a(s,u),a(s,o),a(o,f),a(s,h),c(_,g,y),w(C,_,y),c(_,M,y),c(_,x,y),a(x,O),a(x,S),a(S,I),c(_,F,y),w(U,_,y),z=!0},p:ye,i(_){z||(j(C.$$.fragment,_),j(U.$$.fragment,_),z=!0)},o(_){A(C.$$.fragment,_),A(U.$$.fragment,_),z=!1},d(_){_&&t(s),_&&t(g),q(C,_),_&&t(M),_&&t(x),_&&t(F),q(U,_)}}}function lm(P){let s,u;return s=new _e({props:{$$slots:{default:[rm]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function nm(P){let s,u;return s=new L({props:{code:`from transformers import AutoModel
tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
<span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=<span class="hljs-literal">True</span>)`}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p:ye,i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function im(P){let s,u;return s=new _e({props:{$$slots:{default:[nm]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function pm(P){let s,u;return s=new L({props:{code:`from transformers import TFAutoModel
tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
<span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=<span class="hljs-literal">True</span>)`}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p:ye,i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function mm(P){let s,u;return s=new _e({props:{$$slots:{default:[pm]},$$scope:{ctx:P}}}),{c(){E(s.$$.fragment)},l(o){k(s.$$.fragment,o)},m(o,f){w(s,o,f),u=!0},p(o,f){const h={};f&2&&(h.$$scope={dirty:f,ctx:o}),s.$set(h)},i(o){u||(j(s.$$.fragment,o),u=!0)},o(o){A(s.$$.fragment,o),u=!1},d(o){q(s,o)}}}function cm(P){let s,u,o,f,h,g,C,M,x,O,S,I,F,U,z,_,y,D,W,R,oe,Y,ee,J,V,X,Q,K,ue,re,de,le,ae,ne,$e,T,N,ie,v,H,me,Ce,fe,ge,pe,Fe,ve,Ba,da,G,bt,Zo,Xo,Et,er,ar,kt,tr,sr,wt,or,rr,jt,lr,nr,At,ir,pr,qt,mr,cr,Tt,ur,Ts,$a,xt,fr,dr,xs,be,yt,$r,hr,Ct,_r,gr,zt,vr,ys,ha,Pt,br,Er,Cs,Ne,Ot,kr,wr,Mt,jr,zs,Ue,Ps,ze,He,Dt,_a,Ar,St,qr,Os,Le,Tr,It,xr,yr,Ms,Ya,Cr,Ds,Re,Ss,We,zr,Ft,Pr,Or,Is,ga,Fs,Ee,Mr,va,Dr,Sr,Nt,Ir,Fr,Ns,ba,Us,Ve,Nr,Ut,Ur,Hr,Hs,Ea,Ls,ke,Lr,Ht,Rr,Wr,ka,Vr,Gr,Rs,wa,Ws,Ge,Br,Lt,Yr,Jr,Vs,ja,Gs,we,Qr,Aa,Kr,Zr,qa,Xr,el,Bs,Ta,Ys,Ja,al,Js,xa,Qs,Be,tl,Rt,sl,ol,Ks,ya,Zs,Ye,rl,Qa,ll,nl,Xs,Pe,Je,Wt,Ca,il,Vt,pl,eo,he,ml,Gt,cl,ul,za,fl,dl,Pa,$l,hl,ao,Oa,to,Qe,so,je,_l,Bt,gl,vl,Yt,bl,El,oo,Ma,ro,Ae,kl,Ka,wl,jl,Za,Al,ql,lo,Oe,Ke,Jt,Da,Tl,Qt,xl,no,Sa,io,Z,yl,Kt,Cl,zl,Zt,Pl,Ol,Xt,Ml,Dl,Xa,Sl,Il,es,Fl,Nl,as,Ul,Hl,po,qe,Ll,ts,Rl,Wl,ss,Vl,Gl,mo,Me,Ze,os,Ia,Bl,rs,Yl,co,Te,Jl,ls,Ql,Kl,et,Zl,Xl,uo,Xe,en,ns,an,tn,fo,Fa,$o,ea,sn,is,on,rn,ho,at,ln,_o,Na,go,tt,nn,vo,aa,st,ot,pn,mn,cn,rt,lt,un,fn,bo,ta,dn,ps,$n,hn,Eo,sa,ko,oa,_n,nt,gn,vn,wo,De,ra,ms,Ua,bn,cs,En,jo,la,Ao,na,qo,B,kn,Ha,us,wn,jn,fs,An,qn,La,Tn,xn,ds,yn,Cn,$s,zn,Pn,Ra,On,Mn,it,Dn,Sn,To,ia,xo,Se,pa,hs,Wa,In,_s,Fn,yo,ma,Co,xe,Nn,gs,Un,Hn,vs,Ln,Rn,zo,ca,Po;return g=new fa({}),S=new Mp({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/quicktour.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/quicktour.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/quicktour.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/quicktour.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/quicktour.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/quicktour.ipynb"}]}}),Y=new vt({props:{$$slots:{default:[Dp]},$$scope:{ctx:P}}}),Q=new fa({}),N=new xp({props:{id:"tiZFewofSLM"}}),Ue=new vt({props:{$$slots:{default:[Sp]},$$scope:{ctx:P}}}),_a=new fa({}),Re=new gt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Up],pytorch:[Fp]},$$scope:{ctx:P}}}),ga=new L({props:{code:`from transformers import pipeline
classifier = pipeline("sentiment-analysis")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>classifier = pipeline(<span class="hljs-string">"sentiment-analysis"</span>)`}}),ba=new L({props:{code:'classifier("We are very happy to show you the \u{1F917} Transformers library.")',highlighted:`<span class="hljs-meta">>>> </span>classifier(<span class="hljs-string">"We are very happy to show you the \u{1F917} Transformers library."</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.9998</span>}]`}}),Ea=new L({props:{code:`results = classifier(["We are very happy to show you the \u{1F917} Transformers library.", "We hope you don't hate it."])
for result in results:
print(f"label: {result['label']}, with score: {round(result['score'], 4)}")`,highlighted:`<span class="hljs-meta">>>> </span>results = classifier([<span class="hljs-string">"We are very happy to show you the \u{1F917} Transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>])
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> result <span class="hljs-keyword">in</span> results:
<span class="hljs-meta">... </span> <span class="hljs-built_in">print</span>(<span class="hljs-string">f"label: <span class="hljs-subst">{result[<span class="hljs-string">'label'</span>]}</span>, with score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">'score'</span>], <span class="hljs-number">4</span>)}</span>"</span>)
label: POSITIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.9998</span>
label: NEGATIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.5309</span>`}}),wa=new L({props:{code:"pip install datasets ",highlighted:"pip install datasets "}}),ja=new L({props:{code:`import torch
from transformers import pipeline
speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>speech_recognizer = pipeline(<span class="hljs-string">"automatic-speech-recognition"</span>, model=<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>)`}}),Ta=new L({props:{code:`from datasets import load_dataset, Audio
dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio
<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"PolyAI/minds14"</span>, name=<span class="hljs-string">"en-US"</span>, split=<span class="hljs-string">"train"</span>)`}}),xa=new L({props:{code:'dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))',highlighted:'<span class="hljs-meta">>>> </span>dataset = dataset.cast_column(<span class="hljs-string">"audio"</span>, Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))'}}),ya=new L({props:{code:`result = speech_recognizer(dataset[:4]["audio"])
print([d["text"] for d in result])`,highlighted:`<span class="hljs-meta">>>> </span>result = speech_recognizer(dataset[:<span class="hljs-number">4</span>][<span class="hljs-string">"audio"</span>])
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>([d[<span class="hljs-string">"text"</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> result])
[<span class="hljs-string">'I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT'</span>, <span class="hljs-string">"FONDERING HOW I'D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE"</span>, <span class="hljs-string">"I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE APSO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS"</span>, <span class="hljs-string">'HOW DO I TURN A JOIN A COUNT'</span>]`}}),Ca=new fa({}),Oa=new L({props:{code:'model_name = "nlptown/bert-base-multilingual-uncased-sentiment"',highlighted:'<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>'}}),Qe=new gt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Wp],pytorch:[Lp]},$$scope:{ctx:P}}}),Ma=new L({props:{code:`classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
classifier("Nous sommes tr\xE8s heureux de vous pr\xE9senter la biblioth\xE8que \u{1F917} Transformers.")`,highlighted:`<span class="hljs-meta">>>> </span>classifier = pipeline(<span class="hljs-string">"sentiment-analysis"</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">>>> </span>classifier(<span class="hljs-string">"Nous sommes tr\xE8s heureux de vous pr\xE9senter la biblioth\xE8que \u{1F917} Transformers."</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'5 stars'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.7273</span>}]`}}),Da=new fa({}),Sa=new xp({props:{id:"AhChOFRegn4"}}),Ia=new fa({}),Fa=new L({props:{code:`from transformers import AutoTokenizer
model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)`}}),Na=new L({props:{code:`encoding = tokenizer("We are very happy to show you the \u{1F917} Transformers library.")
print(encoding)`,highlighted:`<span class="hljs-meta">>>> </span>encoding = tokenizer(<span class="hljs-string">"We are very happy to show you the \u{1F917} Transformers library."</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(encoding)
{<span class="hljs-string">'input_ids'</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">11312</span>, <span class="hljs-number">10320</span>, <span class="hljs-number">12495</span>, <span class="hljs-number">19308</span>, <span class="hljs-number">10114</span>, <span class="hljs-number">11391</span>, <span class="hljs-number">10855</span>, <span class="hljs-number">10103</span>, <span class="hljs-number">100</span>, <span class="hljs-number">58263</span>, <span class="hljs-number">13299</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
<span class="hljs-string">'token_type_ids'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'attention_mask'</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]}`}}),sa=new gt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Yp],pytorch:[Gp]},$$scope:{ctx:P}}}),Ua=new fa({}),la=new gt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[em],pytorch:[Kp]},$$scope:{ctx:P}}}),na=new vt({props:{$$slots:{default:[am]},$$scope:{ctx:P}}}),ia=new vt({props:{$$slots:{default:[tm]},$$scope:{ctx:P}}}),Wa=new fa({}),ma=new gt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[lm],pytorch:[om]},$$scope:{ctx:P}}}),ca=new gt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[mm],pytorch:[im]},$$scope:{ctx:P}}}),{c(){s=n("meta"),u=d(),o=n("h1"),f=n("a"),h=n("span"),E(g.$$.fragment),C=d(),M=n("span"),x=r("Tour r\xE1pido"),O=d(),E(S.$$.fragment),I=d(),F=n("p"),U=r("Comece a trabalhar com \u{1F917} Transformers! Comece usando "),z=n("code"),_=r("pipeline()"),y=r(" para r\xE1pida infer\xEAncia e facilmente carregue um modelo pr\xE9-treinado e um tokenizer com "),D=n("a"),W=r("AutoClass"),R=r(" para resolver tarefas de texto, vis\xE3o ou \xE1udio."),oe=d(),E(Y.$$.fragment),ee=d(),J=n("h2"),V=n("a"),X=n("span"),E(Q.$$.fragment),K=d(),ue=n("span"),re=r("Pipeline"),de=d(),le=n("p"),ae=n("code"),ne=r("pipeline()"),$e=r(" \xE9 a maneira mais f\xE1cil de usar um modelo pr\xE9-treinado para uma dada tarefa."),T=d(),E(N.$$.fragment),ie=d(),v=n("p"),H=r("A "),me=n("code"),Ce=r("pipeline()"),fe=r(" apoia diversas tarefas fora da caixa:"),ge=d(),pe=n("p"),Fe=n("strong"),ve=r("Texto"),Ba=r(":"),da=d(),G=n("ul"),bt=n("li"),Zo=r("An\xE1lise sentimental: classifica a polaridade de um texto."),Xo=d(),Et=n("li"),er=r("Gera\xE7\xE3o de texto (em Ingl\xEAs): gera texto a partir de uma entrada."),ar=d(),kt=n("li"),tr=r("Reconhecimento de entidade mencionada: legenda cada palavra com uma classe que a representa (pessoa, data, local, etc\u2026)"),sr=d(),wt=n("li"),or=r("Respostas: extrai uma resposta dado algum contexto e uma quest\xE3o"),rr=d(),jt=n("li"),lr=r("M\xE1scara de preenchimento: preenche o espa\xE7o, dado um texto com m\xE1scaras de palavras."),nr=d(),At=n("li"),ir=r("Sumariza\xE7\xE3o: gera o resumo de um texto longo ou documento."),pr=d(),qt=n("li"),mr=r("Tradu\xE7\xE3o: traduz texto para outra l\xEDngua."),cr=d(),Tt=n("li"),ur=r("Extra\xE7\xE3o de caracter\xEDsticas: cria um tensor que representa o texto."),Ts=d(),$a=n("p"),xt=n("strong"),fr=r("Imagem"),dr=r(":"),xs=d(),be=n("ul"),yt=n("li"),$r=r("Classifica\xE7\xE3o de imagens: classifica uma imagem."),hr=d(),Ct=n("li"),_r=r("Segmenta\xE7\xE3o de imagem: classifica cada pixel da imagem."),gr=d(),zt=n("li"),vr=r("Detec\xE7\xE3o de objetos: detecta objetos em uma imagem."),ys=d(),ha=n("p"),Pt=n("strong"),br=r("Audio"),Er=r(":"),Cs=d(),Ne=n("ul"),Ot=n("li"),kr=r("Classfica\xE7\xE3o de \xE1udio: legenda um trecho de \xE1udio fornecido."),wr=d(),Mt=n("li"),jr=r("Reconhecimento de fala autom\xE1tico: transcreve audio em texto."),zs=d(),E(Ue.$$.fragment),Ps=d(),ze=n("h3"),He=n("a"),Dt=n("span"),E(_a.$$.fragment),Ar=d(),St=n("span"),qr=r("Uso da pipeline"),Os=d(),Le=n("p"),Tr=r("No exemplo a seguir, voc\xEA usar\xE1 "),It=n("code"),xr=r("pipeline()"),yr=r(" para an\xE1lise sentimental."),Ms=d(),Ya=n("p"),Cr=r("Instale as seguintes depend\xEAncias se voc\xEA ainda n\xE3o o fez:"),Ds=d(),E(Re.$$.fragment),Ss=d(),We=n("p"),zr=r("Importe "),Ft=n("code"),Pr=r("pipeline()"),Or=r(" e especifique a tarefa que deseja completar:"),Is=d(),E(ga.$$.fragment),Fs=d(),Ee=n("p"),Mr=r("A pipeline baixa and armazena um "),va=n("a"),Dr=r("modelo pr\xE9-treinado"),Sr=r(" padr\xE3o e tokenizer para an\xE1lise sentimental. Agora voc\xEA pode usar "),Nt=n("code"),Ir=r("classifier"),Fr=r(" no texto alvo:"),Ns=d(),E(ba.$$.fragment),Us=d(),Ve=n("p"),Nr=r("Para mais de uma senten\xE7a, passe uma lista para a "),Ut=n("code"),Ur=r("pipeline()"),Hr=r(", a qual retornar\xE1 uma lista de dicion\xE1rios:"),Hs=d(),E(Ea.$$.fragment),Ls=d(),ke=n("p"),Lr=r("A "),Ht=n("code"),Rr=r("pipeline()"),Wr=r(" tamb\xE9m pode iterar sobre um Dataset inteiro. Comece instalando a biblioteca de "),ka=n("a"),Vr=r("\u{1F917} Datasets"),Gr=r(":"),Rs=d(),E(wa.$$.fragment),Ws=d(),Ge=n("p"),Br=r("Crie uma "),Lt=n("code"),Yr=r("pipeline()"),Jr=r(" com a tarefa que deseja resolver e o modelo que deseja usar."),Vs=d(),E(ja.$$.fragment),Gs=d(),we=n("p"),Qr=r("A seguir, carregue uma base de dados (confira a \u{1F917} "),Aa=n("a"),Kr=r("Inicia\xE7\xE3o em Datasets"),Zr=r(" para mais detalhes) que voc\xEA gostaria de iterar sobre. Por exemplo, vamos carregar o dataset "),qa=n("a"),Xr=r("MInDS-14"),el=r(":"),Bs=d(),E(Ta.$$.fragment),Ys=d(),Ja=n("p"),al=r("Precisamos garantir que a taxa de amostragem do conjunto de dados corresponda \xE0 taxa de amostragem em que o facebook/wav2vec2-base-960h foi treinado."),Js=d(),E(xa.$$.fragment),Qs=d(),Be=n("p"),tl=r("Os arquivos de \xE1udio s\xE3o carregados e re-amostrados automaticamente ao chamar a coluna "),Rt=n("code"),sl=r('"audio"'),ol=r(`.
Vamos extrair as arrays de formas de onda originais das primeiras 4 amostras e pass\xE1-las como uma lista para o pipeline:`),Ks=d(),E(ya.$$.fragment),Zs=d(),Ye=n("p"),rl=r("Para um conjunto de dados maior onde as entradas s\xE3o maiores (como em fala ou vis\xE3o), ser\xE1 necess\xE1rio passar um gerador em vez de uma lista que carregue todas as entradas na mem\xF3ria. Consulte a "),Qa=n("a"),ll=r("documenta\xE7\xE3o do pipeline"),nl=r(" para mais informa\xE7\xF5es."),Xs=d(),Pe=n("h3"),Je=n("a"),Wt=n("span"),E(Ca.$$.fragment),il=d(),Vt=n("span"),pl=r("Use outro modelo e tokenizer na pipeline"),eo=d(),he=n("p"),ml=r("A "),Gt=n("code"),cl=r("pipeline()"),ul=r(" pode acomodar qualquer modelo do "),za=n("a"),fl=r("Model Hub"),dl=r(", facilitando sua adapta\xE7\xE3o para outros casos de uso. Por exemplo, se voc\xEA quiser um modelo capaz de lidar com texto em franc\xEAs, use as tags no Model Hub para filtrar um modelo apropriado. O principal resultado filtrado retorna um "),Pa=n("a"),$l=r("modelo BERT"),hl=r(" bil\xEDngue ajustado para an\xE1lise de sentimentos. \xD3timo, vamos usar este modelo!"),ao=d(),E(Oa.$$.fragment),to=d(),E(Qe.$$.fragment),so=d(),je=n("p"),_l=r("Ent\xE3o voc\xEA pode especificar o modelo e o tokenizador na "),Bt=n("code"),gl=r("pipeline()"),vl=r(" e aplicar o "),Yt=n("code"),bl=r("classifier"),El=r(" no seu texto alvo:"),oo=d(),E(Ma.$$.fragment),ro=d(),Ae=n("p"),kl=r("Se voc\xEA n\xE3o conseguir achar um modelo para o seu caso de uso, precisar\xE1 usar fine-tune em um modelo pr\xE9-treinado nos seus dados. Veja nosso "),Ka=n("a"),wl=r("tutorial de fine-tuning"),jl=r(" para descobrir como. Finalmente, depois que voc\xEA tiver usado esse processo em seu modelo, considere compartilh\xE1-lo conosco (veja o tutorial "),Za=n("a"),Al=r("aqui"),ql=r(") na plataforma Model Hub afim de democratizar NLP! \u{1F917}"),lo=d(),Oe=n("h2"),Ke=n("a"),Jt=n("span"),E(Da.$$.fragment),Tl=d(),Qt=n("span"),xl=r("AutoClass"),no=d(),E(Sa.$$.fragment),io=d(),Z=n("p"),yl=r("Por baixo dos panos, as classes "),Kt=n("code"),Cl=r("AutoModelForSequenceClassification"),zl=r(" e "),Zt=n("code"),Pl=r("AutoTokenizer"),Ol=r(" trabalham juntas para fortificar o "),Xt=n("code"),Ml=r("pipeline()"),Dl=r(". Um "),Xa=n("a"),Sl=r("AutoClass"),Il=r(" \xE9 um atalho que automaticamente recupera a arquitetura de um modelo pr\xE9-treinado a partir de seu nome ou caminho. Basta selecionar a "),es=n("code"),Fl=r("AutoClass"),Nl=r(" apropriada para sua tarefa e seu tokenizer associado com "),as=n("code"),Ul=r("AutoTokenizer"),Hl=r("."),po=d(),qe=n("p"),Ll=r("Vamos voltar ao nosso exemplo e ver como voc\xEA pode usar a "),ts=n("code"),Rl=r("AutoClass"),Wl=r(" para replicar os resultados do "),ss=n("code"),Vl=r("pipeline()"),Gl=r("."),mo=d(),Me=n("h3"),Ze=n("a"),os=n("span"),E(Ia.$$.fragment),Bl=d(),rs=n("span"),Yl=r("AutoTokenizer"),co=d(),Te=n("p"),Jl=r("Um tokenizer \xE9 respons\xE1vel por pr\xE9-processar o texto em um formato que seja compreens\xEDvel para o modelo. Primeiro, o tokenizer dividir\xE1 o texto em palavras chamadas "),ls=n("em"),Ql=r("tokens"),Kl=r(". Existem v\xE1rias regras que regem o processo de tokeniza\xE7\xE3o, incluindo como dividir uma palavra e em que n\xEDvel (saiba mais sobre tokeniza\xE7\xE3o "),et=n("a"),Zl=r("aqui"),Xl=r("). A coisa mais importante a lembrar, por\xE9m, \xE9 que voc\xEA precisa instanciar o tokenizer com o mesmo nome do modelo para garantir que est\xE1 usando as mesmas regras de tokeniza\xE7\xE3o com as quais um modelo foi pr\xE9-treinado."),uo=d(),Xe=n("p"),en=r("Carregue um tokenizer com "),ns=n("code"),an=r("AutoTokenizer"),tn=r(":"),fo=d(),E(Fa.$$.fragment),$o=d(),ea=n("p"),sn=r("Em seguida, o tokenizer converte os tokens em n\xFAmeros para construir um tensor como entrada para o modelo. Isso \xE9 conhecido como o "),is=n("em"),on=r("vocabul\xE1rio"),rn=r(" do modelo."),ho=d(),at=n("p"),ln=r("Passe o texto para o tokenizer:"),_o=d(),E(Na.$$.fragment),go=d(),tt=n("p"),nn=r("O tokenizer retornar\xE1 um dicion\xE1rio contendo:"),vo=d(),aa=n("ul"),st=n("li"),ot=n("a"),pn=r("input_ids"),mn=r(": representa\xE7\xF5es num\xE9ricas de seus tokens."),cn=d(),rt=n("li"),lt=n("a"),un=r("atttention_mask"),fn=r(": indica quais tokens devem ser atendidos."),bo=d(),ta=n("p"),dn=r("Assim como o "),ps=n("code"),$n=r("pipeline()"),hn=r(", o tokenizer aceitar\xE1 uma lista de entradas. Al\xE9m disso, o tokenizer tamb\xE9m pode preencher e truncar o texto para retornar um lote com comprimento uniforme:"),Eo=d(),E(sa.$$.fragment),ko=d(),oa=n("p"),_n=r("Leia o tutorial de "),nt=n("a"),gn=r("pr\xE9-processamento"),vn=r(" para obter mais detalhes sobre tokeniza\xE7\xE3o."),wo=d(),De=n("h3"),ra=n("a"),ms=n("span"),E(Ua.$$.fragment),bn=d(),cs=n("span"),En=r("AutoModel"),jo=d(),E(la.$$.fragment),Ao=d(),E(na.$$.fragment),qo=d(),B=n("p"),kn=r("Os modelos s\xE3o um standard "),Ha=n("a"),us=n("code"),wn=r("torch.nn.Module"),jn=r(" ou um ["),fs=n("code"),An=r("tf.keras.Model"),qn=r("](https: //"),La=n("a"),Tn=r("www.tensorflow.org/api_docs/python/tf/keras/Model"),xn=r(") para que voc\xEA possa us\xE1-los em seu loop de treinamento habitual. No entanto, para facilitar as coisas, \u{1F917} Transformers fornece uma classe "),ds=n("code"),yn=r("Trainer"),Cn=r(" para PyTorch que adiciona funcionalidade para treinamento distribu\xEDdo, precis\xE3o mista e muito mais. Para o TensorFlow, voc\xEA pode usar o m\xE9todo "),$s=n("code"),zn=r("fit"),Pn=r(" de "),Ra=n("a"),On=r("Keras"),Mn=r(". Consulte o "),it=n("a"),Dn=r("tutorial de treinamento"),Sn=r(" para obter mais detalhes."),To=d(),E(ia.$$.fragment),xo=d(),Se=n("h3"),pa=n("a"),hs=n("span"),E(Wa.$$.fragment),In=d(),_s=n("span"),Fn=r("Salvar um modelo"),yo=d(),E(ma.$$.fragment),Co=d(),xe=n("p"),Nn=r("Um recurso particularmente interessante dos \u{1F917} Transformers \xE9 a capacidade de salvar um modelo e recarreg\xE1-lo como um modelo PyTorch ou TensorFlow. Use "),gs=n("code"),Un=r("from_pt"),Hn=r(" ou "),vs=n("code"),Ln=r("from_tf"),Rn=r(" para converter o modelo de um framework para outro:"),zo=d(),E(ca.$$.fragment),this.h()},l(e){const m=Pp('[data-svelte="svelte-1phssyn"]',document.head);s=i(m,"META",{name:!0,content:!0}),m.forEach(t),u=$(e),o=i(e,"H1",{class:!0});var Va=p(o);f=i(Va,"A",{id:!0,class:!0,href:!0});var bs=p(f);h=i(bs,"SPAN",{});var Es=p(h);k(g.$$.fragment,Es),Es.forEach(t),bs.forEach(t),C=$(Va),M=i(Va,"SPAN",{});var ks=p(M);x=l(ks,"Tour r\xE1pido"),ks.forEach(t),Va.forEach(t),O=$(e),k(S.$$.fragment,e),I=$(e),F=i(e,"P",{});var Ie=p(F);U=l(Ie,"Comece a trabalhar com \u{1F917} Transformers! 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Vamos extrair as arrays de formas de onda originais das primeiras 4 amostras e pass\xE1-las como uma lista para o pipeline:`),Uo.forEach(t),Ks=$(e),k(ya.$$.fragment,e),Zs=$(e),Ye=i(e,"P",{});var Ho=p(Ye);rl=l(Ho,"Para um conjunto de dados maior onde as entradas s\xE3o maiores (como em fala ou vis\xE3o), ser\xE1 necess\xE1rio passar um gerador em vez de uma lista que carregue todas as entradas na mem\xF3ria. Consulte a "),Qa=i(Ho,"A",{href:!0});var zi=p(Qa);ll=l(zi,"documenta\xE7\xE3o do pipeline"),zi.forEach(t),nl=l(Ho," para mais informa\xE7\xF5es."),Ho.forEach(t),Xs=$(e),Pe=i(e,"H3",{class:!0});var Lo=p(Pe);Je=i(Lo,"A",{id:!0,class:!0,href:!0});var Pi=p(Je);Wt=i(Pi,"SPAN",{});var Oi=p(Wt);k(Ca.$$.fragment,Oi),Oi.forEach(t),Pi.forEach(t),il=$(Lo),Vt=i(Lo,"SPAN",{});var Mi=p(Vt);pl=l(Mi,"Use outro modelo e tokenizer na pipeline"),Mi.forEach(t),Lo.forEach(t),eo=$(e),he=i(e,"P",{});var ua=p(he);ml=l(ua,"A "),Gt=i(ua,"CODE",{});var Di=p(Gt);cl=l(Di,"pipeline()"),Di.forEach(t),ul=l(ua," pode acomodar qualquer modelo do "),za=i(ua,"A",{href:!0,rel:!0});var Si=p(za);fl=l(Si,"Model Hub"),Si.forEach(t),dl=l(ua,", facilitando sua adapta\xE7\xE3o para outros casos de uso. Por exemplo, se voc\xEA quiser um modelo capaz de lidar com texto em franc\xEAs, use as tags no Model Hub para filtrar um modelo apropriado. 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Um "),Xa=i(ce,"A",{href:!0});var Yi=p(Xa);Sl=l(Yi,"AutoClass"),Yi.forEach(t),Il=l(ce," \xE9 um atalho que automaticamente recupera a arquitetura de um modelo pr\xE9-treinado a partir de seu nome ou caminho. 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Al\xE9m disso, o tokenizer tamb\xE9m pode preencher e truncar o texto para retornar um lote com comprimento uniforme:"),Yo.forEach(t),Eo=$(e),k(sa.$$.fragment,e),ko=$(e),oa=i(e,"P",{});var Jo=p(oa);_n=l(Jo,"Leia o tutorial de "),nt=i(Jo,"A",{href:!0});var cp=p(nt);gn=l(cp,"pr\xE9-processamento"),cp.forEach(t),vn=l(Jo," para obter mais detalhes sobre tokeniza\xE7\xE3o."),Jo.forEach(t),wo=$(e),De=i(e,"H3",{class:!0});var Qo=p(De);ra=i(Qo,"A",{id:!0,class:!0,href:!0});var up=p(ra);ms=i(up,"SPAN",{});var fp=p(ms);k(Ua.$$.fragment,fp),fp.forEach(t),up.forEach(t),bn=$(Qo),cs=i(Qo,"SPAN",{});var dp=p(cs);En=l(dp,"AutoModel"),dp.forEach(t),Qo.forEach(t),jo=$(e),k(la.$$.fragment,e),Ao=$(e),k(na.$$.fragment,e),qo=$(e),B=i(e,"P",{});var se=p(B);kn=l(se,"Os modelos s\xE3o um standard "),Ha=i(se,"A",{href:!0,rel:!0});var $p=p(Ha);us=i($p,"CODE",{});var hp=p(us);wn=l(hp,"torch.nn.Module"),hp.forEach(t),$p.forEach(t),jn=l(se," ou um ["),fs=i(se,"CODE",{});var _p=p(fs);An=l(_p,"tf.keras.Model"),_p.forEach(t),qn=l(se,"](https: //"),La=i(se,"A",{href:!0,rel:!0});var gp=p(La);Tn=l(gp,"www.tensorflow.org/api_docs/python/tf/keras/Model"),gp.forEach(t),xn=l(se,") para que voc\xEA possa us\xE1-los em seu loop de treinamento habitual. 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se n\xE3o tiver esse recurso de forma local.`),w.forEach(a),this.h()},h(){d(b,"href","https://colab.research.google.com/"),d(b,"rel","nofollow"),d(E,"href","https://studiolab.sagemaker.aws/"),d(E,"rel","nofollow")},m(A,w){l(A,$,w),s($,y),s($,b),s(b,k),s($,P),s($,E),s(E,O),s($,z)},d(A){A&&a($)}}}function Wm(oe){let $,y,b,k,P,E,O,z,A,w,C,F,Ga,Ur,xt,N,Le,Wr,bs,Gr,Yr,Qr,js,Vr,Jr,ws,Xr,St,Ya,Ot,B,re,ks,Me,Zr,Es,en,Ft,He,Nt,Qa,an,It,ne,sn,Re,tn,on,Lt,Be,Mt,le,rn,Ke,ys,nn,ln,Ht,Ue,Rt,Va,pn,Bt,We,Kt,Ja,Ut,K,ie,qs,Ge,mn,Xa,cn,As,dn,Wt,Ye,Gt,I,un,Ts,fn,hn,Ps,gn,_n,Yt,pe,vn,Qe,$n,bn,Qt,Ve,Vt,me,Jt,U,ce,zs,Je,jn,Cs,wn,Xt,L,kn,Ds,En,yn,Xe,qn,An,Zt,Za,Tn,eo,Ze,ao,W,de,xs,ea,Pn,Ss,zn,so,q,Cn,Os,Dn,xn,Fs,Sn,On,aa,Ns,Fn,Nn,Is,In,Ln,sa,Mn,Hn,to,ta,oo,D,Rn,Ls,Bn,Kn,Ms,Un,Wn,Hs,Gn,Yn,ro,oa,no,ue,Qn,Rs,Vn,Jn,lo,ra,io,G,fe,Bs,na,Xn,Ks,Zn,po,he,el,Us,al,sl,mo,la,co,ge,tl,Ws,ol,rl,uo,ia,fo,es,ho,Y,_e,Gs,pa,nl,Ys,ll,go,ma,_o,as,il,vo,Q,ve,Qs,ca,pl,Vs,ml,$o,M,cl,Js,dl,ul,Xs,fl,hl,bo,da,jo,$e,wo,x,gl,ua,Zs,_l,vl,et,$l,bl,at,jl,wl,ko,fa,Eo,V,be,st,ha,kl,tt,El,yo,ss,yl,qo,ga,Ao,je,ql,_a,ot,Al,Tl,To,va,Po,ts,zo,J,we,rt,$a,Pl,nt,zl,Co,ba,Do,ke,Cl,lt,Dl,xl,xo,os,Sl,So,ja,Oo,Ee,Ol,it,Fl,Nl,Fo,H,wa,ka,Il,pt,Ll,Ml,Hl,Ea,Rl,ya,X,Bl,mt,Kl,Ul,ct,Wl,Gl,Yl,qa,Ql,Aa,dt,Vl,Jl,Ta,No,rs,Xl,Io,Pa,Lo,Z,ye,ut,za,Zl,ft,ei,Mo,qe,ai,ht,si,ti,Ho,Ca,Ro,ns,oi,Bo,Da,Ko,ee,Ae,gt,xa,ri,_t,ni,Uo,Te,li,Sa,vt,ii,pi,Wo,Oa,Go,Pe,mi,$t,ci,di,Yo,Fa,Qo,ze,ui,bt,fi,hi,Vo,Na,Jo,Ce,Xo,ls,gi,Zo,ae,De,jt,Ia,_i,wt,vi,er,xe,$i,La,bi,ji,ar,Ma,sr,se,Se,kt,Ha,wi,Et,ki,tr,R,Ei,yt,yi,qi,Ra,qt,Ai,Ti,or,Ba,rr,is,nr,te,Oe,At,Ka,Pi,Tt,zi,lr,ps,Ci,ir,Fe,Pt,ms,Ua,Di,xi,Si,zt,cs,ds,Oi,Fi,pr;return E=new T({}),C=new Rm({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/training.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/training.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/training.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/training.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/training.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/training.ipynb"}]}}),Me=new T({}),He=new Kr({props:{id:"_BZearw7f0w"}}),Be=new j({props:{code:`from datasets import load_dataset
dataset = load_dataset("yelp_review_full")
dataset[100]`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"yelp_review_full"</span>)
<span class="hljs-meta">>>> </span>dataset[<span class="hljs-number">100</span>]
{<span class="hljs-string">'label'</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">'text'</span>: <span class="hljs-string">'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\\\nThe cashier took my friends\\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\\\"serving off their orders\\\\" when they didn\\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\\\nThe manager was rude when giving me my order. She didn\\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\\\nI\\'ve eaten at various McDonalds restaurants for over 30 years. I\\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'</span>}`}}),Ue=new j({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>)
<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenizer(examples[<span class="hljs-string">"text"</span>], padding=<span class="hljs-string">"max_length"</span>, truncation=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokenized_datasets = dataset.<span class="hljs-built_in">map</span>(tokenize_function, batched=<span class="hljs-literal">True</span>)`}}),We=new j({props:{code:`small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))`,highlighted:`<span class="hljs-meta">>>> </span>small_train_dataset = tokenized_datasets[<span class="hljs-string">"train"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))
<span class="hljs-meta">>>> </span>small_eval_dataset = tokenized_datasets[<span class="hljs-string">"test"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))`}}),Ge=new T({}),Ye=new Kr({props:{id:"nvBXf7s7vTI"}}),Ve=new j({props:{code:`from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>, num_labels=<span class="hljs-number">5</span>)`}}),me=new Mi({props:{$$slots:{default:[Bm]},$$scope:{ctx:oe}}}),Je=new T({}),Ze=new j({props:{code:`from transformers import TrainingArguments
training_args = TrainingArguments(output_dir="test_trainer")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments
<span class="hljs-meta">>>> </span>training_args = TrainingArguments(output_dir=<span class="hljs-string">"test_trainer"</span>)`}}),ea=new T({}),ta=new j({props:{code:`import numpy as np
from datasets import load_metric
metric = load_metric("accuracy")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_metric
<span class="hljs-meta">>>> </span>metric = load_metric(<span class="hljs-string">"accuracy"</span>)`}}),oa=new j({props:{code:`def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_pred</span>):
<span class="hljs-meta">... </span> logits, labels = eval_pred
<span class="hljs-meta">... </span> predictions = np.argmax(logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> metric.compute(predictions=predictions, references=labels)`}}),ra=new j({props:{code:`from transformers import TrainingArguments
training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments
<span class="hljs-meta">>>> </span>training_args = TrainingArguments(output_dir=<span class="hljs-string">"test_trainer"</span>, evaluation_strategy=<span class="hljs-string">"epoch"</span>)`}}),na=new T({}),la=new j({props:{code:`trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)`,highlighted:`<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=small_train_dataset,
<span class="hljs-meta">... </span> eval_dataset=small_eval_dataset,
<span class="hljs-meta">... </span> compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>)`}}),ia=new j({props:{code:"trainer.train()",highlighted:'<span class="hljs-meta">>>> </span>trainer.train()'}}),pa=new T({}),ma=new Kr({props:{id:"rnTGBy2ax1c"}}),ca=new T({}),da=new j({props:{code:`from transformers import DefaultDataCollator
data_collator = DefaultDataCollator(return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator
<span class="hljs-meta">>>> </span>data_collator = DefaultDataCollator(return_tensors=<span class="hljs-string">"tf"</span>)`}}),$e=new Mi({props:{$$slots:{default:[Km]},$$scope:{ctx:oe}}}),fa=new j({props:{code:`tf_train_dataset = small_train_dataset.to_tf_dataset(
columns=["attention_mask", "input_ids", "token_type_ids"],
label_cols=["labels"],
shuffle=True,
collate_fn=data_collator,
batch_size=8,
)
tf_validation_dataset = small_eval_dataset.to_tf_dataset(
columns=["attention_mask", "input_ids", "token_type_ids"],
label_cols=["labels"],
shuffle=False,
collate_fn=data_collator,
batch_size=8,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_dataset = small_train_dataset.to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"token_type_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_dataset = small_eval_dataset.to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"token_type_ids"</span>],
<span class="hljs-meta">... </span> label_cols=[<span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>)`}}),ha=new T({}),ga=new j({props:{code:`import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>, num_labels=<span class="hljs-number">5</span>)`}}),va=new j({props:{code:`model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=tf.metrics.SparseCategoricalAccuracy(),
)
model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)`,highlighted:`<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(
<span class="hljs-meta">... </span> optimizer=tf.keras.optimizers.Adam(learning_rate=<span class="hljs-number">5e-5</span>),
<span class="hljs-meta">... </span> loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=<span class="hljs-literal">True</span>),
<span class="hljs-meta">... </span> metrics=tf.metrics.SparseCategoricalAccuracy(),
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=<span class="hljs-number">3</span>)`}}),$a=new T({}),ba=new Kr({props:{id:"Dh9CL8fyG80"}}),ja=new j({props:{code:`del model
del pytorch_model
del trainer
torch.cuda.empty_cache()`,highlighted:`<span class="hljs-keyword">del</span> model
<span class="hljs-keyword">del</span> pytorch_model
<span class="hljs-keyword">del</span> trainer
torch.cuda.empty_cache()`}}),Ea=new j({props:{code:'tokenized_datasets = tokenized_datasets.remove_columns(["text"])',highlighted:'<span class="hljs-meta">>>> </span>tokenized_datasets = tokenized_datasets.remove_columns([<span class="hljs-string">"text"</span>])'}}),qa=new j({props:{code:'tokenized_datasets = tokenized_datasets.rename_column("label", "labels")',highlighted:'<span class="hljs-meta">>>> </span>tokenized_datasets = tokenized_datasets.rename_column(<span class="hljs-string">"label"</span>, <span class="hljs-string">"labels"</span>)'}}),Ta=new j({props:{code:'tokenized_datasets.set_format("torch")',highlighted:'<span class="hljs-meta">>>> </span>tokenized_datasets.set_format(<span class="hljs-string">"torch"</span>)'}}),Pa=new j({props:{code:`small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))`,highlighted:`<span class="hljs-meta">>>> </span>small_train_dataset = tokenized_datasets[<span class="hljs-string">"train"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))
<span class="hljs-meta">>>> </span>small_eval_dataset = tokenized_datasets[<span class="hljs-string">"test"</span>].shuffle(seed=<span class="hljs-number">42</span>).select(<span class="hljs-built_in">range</span>(<span class="hljs-number">1000</span>))`}}),za=new T({}),Ca=new j({props:{code:`from torch.utils.data import DataLoader
train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torch.utils.data <span class="hljs-keyword">import</span> DataLoader
<span class="hljs-meta">>>> </span>train_dataloader = DataLoader(small_train_dataset, shuffle=<span class="hljs-literal">True</span>, batch_size=<span class="hljs-number">8</span>)
<span class="hljs-meta">>>> </span>eval_dataloader = DataLoader(small_eval_dataset, batch_size=<span class="hljs-number">8</span>)`}}),Da=new j({props:{code:`from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"bert-base-cased"</span>, num_labels=<span class="hljs-number">5</span>)`}}),xa=new T({}),Oa=new j({props:{code:`from torch.optim import AdamW
optimizer = AdamW(model.parameters(), lr=5e-5)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torch.optim <span class="hljs-keyword">import</span> AdamW
<span class="hljs-meta">>>> </span>optimizer = AdamW(model.parameters(), lr=<span class="hljs-number">5e-5</span>)`}}),Fa=new j({props:{code:`from transformers import get_scheduler
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> get_scheduler
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span>num_training_steps = num_epochs * <span class="hljs-built_in">len</span>(train_dataloader)
<span class="hljs-meta">>>> </span>lr_scheduler = get_scheduler(
<span class="hljs-meta">... </span> name=<span class="hljs-string">"linear"</span>, optimizer=optimizer, num_warmup_steps=<span class="hljs-number">0</span>, num_training_steps=num_training_steps
<span class="hljs-meta">... </span>)`}}),Na=new j({props:{code:`import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>device = torch.device(<span class="hljs-string">"cuda"</span>) <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> torch.device(<span class="hljs-string">"cpu"</span>)
<span class="hljs-meta">>>> </span>model.to(device)`}}),Ce=new Mi({props:{$$slots:{default:[Um]},$$scope:{ctx:oe}}}),Ia=new T({}),Ma=new j({props:{code:`from tqdm.auto import tqdm
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm
<span class="hljs-meta">>>> </span>progress_bar = tqdm(<span class="hljs-built_in">range</span>(num_training_steps))
<span class="hljs-meta">>>> </span>model.train()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_epochs):
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
<span class="hljs-meta">... </span> batch = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
<span class="hljs-meta">... </span> outputs = model(**batch)
<span class="hljs-meta">... </span> loss = outputs.loss
<span class="hljs-meta">... </span> loss.backward()
<span class="hljs-meta">... </span> optimizer.step()
<span class="hljs-meta">... </span> lr_scheduler.step()
<span class="hljs-meta">... </span> optimizer.zero_grad()
<span class="hljs-meta">... </span> progress_bar.update(<span class="hljs-number">1</span>)`}}),Ha=new T({}),Ba=new j({props:{code:`metric = load_metric("accuracy")
model.eval()
for batch in eval_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)
metric.add_batch(predictions=predictions, references=batch["labels"])
metric.compute()`,highlighted:`<span class="hljs-meta">>>> </span>metric = load_metric(<span class="hljs-string">"accuracy"</span>)
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">eval</span>()
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> eval_dataloader:
<span class="hljs-meta">... </span> batch = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> batch.items()}
<span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span> outputs = model(**batch)
<span class="hljs-meta">... </span> logits = outputs.logits
<span class="hljs-meta">... </span> predictions = torch.argmax(logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span> metric.add_batch(predictions=predictions, references=batch[<span class="hljs-string">"labels"</span>])
<span class="hljs-meta">>>> </span>metric.compute()`}}),Ka=new T({}),{c(){$=o("meta"),y=m(),b=o("h1"),k=o("a"),P=o("span"),u(E.$$.fragment),O=m(),z=o("span"),A=i("Fine-tuning de um modelo pr\xE9-treinado"),w=m(),u(C.$$.fragment),F=m(),Ga=o("p"),Ur=i(`O uso de um modelo pr\xE9-treinado tem importantes vantagens. Redu\xE7\xE3o do custo computacional, a pegada de carbono, e te
permite utilizar modelos de \xFAltima gera\xE7\xE3o sem ter que treinar um novo desde o in\xEDcio.
O \u{1F917} Transformers proporciona acesso a milhares de modelos pr\xE9-treinados numa ampla gama de tarefas.
Quando utilizar um modelo pr\xE9-treinado, treine-o com um dataset espec\xEDfico para a sua tarefa.
Isto \xE9 chamado de fine-tuning, uma t\xE9cnica de treinamento incrivelmente poderosa. Neste tutorial faremos o fine-tuning
de um modelo pr\xE9-treinado com um framework de Deep Learning da sua escolha:`),xt=m(),N=o("ul"),Le=o("li"),Wr=i("Fine-tuning de um modelo pr\xE9-treinado com o \u{1F917} Transformers "),bs=o("code"),Gr=i("Trainer"),Yr=i("."),Qr=m(),js=o("li"),Vr=i("Fine-tuning de um modelo pr\xE9-treinado no TensorFlow com o Keras."),Jr=m(),ws=o("li"),Xr=i("Fine-tuning de um modelo pr\xE9-treinado em PyTorch nativo."),St=m(),Ya=o("a"),Ot=m(),B=o("h2"),re=o("a"),ks=o("span"),u(Me.$$.fragment),Zr=m(),Es=o("span"),en=i("Preparando um dataset"),Ft=m(),u(He.$$.fragment),Nt=m(),Qa=o("p"),an=i(`Antes de aplicar o fine-tuning a um modelo pr\xE9-treinado, baixe um dataset e prepare-o para o treinamento.
O tutorial anterior ensinar\xE1 a processar os dados para o treinamento, e ent\xE3o poder\xE1 ter a oportunidade de testar
esse novo conhecimento em algo pr\xE1tico.`),It=m(),ne=o("p"),sn=i("Comece carregando o dataset "),Re=o("a"),tn=i("Yelp Reviews"),on=i(":"),Lt=m(),u(Be.$$.fragment),Mt=m(),le=o("p"),rn=i(`Como j\xE1 sabe, \xE9 necess\xE1rio ter um tokenizador para processar o texto e incluir uma estrat\xE9gia de padding e truncamento,
para manejar qualquer tamanho var\xEDavel de sequ\xEAncia. Para processar o seu dataset em apenas um passo, utilize o m\xE9todo de
\u{1F917} Datasets `),Ke=o("a"),ys=o("code"),nn=i("map"),ln=i(` para aplicar uma fun\xE7\xE3o de preprocessamento sobre
todo o dataset.`),Ht=m(),u(Ue.$$.fragment),Rt=m(),Va=o("p"),pn=i("Se desejar, \xE9 poss\xEDvel criar um subconjunto menor do dataset completo para aplicar o fine-tuning e assim reduzir o tempo necess\xE1rio."),Bt=m(),u(We.$$.fragment),Kt=m(),Ja=o("a"),Ut=m(),K=o("h2"),ie=o("a"),qs=o("span"),u(Ge.$$.fragment),mn=m(),Xa=o("span"),cn=i("Fine-tuning com o "),As=o("code"),dn=i("Trainer"),Wt=m(),u(Ye.$$.fragment),Gt=m(),I=o("p"),un=i("O \u{1F917} Transformers proporciona uma classe "),Ts=o("code"),fn=i("Trainer"),hn=i(` otimizada para o treinamento de modelos de \u{1F917} Transformers,
facilitando os primeiros passos do treinamento sem a necessidade de escrever manualmente o seu pr\xF3prio ciclo.
A API do `),Ps=o("code"),gn=i("Trainer"),_n=i(` suporta um grande conjunto de op\xE7\xF5es de treinamento e funcionalidades, como o logging,
o gradient accumulation e o mixed precision.`),Yt=m(),pe=o("p"),vn=i(`Comece carregando seu modelo e especifique o n\xFAmero de labels de previs\xE3o.
A partir do `),Qe=o("a"),$n=i("Card Dataset"),bn=i(` do Yelp Reveiw, que ja
sabemos ter 5 labels usamos o seguinte c\xF3digo:`),Qt=m(),u(Ve.$$.fragment),Vt=m(),u(me.$$.fragment),Jt=m(),U=o("h3"),ce=o("a"),zs=o("span"),u(Je.$$.fragment),jn=m(),Cs=o("span"),wn=i("Hiperpar\xE2metros de treinamento"),Xt=m(),L=o("p"),kn=i("Em seguida, crie uma classe "),Ds=o("code"),En=i("TrainingArguments"),yn=i(` que contenha todos os hiperpar\xE2metros que possam ser ajustados, assim
como os indicadores para ativar as diferentes op\xE7\xF5es de treinamento. Para este tutorial, voc\xEA pode come\xE7ar o treinamento
usando os `),Xe=o("a"),qn=i("hiperpar\xE1metros"),An=i(` padr\xE3o,
por\xE9m, sinta-se livre para experimentar com eles e encontrar uma configura\xE7\xE3o \xF3tima.`),Zt=m(),Za=o("p"),Tn=i("Especifique onde salvar os checkpoints do treinamento:"),eo=m(),u(Ze.$$.fragment),ao=m(),W=o("h3"),de=o("a"),xs=o("span"),u(ea.$$.fragment),Pn=m(),Ss=o("span"),zn=i("M\xE9tricas"),so=m(),q=o("p"),Cn=i("O "),Os=o("code"),Dn=i("Trainer"),xn=i(` n\xE3o avalia automaticamente o rendimento do modelo durante o treinamento. Ser\xE1 necess\xE1rio passar ao
`),Fs=o("code"),Sn=i("Trainer"),On=i(` uma fun\xE7\xE3o para calcular e fazer um diagn\xF3stico sobre as m\xE9tricas. A biblioteca \u{1F917} Datasets proporciona
uma fun\xE7\xE3o de `),aa=o("a"),Ns=o("code"),Fn=i("accuracy"),Nn=i(` simples que pode ser carregada com a fun\xE7\xE3o
`),Is=o("code"),In=i("load_metric"),Ln=i(" (ver este "),sa=o("a"),Mn=i("tutorial"),Hn=i(" para mais informa\xE7\xF5es):"),to=m(),u(ta.$$.fragment),oo=m(),D=o("p"),Rn=i("Defina a fun\xE7\xE3o "),Ls=o("code"),Bn=i("compute"),Kn=i(" dentro de "),Ms=o("code"),Un=i("metric"),Wn=i(` para calcular a precis\xE3o das suas predi\xE7\xF5es.
Antes de passar as suas predi\xE7\xF5es ao `),Hs=o("code"),Gn=i("compute"),Yn=i(`, \xE9 necess\xE1rio converter as predi\xE7\xF5es \xE0 logits (lembre-se que
todos os modelos de \u{1F917} Transformers retornam logits).`),ro=m(),u(oa.$$.fragment),no=m(),ue=o("p"),Qn=i("Se quiser controlar as suas m\xE9tricas de avalia\xE7\xE3o durante o fine-tuning, especifique o par\xE2metro "),Rs=o("code"),Vn=i("evaluation_strategy"),Jn=i(`
nos seus argumentos de treinamento para que o modelo considere a m\xE9trica de avalia\xE7\xE3o ao final de cada \xE9poca:`),lo=m(),u(ra.$$.fragment),io=m(),G=o("h3"),fe=o("a"),Bs=o("span"),u(na.$$.fragment),Xn=m(),Ks=o("span"),Zn=i("Trainer"),po=m(),he=o("p"),el=i("Crie um objeto "),Us=o("code"),al=i("Trainer"),sl=i(" com o seu modelo, argumentos de treinamento, conjuntos de dados de treinamento e de teste, e a sua fun\xE7\xE3o de avalia\xE7\xE3o:"),mo=m(),u(la.$$.fragment),co=m(),ge=o("p"),tl=i("Em seguida, aplique o fine-tuning a seu modelo chamado "),Ws=o("code"),ol=i("train()"),rl=i(":"),uo=m(),u(ia.$$.fragment),fo=m(),es=o("a"),ho=m(),Y=o("h2"),_e=o("a"),Gs=o("span"),u(pa.$$.fragment),nl=m(),Ys=o("span"),ll=i("Fine-tuning com Keras"),go=m(),u(ma.$$.fragment),_o=m(),as=o("p"),il=i(`Os modelos de \u{1F917} Transformers tamb\xE9m permitem realizar o treinamento com o TensorFlow com a API do Keras.
Contudo, ser\xE1 necess\xE1rio fazer algumas mudan\xE7as antes de realizar o fine-tuning.`),vo=m(),Q=o("h3"),ve=o("a"),Qs=o("span"),u(ca.$$.fragment),pl=m(),Vs=o("span"),ml=i("Convers\xE3o do dataset ao formato do TensorFlow"),$o=m(),M=o("p"),cl=i("O "),Js=o("code"),dl=i("DefaultDataCollator"),ul=i(` junta os tensores em um batch para que o modelo possa ser treinado em cima deles.
Assegure-se de especificar os `),Xs=o("code"),fl=i("return_tensors"),hl=i(" para retornar os tensores do TensorFlow:"),bo=m(),u(da.$$.fragment),jo=m(),u($e.$$.fragment),wo=m(),x=o("p"),gl=i(`Em seguida, converta os datasets tokenizados em datasets do TensorFlow com o m\xE9todo
`),ua=o("a"),Zs=o("code"),_l=i("to_tf_dataset"),vl=i(`.
Especifique suas entradas em `),et=o("code"),$l=i("columns"),bl=i(" e seu r\xF3tulo em "),at=o("code"),jl=i("label_cols"),wl=i(":"),ko=m(),u(fa.$$.fragment),Eo=m(),V=o("h3"),be=o("a"),st=o("span"),u(ha.$$.fragment),kl=m(),tt=o("span"),El=i("Compila\xE7\xE3o e ajustes"),yo=m(),ss=o("p"),yl=i("Carregue um modelo do TensorFlow com o n\xFAmero esperado de r\xF3tulos:"),qo=m(),u(ga.$$.fragment),Ao=m(),je=o("p"),ql=i("A seguir, compile e ajuste o fine-tuning a seu modelo com "),_a=o("a"),ot=o("code"),Al=i("fit"),Tl=i(` como
faria com qualquer outro modelo do Keras:`),To=m(),u(va.$$.fragment),Po=m(),ts=o("a"),zo=m(),J=o("h2"),we=o("a"),rt=o("span"),u($a.$$.fragment),Pl=m(),nt=o("span"),zl=i("Fine-tune em PyTorch nativo"),Co=m(),u(ba.$$.fragment),Do=m(),ke=o("p"),Cl=i("O "),lt=o("code"),Dl=i("Trainer"),xl=i(` se encarrega do ciclo de treinamento e permite aplicar o fine-tuning a um modelo em uma linha de c\xF3digo apenas.
Para os usu\xE1rios que preferirem escrever o seu pr\xF3prio ciclo de treinamento, tamb\xE9m \xE9 poss\xEDvel aplicar o fine-tuning a um
modelo de \u{1F917} Transformers em PyTorch nativo.`),xo=m(),os=o("p"),Sl=i(`Neste momento, talvez ocorra a necessidade de reinicar seu notebook ou executar a seguinte linha de c\xF3digo para liberar
mem\xF3ria:`),So=m(),u(ja.$$.fragment),Oo=m(),Ee=o("p"),Ol=i("Em sequ\xEAncia, faremos um post-processing manual do "),it=o("code"),Fl=i("tokenized_dataset"),Nl=i(" e assim prepar\xE1-lo para o treinamento."),Fo=m(),H=o("ol"),wa=o("li"),ka=o("p"),Il=i("Apague a coluna de "),pt=o("code"),Ll=i("text"),Ml=i(" porque o modelo n\xE3o aceita texto cru como entrada:"),Hl=m(),u(Ea.$$.fragment),Rl=m(),ya=o("li"),X=o("p"),Bl=i("Troque o nome da coluna "),mt=o("code"),Kl=i("label"),Ul=i(" para "),ct=o("code"),Wl=i("labels"),Gl=i(", pois o modelo espera um argumento de mesmo nome:"),Yl=m(),u(qa.$$.fragment),Ql=m(),Aa=o("li"),dt=o("p"),Vl=i("Defina o formato do dataset para retornar tensores do PyTorch no lugar de listas:"),Jl=m(),u(Ta.$$.fragment),No=m(),rs=o("p"),Xl=i("Em sequ\xEAncia, crie um subconjunto menor do dataset, como foi mostrado anteriormente, para aceler\xE1-lo o fine-tuning."),Io=m(),u(Pa.$$.fragment),Lo=m(),Z=o("h3"),ye=o("a"),ut=o("span"),u(za.$$.fragment),Zl=m(),ft=o("span"),ei=i("DataLoader"),Mo=m(),qe=o("p"),ai=i("Crie um "),ht=o("code"),si=i("DataLoader"),ti=i(" para os seus datasets de treinamento e de teste para poder iterar sobre batches de dados:"),Ho=m(),u(Ca.$$.fragment),Ro=m(),ns=o("p"),oi=i("Carregue seu modelo com o n\xFAmero de labels esperados:"),Bo=m(),u(Da.$$.fragment),Ko=m(),ee=o("h3"),Ae=o("a"),gt=o("span"),u(xa.$$.fragment),ri=m(),_t=o("span"),ni=i("Otimiza\xE7\xE3o e configura\xE7\xE3o do Learning Rate"),Uo=m(),Te=o("p"),li=i(`Crie um otimizador e um learning rate para aplicar o fine-tuning ao modelo.
Iremos utilizar o otimizador `),Sa=o("a"),vt=o("code"),ii=i("AdamW"),pi=i(" do PyTorch:"),Wo=m(),u(Oa.$$.fragment),Go=m(),Pe=o("p"),mi=i("Defina o learning rate do "),$t=o("code"),ci=i("Trainer"),di=i(":"),Yo=m(),u(Fa.$$.fragment),Qo=m(),ze=o("p"),ui=i("Por \xFAltimo, especifique o "),bt=o("code"),fi=i("device"),hi=i(` do ambiente para utilizar uma GPU se tiver acesso \xE0 alguma. Caso contr\xE1rio, o treinamento
em uma CPU pode acabar levando v\xE1rias horas em vez de minutos.`),Vo=m(),u(Na.$$.fragment),Jo=m(),u(Ce.$$.fragment),Xo=m(),ls=o("p"),gi=i("Perfeito, agora estamos prontos para come\xE7ar o treinamento! \u{1F973}"),Zo=m(),ae=o("h3"),De=o("a"),jt=o("span"),u(Ia.$$.fragment),_i=m(),wt=o("span"),vi=i("Ciclo de treinamento"),er=m(),xe=o("p"),$i=i("Para visualizar melhor o processo de treinamento, utilize a biblioteca "),La=o("a"),bi=i("tqdm"),ji=i(` para adicionar
uma barra de progresso sobre o n\xFAmero de passos percorridos no treinamento atual:`),ar=m(),u(Ma.$$.fragment),sr=m(),se=o("h3"),Se=o("a"),kt=o("span"),u(Ha.$$.fragment),wi=m(),Et=o("span"),ki=i("M\xE9tricas"),tr=m(),R=o("p"),Ei=i("Da mesma forma que \xE9 necess\xE1rio adicionar uma fun\xE7\xE3o de avalia\xE7\xE3o ao "),yt=o("code"),yi=i("Trainer"),qi=i(`, \xE9 necess\xE1rio fazer o mesmo quando
escrevendo o pr\xF3prio ciclo de treinamento. Contudo, em vez de calcular e retornar a m\xE9trica final de cada \xE9poca,
voc\xEA dever\xE1 adicionar todos os batches com `),Ra=o("a"),qt=o("code"),Ai=i("add_batch"),Ti=i(`
e calcular a m\xE9trica apenas no final.`),or=m(),u(Ba.$$.fragment),rr=m(),is=o("a"),nr=m(),te=o("h2"),Oe=o("a"),At=o("span"),u(Ka.$$.fragment),Pi=m(),Tt=o("span"),zi=i("Recursos adicionais"),lr=m(),ps=o("p"),Ci=i("Para mais exemplos de fine-tuning acesse:"),ir=m(),Fe=o("ul"),Pt=o("li"),ms=o("p"),Ua=o("a"),Di=i("\u{1F917} Transformers Examples"),xi=i(` inclui scripts
para treinas tarefas comuns de NLP em PyTorch e TensorFlow.`),Si=m(),zt=o("li"),cs=o("p"),ds=o("a"),Oi=i("\u{1F917} Transformers Notebooks"),Fi=i(` cont\xE9m v\xE1rios notebooks sobre como aplicar o fine-tuning a um modelo
para tarefas espec\xEDficas no PyTorch e TensorFlow.`),this.h()},l(e){const t=Mm('[data-svelte="svelte-1phssyn"]',document.head);$=r(t,"META",{name:!0,content:!0}),t.forEach(a),y=c(e),b=r(e,"H1",{class:!0});var Wa=n(b);k=r(Wa,"A",{id:!0,class:!0,href:!0});var Ct=n(k);P=r(Ct,"SPAN",{});var Dt=n(P);f(E.$$.fragment,Dt),Dt.forEach(a),Ct.forEach(a),O=c(Wa),z=r(Wa,"SPAN",{});var Hi=n(z);A=p(Hi,"Fine-tuning de um modelo pr\xE9-treinado"),Hi.forEach(a),Wa.forEach(a),w=c(e),f(C.$$.fragment,e),F=c(e),Ga=r(e,"P",{});var Ri=n(Ga);Ur=p(Ri,`O uso de um modelo pr\xE9-treinado tem importantes vantagens. Redu\xE7\xE3o do custo computacional, a pegada de carbono, e te
permite utilizar modelos de \xFAltima gera\xE7\xE3o sem ter que treinar um novo desde o in\xEDcio.
O \u{1F917} Transformers proporciona acesso a milhares de modelos pr\xE9-treinados numa ampla gama de tarefas.
Quando utilizar um modelo pr\xE9-treinado, treine-o com um dataset espec\xEDfico para a sua tarefa.
Isto \xE9 chamado de fine-tuning, uma t\xE9cnica de treinamento incrivelmente poderosa. Neste tutorial faremos o fine-tuning
de um modelo pr\xE9-treinado com um framework de Deep Learning da sua escolha:`),Ri.forEach(a),xt=c(e),N=r(e,"UL",{});var us=n(N);Le=r(us,"LI",{});var mr=n(Le);Wr=p(mr,"Fine-tuning de um modelo pr\xE9-treinado com o \u{1F917} Transformers "),bs=r(mr,"CODE",{});var Bi=n(bs);Gr=p(Bi,"Trainer"),Bi.forEach(a),Yr=p(mr,"."),mr.forEach(a),Qr=c(us),js=r(us,"LI",{});var Ki=n(js);Vr=p(Ki,"Fine-tuning de um modelo pr\xE9-treinado no TensorFlow com o Keras."),Ki.forEach(a),Jr=c(us),ws=r(us,"LI",{});var Ui=n(ws);Xr=p(Ui,"Fine-tuning de um modelo pr\xE9-treinado em PyTorch nativo."),Ui.forEach(a),us.forEach(a),St=c(e),Ya=r(e,"A",{id:!0}),n(Ya).forEach(a),Ot=c(e),B=r(e,"H2",{class:!0});var cr=n(B);re=r(cr,"A",{id:!0,class:!0,href:!0});var Wi=n(re);ks=r(Wi,"SPAN",{});var Gi=n(ks);f(Me.$$.fragment,Gi),Gi.forEach(a),Wi.forEach(a),Zr=c(cr),Es=r(cr,"SPAN",{});var Yi=n(Es);en=p(Yi,"Preparando um dataset"),Yi.forEach(a),cr.forEach(a),Ft=c(e),f(He.$$.fragment,e),Nt=c(e),Qa=r(e,"P",{});var Qi=n(Qa);an=p(Qi,`Antes de aplicar o fine-tuning a um modelo pr\xE9-treinado, baixe um dataset e prepare-o para o treinamento.
O tutorial anterior ensinar\xE1 a processar os dados para o treinamento, e ent\xE3o poder\xE1 ter a oportunidade de testar
esse novo conhecimento em algo pr\xE1tico.`),Qi.forEach(a),It=c(e),ne=r(e,"P",{});var dr=n(ne);sn=p(dr,"Comece carregando o dataset "),Re=r(dr,"A",{href:!0,rel:!0});var Vi=n(Re);tn=p(Vi,"Yelp Reviews"),Vi.forEach(a),on=p(dr,":"),dr.forEach(a),Lt=c(e),f(Be.$$.fragment,e),Mt=c(e),le=r(e,"P",{});var ur=n(le);rn=p(ur,`Como j\xE1 sabe, \xE9 necess\xE1rio ter um tokenizador para processar o texto e incluir uma estrat\xE9gia de padding e truncamento,
para manejar qualquer tamanho var\xEDavel de sequ\xEAncia. Para processar o seu dataset em apenas um passo, utilize o m\xE9todo de
\u{1F917} Datasets `),Ke=r(ur,"A",{href:!0,rel:!0});var Ji=n(Ke);ys=r(Ji,"CODE",{});var Xi=n(ys);nn=p(Xi,"map"),Xi.forEach(a),Ji.forEach(a),ln=p(ur,` para aplicar uma fun\xE7\xE3o de preprocessamento sobre
todo o dataset.`),ur.forEach(a),Ht=c(e),f(Ue.$$.fragment,e),Rt=c(e),Va=r(e,"P",{});var Zi=n(Va);pn=p(Zi,"Se desejar, \xE9 poss\xEDvel criar um subconjunto menor do dataset completo para aplicar o fine-tuning e assim reduzir o tempo necess\xE1rio."),Zi.forEach(a),Bt=c(e),f(We.$$.fragment,e),Kt=c(e),Ja=r(e,"A",{id:!0}),n(Ja).forEach(a),Ut=c(e),K=r(e,"H2",{class:!0});var fr=n(K);ie=r(fr,"A",{id:!0,class:!0,href:!0});var ep=n(ie);qs=r(ep,"SPAN",{});var ap=n(qs);f(Ge.$$.fragment,ap),ap.forEach(a),ep.forEach(a),mn=c(fr),Xa=r(fr,"SPAN",{});var Ni=n(Xa);cn=p(Ni,"Fine-tuning com o "),As=r(Ni,"CODE",{});var sp=n(As);dn=p(sp,"Trainer"),sp.forEach(a),Ni.forEach(a),fr.forEach(a),Wt=c(e),f(Ye.$$.fragment,e),Gt=c(e),I=r(e,"P",{});var fs=n(I);un=p(fs,"O \u{1F917} Transformers proporciona uma classe "),Ts=r(fs,"CODE",{});var tp=n(Ts);fn=p(tp,"Trainer"),tp.forEach(a),hn=p(fs,` otimizada para o treinamento de modelos de \u{1F917} Transformers,
facilitando os primeiros passos do treinamento sem a necessidade de escrever manualmente o seu pr\xF3prio ciclo.
A API do `),Ps=r(fs,"CODE",{});var op=n(Ps);gn=p(op,"Trainer"),op.forEach(a),_n=p(fs,` suporta um grande conjunto de op\xE7\xF5es de treinamento e funcionalidades, como o logging,
o gradient accumulation e o mixed precision.`),fs.forEach(a),Yt=c(e),pe=r(e,"P",{});var hr=n(pe);vn=p(hr,`Comece carregando seu modelo e especifique o n\xFAmero de labels de previs\xE3o.
A partir do `),Qe=r(hr,"A",{href:!0,rel:!0});var rp=n(Qe);$n=p(rp,"Card Dataset"),rp.forEach(a),bn=p(hr,` do Yelp Reveiw, que ja
sabemos ter 5 labels usamos o seguinte c\xF3digo:`),hr.forEach(a),Qt=c(e),f(Ve.$$.fragment,e),Vt=c(e),f(me.$$.fragment,e),Jt=c(e),U=r(e,"H3",{class:!0});var gr=n(U);ce=r(gr,"A",{id:!0,class:!0,href:!0});var np=n(ce);zs=r(np,"SPAN",{});var lp=n(zs);f(Je.$$.fragment,lp),lp.forEach(a),np.forEach(a),jn=c(gr),Cs=r(gr,"SPAN",{});var ip=n(Cs);wn=p(ip,"Hiperpar\xE2metros de treinamento"),ip.forEach(a),gr.forEach(a),Xt=c(e),L=r(e,"P",{});var hs=n(L);kn=p(hs,"Em seguida, crie uma classe "),Ds=r(hs,"CODE",{});var pp=n(Ds);En=p(pp,"TrainingArguments"),pp.forEach(a),yn=p(hs,` que contenha todos os hiperpar\xE2metros que possam ser ajustados, assim
como os indicadores para ativar as diferentes op\xE7\xF5es de treinamento. Para este tutorial, voc\xEA pode come\xE7ar o treinamento
usando os `),Xe=r(hs,"A",{href:!0,rel:!0});var mp=n(Xe);qn=p(mp,"hiperpar\xE1metros"),mp.forEach(a),An=p(hs,` padr\xE3o,
por\xE9m, sinta-se livre para experimentar com eles e encontrar uma configura\xE7\xE3o \xF3tima.`),hs.forEach(a),Zt=c(e),Za=r(e,"P",{});var cp=n(Za);Tn=p(cp,"Especifique onde salvar os checkpoints do treinamento:"),cp.forEach(a),eo=c(e),f(Ze.$$.fragment,e),ao=c(e),W=r(e,"H3",{class:!0});var _r=n(W);de=r(_r,"A",{id:!0,class:!0,href:!0});var dp=n(de);xs=r(dp,"SPAN",{});var up=n(xs);f(ea.$$.fragment,up),up.forEach(a),dp.forEach(a),Pn=c(_r),Ss=r(_r,"SPAN",{});var fp=n(Ss);zn=p(fp,"M\xE9tricas"),fp.forEach(a),_r.forEach(a),so=c(e),q=r(e,"P",{});var S=n(q);Cn=p(S,"O "),Os=r(S,"CODE",{});var hp=n(Os);Dn=p(hp,"Trainer"),hp.forEach(a),xn=p(S,` n\xE3o avalia automaticamente o rendimento do modelo durante o treinamento. Ser\xE1 necess\xE1rio passar ao
`),Fs=r(S,"CODE",{});var gp=n(Fs);Sn=p(gp,"Trainer"),gp.forEach(a),On=p(S,` uma fun\xE7\xE3o para calcular e fazer um diagn\xF3stico sobre as m\xE9tricas. A biblioteca \u{1F917} Datasets proporciona
uma fun\xE7\xE3o de `),aa=r(S,"A",{href:!0,rel:!0});var _p=n(aa);Ns=r(_p,"CODE",{});var vp=n(Ns);Fn=p(vp,"accuracy"),vp.forEach(a),_p.forEach(a),Nn=p(S,` simples que pode ser carregada com a fun\xE7\xE3o
`),Is=r(S,"CODE",{});var $p=n(Is);In=p($p,"load_metric"),$p.forEach(a),Ln=p(S," (ver este "),sa=r(S,"A",{href:!0,rel:!0});var bp=n(sa);Mn=p(bp,"tutorial"),bp.forEach(a),Hn=p(S," para mais informa\xE7\xF5es):"),S.forEach(a),to=c(e),f(ta.$$.fragment,e),oo=c(e),D=r(e,"P",{});var Ne=n(D);Rn=p(Ne,"Defina a fun\xE7\xE3o "),Ls=r(Ne,"CODE",{});var jp=n(Ls);Bn=p(jp,"compute"),jp.forEach(a),Kn=p(Ne," dentro de "),Ms=r(Ne,"CODE",{});var wp=n(Ms);Un=p(wp,"metric"),wp.forEach(a),Wn=p(Ne,` para calcular a precis\xE3o das suas predi\xE7\xF5es.
Antes de passar as suas predi\xE7\xF5es ao `),Hs=r(Ne,"CODE",{});var kp=n(Hs);Gn=p(kp,"compute"),kp.forEach(a),Yn=p(Ne,`, \xE9 necess\xE1rio converter as predi\xE7\xF5es \xE0 logits (lembre-se que
todos os modelos de \u{1F917} Transformers retornam logits).`),Ne.forEach(a),ro=c(e),f(oa.$$.fragment,e),no=c(e),ue=r(e,"P",{});var vr=n(ue);Qn=p(vr,"Se quiser controlar as suas m\xE9tricas de avalia\xE7\xE3o durante o fine-tuning, especifique o par\xE2metro "),Rs=r(vr,"CODE",{});var Ep=n(Rs);Vn=p(Ep,"evaluation_strategy"),Ep.forEach(a),Jn=p(vr,`
nos seus argumentos de treinamento para que o modelo considere a m\xE9trica de avalia\xE7\xE3o ao final de cada \xE9poca:`),vr.forEach(a),lo=c(e),f(ra.$$.fragment,e),io=c(e),G=r(e,"H3",{class:!0});var $r=n(G);fe=r($r,"A",{id:!0,class:!0,href:!0});var yp=n(fe);Bs=r(yp,"SPAN",{});var qp=n(Bs);f(na.$$.fragment,qp),qp.forEach(a),yp.forEach(a),Xn=c($r),Ks=r($r,"SPAN",{});var Ap=n(Ks);Zn=p(Ap,"Trainer"),Ap.forEach(a),$r.forEach(a),po=c(e),he=r(e,"P",{});var br=n(he);el=p(br,"Crie um objeto "),Us=r(br,"CODE",{});var Tp=n(Us);al=p(Tp,"Trainer"),Tp.forEach(a),sl=p(br," com o seu modelo, argumentos de treinamento, conjuntos de dados de treinamento e de teste, e a sua fun\xE7\xE3o de avalia\xE7\xE3o:"),br.forEach(a),mo=c(e),f(la.$$.fragment,e),co=c(e),ge=r(e,"P",{});var jr=n(ge);tl=p(jr,"Em seguida, aplique o fine-tuning a seu modelo chamado "),Ws=r(jr,"CODE",{});var Pp=n(Ws);ol=p(Pp,"train()"),Pp.forEach(a),rl=p(jr,":"),jr.forEach(a),uo=c(e),f(ia.$$.fragment,e),fo=c(e),es=r(e,"A",{id:!0}),n(es).forEach(a),ho=c(e),Y=r(e,"H2",{class:!0});var wr=n(Y);_e=r(wr,"A",{id:!0,class:!0,href:!0});var zp=n(_e);Gs=r(zp,"SPAN",{});var Cp=n(Gs);f(pa.$$.fragment,Cp),Cp.forEach(a),zp.forEach(a),nl=c(wr),Ys=r(wr,"SPAN",{});var Dp=n(Ys);ll=p(Dp,"Fine-tuning com Keras"),Dp.forEach(a),wr.forEach(a),go=c(e),f(ma.$$.fragment,e),_o=c(e),as=r(e,"P",{});var xp=n(as);il=p(xp,`Os modelos de \u{1F917} Transformers tamb\xE9m permitem realizar o treinamento com o TensorFlow com a API do Keras.
Contudo, ser\xE1 necess\xE1rio fazer algumas mudan\xE7as antes de realizar o fine-tuning.`),xp.forEach(a),vo=c(e),Q=r(e,"H3",{class:!0});var kr=n(Q);ve=r(kr,"A",{id:!0,class:!0,href:!0});var Sp=n(ve);Qs=r(Sp,"SPAN",{});var Op=n(Qs);f(ca.$$.fragment,Op),Op.forEach(a),Sp.forEach(a),pl=c(kr),Vs=r(kr,"SPAN",{});var Fp=n(Vs);ml=p(Fp,"Convers\xE3o do dataset ao formato do TensorFlow"),Fp.forEach(a),kr.forEach(a),$o=c(e),M=r(e,"P",{});var gs=n(M);cl=p(gs,"O "),Js=r(gs,"CODE",{});var Np=n(Js);dl=p(Np,"DefaultDataCollator"),Np.forEach(a),ul=p(gs,` junta os tensores em um batch para que o modelo possa ser treinado em cima deles.
Assegure-se de especificar os `),Xs=r(gs,"CODE",{});var Ip=n(Xs);fl=p(Ip,"return_tensors"),Ip.forEach(a),hl=p(gs," para retornar os tensores do TensorFlow:"),gs.forEach(a),bo=c(e),f(da.$$.fragment,e),jo=c(e),f($e.$$.fragment,e),wo=c(e),x=r(e,"P",{});var Ie=n(x);gl=p(Ie,`Em seguida, converta os datasets tokenizados em datasets do TensorFlow com o m\xE9todo
`),ua=r(Ie,"A",{href:!0,rel:!0});var Lp=n(ua);Zs=r(Lp,"CODE",{});var Mp=n(Zs);_l=p(Mp,"to_tf_dataset"),Mp.forEach(a),Lp.forEach(a),vl=p(Ie,`.
Especifique suas entradas em `),et=r(Ie,"CODE",{});var Hp=n(et);$l=p(Hp,"columns"),Hp.forEach(a),bl=p(Ie," e seu r\xF3tulo em "),at=r(Ie,"CODE",{});var Rp=n(at);jl=p(Rp,"label_cols"),Rp.forEach(a),wl=p(Ie,":"),Ie.forEach(a),ko=c(e),f(fa.$$.fragment,e),Eo=c(e),V=r(e,"H3",{class:!0});var Er=n(V);be=r(Er,"A",{id:!0,class:!0,href:!0});var Bp=n(be);st=r(Bp,"SPAN",{});var Kp=n(st);f(ha.$$.fragment,Kp),Kp.forEach(a),Bp.forEach(a),kl=c(Er),tt=r(Er,"SPAN",{});var Up=n(tt);El=p(Up,"Compila\xE7\xE3o e ajustes"),Up.forEach(a),Er.forEach(a),yo=c(e),ss=r(e,"P",{});var Wp=n(ss);yl=p(Wp,"Carregue um modelo do TensorFlow com o n\xFAmero esperado de r\xF3tulos:"),Wp.forEach(a),qo=c(e),f(ga.$$.fragment,e),Ao=c(e),je=r(e,"P",{});var yr=n(je);ql=p(yr,"A seguir, compile e ajuste o fine-tuning a seu modelo com "),_a=r(yr,"A",{href:!0,rel:!0});var Gp=n(_a);ot=r(Gp,"CODE",{});var Yp=n(ot);Al=p(Yp,"fit"),Yp.forEach(a),Gp.forEach(a),Tl=p(yr,` como
faria com qualquer outro modelo do Keras:`),yr.forEach(a),To=c(e),f(va.$$.fragment,e),Po=c(e),ts=r(e,"A",{id:!0}),n(ts).forEach(a),zo=c(e),J=r(e,"H2",{class:!0});var qr=n(J);we=r(qr,"A",{id:!0,class:!0,href:!0});var Qp=n(we);rt=r(Qp,"SPAN",{});var Vp=n(rt);f($a.$$.fragment,Vp),Vp.forEach(a),Qp.forEach(a),Pl=c(qr),nt=r(qr,"SPAN",{});var Jp=n(nt);zl=p(Jp,"Fine-tune em PyTorch nativo"),Jp.forEach(a),qr.forEach(a),Co=c(e),f(ba.$$.fragment,e),Do=c(e),ke=r(e,"P",{});var Ar=n(ke);Cl=p(Ar,"O "),lt=r(Ar,"CODE",{});var Xp=n(lt);Dl=p(Xp,"Trainer"),Xp.forEach(a),xl=p(Ar,` se encarrega do ciclo de treinamento e permite aplicar o fine-tuning a um modelo em uma linha de c\xF3digo apenas.
Para os usu\xE1rios que preferirem escrever o seu pr\xF3prio ciclo de treinamento, tamb\xE9m \xE9 poss\xEDvel aplicar o fine-tuning a um
modelo de \u{1F917} Transformers em PyTorch nativo.`),Ar.forEach(a),xo=c(e),os=r(e,"P",{});var Zp=n(os);Sl=p(Zp,`Neste momento, talvez ocorra a necessidade de reinicar seu notebook ou executar a seguinte linha de c\xF3digo para liberar
mem\xF3ria:`),Zp.forEach(a),So=c(e),f(ja.$$.fragment,e),Oo=c(e),Ee=r(e,"P",{});var Tr=n(Ee);Ol=p(Tr,"Em sequ\xEAncia, faremos um post-processing manual do "),it=r(Tr,"CODE",{});var em=n(it);Fl=p(em,"tokenized_dataset"),em.forEach(a),Nl=p(Tr," e assim prepar\xE1-lo para o treinamento."),Tr.forEach(a),Fo=c(e),H=r(e,"OL",{});var _s=n(H);wa=r(_s,"LI",{});var Pr=n(wa);ka=r(Pr,"P",{});var zr=n(ka);Il=p(zr,"Apague a coluna de "),pt=r(zr,"CODE",{});var am=n(pt);Ll=p(am,"text"),am.forEach(a),Ml=p(zr," porque o modelo n\xE3o aceita texto cru como entrada:"),zr.forEach(a),Hl=c(Pr),f(Ea.$$.fragment,Pr),Pr.forEach(a),Rl=c(_s),ya=r(_s,"LI",{});var Cr=n(ya);X=r(Cr,"P",{});var vs=n(X);Bl=p(vs,"Troque o nome da coluna "),mt=r(vs,"CODE",{});var sm=n(mt);Kl=p(sm,"label"),sm.forEach(a),Ul=p(vs," para "),ct=r(vs,"CODE",{});var tm=n(ct);Wl=p(tm,"labels"),tm.forEach(a),Gl=p(vs,", pois o modelo espera um argumento de mesmo nome:"),vs.forEach(a),Yl=c(Cr),f(qa.$$.fragment,Cr),Cr.forEach(a),Ql=c(_s),Aa=r(_s,"LI",{});var Dr=n(Aa);dt=r(Dr,"P",{});var om=n(dt);Vl=p(om,"Defina o formato do dataset para retornar tensores do PyTorch no lugar de listas:"),om.forEach(a),Jl=c(Dr),f(Ta.$$.fragment,Dr),Dr.forEach(a),_s.forEach(a),No=c(e),rs=r(e,"P",{});var rm=n(rs);Xl=p(rm,"Em sequ\xEAncia, crie um subconjunto menor do dataset, como foi mostrado anteriormente, para aceler\xE1-lo o fine-tuning."),rm.forEach(a),Io=c(e),f(Pa.$$.fragment,e),Lo=c(e),Z=r(e,"H3",{class:!0});var xr=n(Z);ye=r(xr,"A",{id:!0,class:!0,href:!0});var nm=n(ye);ut=r(nm,"SPAN",{});var lm=n(ut);f(za.$$.fragment,lm),lm.forEach(a),nm.forEach(a),Zl=c(xr),ft=r(xr,"SPAN",{});var im=n(ft);ei=p(im,"DataLoader"),im.forEach(a),xr.forEach(a),Mo=c(e),qe=r(e,"P",{});var Sr=n(qe);ai=p(Sr,"Crie um "),ht=r(Sr,"CODE",{});var pm=n(ht);si=p(pm,"DataLoader"),pm.forEach(a),ti=p(Sr," para os seus datasets de treinamento e de teste para poder iterar sobre batches de dados:"),Sr.forEach(a),Ho=c(e),f(Ca.$$.fragment,e),Ro=c(e),ns=r(e,"P",{});var mm=n(ns);oi=p(mm,"Carregue seu modelo com o n\xFAmero de labels esperados:"),mm.forEach(a),Bo=c(e),f(Da.$$.fragment,e),Ko=c(e),ee=r(e,"H3",{class:!0});var Or=n(ee);Ae=r(Or,"A",{id:!0,class:!0,href:!0});var cm=n(Ae);gt=r(cm,"SPAN",{});var dm=n(gt);f(xa.$$.fragment,dm),dm.forEach(a),cm.forEach(a),ri=c(Or),_t=r(Or,"SPAN",{});var um=n(_t);ni=p(um,"Otimiza\xE7\xE3o e configura\xE7\xE3o do Learning Rate"),um.forEach(a),Or.forEach(a),Uo=c(e),Te=r(e,"P",{});var Fr=n(Te);li=p(Fr,`Crie um otimizador e um learning rate para aplicar o fine-tuning ao modelo.
Iremos utilizar o otimizador `),Sa=r(Fr,"A",{href:!0,rel:!0});var fm=n(Sa);vt=r(fm,"CODE",{});var hm=n(vt);ii=p(hm,"AdamW"),hm.forEach(a),fm.forEach(a),pi=p(Fr," do PyTorch:"),Fr.forEach(a),Wo=c(e),f(Oa.$$.fragment,e),Go=c(e),Pe=r(e,"P",{});var Nr=n(Pe);mi=p(Nr,"Defina o learning rate do "),$t=r(Nr,"CODE",{});var gm=n($t);ci=p(gm,"Trainer"),gm.forEach(a),di=p(Nr,":"),Nr.forEach(a),Yo=c(e),f(Fa.$$.fragment,e),Qo=c(e),ze=r(e,"P",{});var Ir=n(ze);ui=p(Ir,"Por \xFAltimo, especifique o "),bt=r(Ir,"CODE",{});var _m=n(bt);fi=p(_m,"device"),_m.forEach(a),hi=p(Ir,` do ambiente para utilizar uma GPU se tiver acesso \xE0 alguma. Caso contr\xE1rio, o treinamento
em uma CPU pode acabar levando v\xE1rias horas em vez de minutos.`),Ir.forEach(a),Vo=c(e),f(Na.$$.fragment,e),Jo=c(e),f(Ce.$$.fragment,e),Xo=c(e),ls=r(e,"P",{});var vm=n(ls);gi=p(vm,"Perfeito, agora estamos prontos para come\xE7ar o treinamento! \u{1F973}"),vm.forEach(a),Zo=c(e),ae=r(e,"H3",{class:!0});var Lr=n(ae);De=r(Lr,"A",{id:!0,class:!0,href:!0});var $m=n(De);jt=r($m,"SPAN",{});var bm=n(jt);f(Ia.$$.fragment,bm),bm.forEach(a),$m.forEach(a),_i=c(Lr),wt=r(Lr,"SPAN",{});var jm=n(wt);vi=p(jm,"Ciclo de treinamento"),jm.forEach(a),Lr.forEach(a),er=c(e),xe=r(e,"P",{});var Mr=n(xe);$i=p(Mr,"Para visualizar melhor o processo de treinamento, utilize a biblioteca "),La=r(Mr,"A",{href:!0,rel:!0});var wm=n(La);bi=p(wm,"tqdm"),wm.forEach(a),ji=p(Mr,` para adicionar
uma barra de progresso sobre o n\xFAmero de passos percorridos no treinamento atual:`),Mr.forEach(a),ar=c(e),f(Ma.$$.fragment,e),sr=c(e),se=r(e,"H3",{class:!0});var Hr=n(se);Se=r(Hr,"A",{id:!0,class:!0,href:!0});var km=n(Se);kt=r(km,"SPAN",{});var Em=n(kt);f(Ha.$$.fragment,Em),Em.forEach(a),km.forEach(a),wi=c(Hr),Et=r(Hr,"SPAN",{});var ym=n(Et);ki=p(ym,"M\xE9tricas"),ym.forEach(a),Hr.forEach(a),tr=c(e),R=r(e,"P",{});var $s=n(R);Ei=p($s,"Da mesma forma que \xE9 necess\xE1rio adicionar uma fun\xE7\xE3o de avalia\xE7\xE3o ao "),yt=r($s,"CODE",{});var qm=n(yt);yi=p(qm,"Trainer"),qm.forEach(a),qi=p($s,`, \xE9 necess\xE1rio fazer o mesmo quando
escrevendo o pr\xF3prio ciclo de treinamento. Contudo, em vez de calcular e retornar a m\xE9trica final de cada \xE9poca,
voc\xEA dever\xE1 adicionar todos os batches com `),Ra=r($s,"A",{href:!0,rel:!0});var Am=n(Ra);qt=r(Am,"CODE",{});var Tm=n(qt);Ai=p(Tm,"add_batch"),Tm.forEach(a),Am.forEach(a),Ti=p($s,`
e calcular a m\xE9trica apenas no final.`),$s.forEach(a),or=c(e),f(Ba.$$.fragment,e),rr=c(e),is=r(e,"A",{id:!0}),n(is).forEach(a),nr=c(e),te=r(e,"H2",{class:!0});var Rr=n(te);Oe=r(Rr,"A",{id:!0,class:!0,href:!0});var Pm=n(Oe);At=r(Pm,"SPAN",{});var zm=n(At);f(Ka.$$.fragment,zm),zm.forEach(a),Pm.forEach(a),Pi=c(Rr),Tt=r(Rr,"SPAN",{});var Cm=n(Tt);zi=p(Cm,"Recursos adicionais"),Cm.forEach(a),Rr.forEach(a),lr=c(e),ps=r(e,"P",{});var Dm=n(ps);Ci=p(Dm,"Para mais exemplos de fine-tuning acesse:"),Dm.forEach(a),ir=c(e),Fe=r(e,"UL",{});var Br=n(Fe);Pt=r(Br,"LI",{});var xm=n(Pt);ms=r(xm,"P",{});var Ii=n(ms);Ua=r(Ii,"A",{href:!0,rel:!0});var Sm=n(Ua);Di=p(Sm,"\u{1F917} Transformers Examples"),Sm.forEach(a),xi=p(Ii,` inclui scripts
para treinas tarefas comuns de NLP em PyTorch e TensorFlow.`),Ii.forEach(a),xm.forEach(a),Si=c(Br),zt=r(Br,"LI",{});var Om=n(zt);cs=r(Om,"P",{});var Li=n(cs);ds=r(Li,"A",{href:!0});var Fm=n(ds);Oi=p(Fm,"\u{1F917} Transformers Notebooks"),Fm.forEach(a),Fi=p(Li,` cont\xE9m v\xE1rios notebooks sobre como aplicar o fine-tuning a um modelo
para tarefas espec\xEDficas no PyTorch e TensorFlow.`),Li.forEach(a),Om.forEach(a),Br.forEach(a),this.h()},h(){d($,"name","hf:doc:metadata"),d($,"content",JSON.stringify(Gm)),d(k,"id","finetuning-de-um-modelo-prtreinado"),d(k,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),d(k,"href","#finetuning-de-um-modelo-prtreinado"),d(b,"class","relative group"),d(Ya,"id","data-processing"),d(re,"id","preparando-um-dataset"),d(re,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),d(re,"href","#preparando-um-dataset"),d(B,"class","relative 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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/multilingual.mdx-hf-doc-builder.js | import{S as Yr,i as Zr,s as en,e as t,k as d,w as u,t as i,M as on,c as s,d as a,m as p,a as l,x as f,h as r,b as c,G as o,g as m,y as g,L as an,q as h,o as _,B as v,v as tn}from"../chunks/vendor-hf-doc-builder.js";import{I as re}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as z}from"../chunks/CodeBlock-hf-doc-builder.js";import{D as sn}from"../chunks/DocNotebookDropdown-hf-doc-builder.js";function ln(wi){let L,fa,O,S,po,ne,Lt,co,Ot,ga,me,ha,y,Ct,uo,Tt,Pt,de,Dt,Xt,_a,C,I,fo,pe,At,go,Nt,va,Le,St,ba,T,B,ho,ce,It,_o,Bt,Ea,Oe,Rt,ka,b,Ce,vo,Ft,Ht,Gt,Te,bo,Wt,Ut,Jt,Pe,Eo,Kt,Qt,Vt,De,ko,Yt,Zt,es,Xe,$o,os,as,ts,Ae,Mo,ss,ls,is,Ne,xo,rs,ns,$a,$,ms,wo,ds,ps,zo,cs,us,yo,fs,gs,Ma,R,hs,qo,_s,vs,xa,ue,wa,F,bs,jo,Es,ks,za,fe,ya,Se,$s,qa,ge,ja,M,Ms,Lo,xs,ws,Oo,zs,ys,Co,qs,js,La,he,Oa,H,Ls,To,Os,Cs,Ca,_e,Ta,q,Ts,ve,Ps,Ds,Po,Xs,As,Pa,P,G,Do,be,Ns,Xo,Ss,Da,Ie,Is,Xa,W,Be,Ao,Bs,Rs,Fs,Re,No,Hs,Gs,Aa,Fe,Ws,Na,D,U,So,Ee,Us,Io,Js,Sa,He,Ks,Ia,J,Ge,Bo,Qs,Vs,Ys,We,Ro,Zs,el,Ba,Ue,ol,Ra,X,K,Fo,ke,al,Ho,tl,Fa,Je,sl,Ha,Q,Ke,Go,ll,il,rl,Qe,Wo,nl,ml,Ga,Ve,dl,Wa,A,V,Uo,$e,pl,Jo,cl,Ua,Ye,ul,Ja,Y,Ze,Ko,fl,gl,hl,eo,Qo,_l,vl,Ka,Z,bl,Vo,El,kl,Qa,Me,Va,oo,$l,Ya,xe,Za,x,Ml,Yo,xl,wl,Zo,zl,yl,ea,ql,jl,et,we,ot,N,ee,oa,ze,Ll,aa,Ol,at,ao,Cl,tt,E,to,ta,Tl,Pl,Dl,so,sa,Xl,Al,Nl,lo,la,Sl,Il,Bl,io,ia,Rl,Fl,Hl,ra,na,Gl,st,oe,Wl,ma,Ul,Jl,lt,ye,it,ro,Kl,rt,qe,nt,w,Ql,da,Vl,Yl,pa,Zl,ei,ca,oi,ai,mt,je,dt,ae,ti,ua,si,li,pt;return ne=new re({}),me=new sn({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/multilingual.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/multilingual.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/multilingual.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/multilingual.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/multilingual.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/multilingual.ipynb"}]}}),pe=new re({}),ce=new re({}),ue=new z({props:{code:`import torch
from transformers import XLMTokenizer, XLMWithLMHeadModel
tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> XLMTokenizer, XLMWithLMHeadModel
<span class="hljs-meta">>>> </span>tokenizer = XLMTokenizer.from_pretrained(<span class="hljs-string">"xlm-clm-enfr-1024"</span>)
<span class="hljs-meta">>>> </span>model = XLMWithLMHeadModel.from_pretrained(<span class="hljs-string">"xlm-clm-enfr-1024"</span>)`}}),fe=new z({props:{code:"print(tokenizer.lang2id)",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(tokenizer.lang2id)
{<span class="hljs-string">'en'</span>: <span class="hljs-number">0</span>, <span class="hljs-string">'fr'</span>: <span class="hljs-number">1</span>}`}}),ge=new z({props:{code:'input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1',highlighted:'<span class="hljs-meta">>>> </span>input_ids = torch.tensor([tokenizer.encode(<span class="hljs-string">"Wikipedia was used to"</span>)]) <span class="hljs-comment"># batch size of 1</span>'}}),he=new z({props:{code:`language_id = tokenizer.lang2id["en"] # 0
langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0])
# We reshape it to be of size (batch_size, sequence_length)
langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)`,highlighted:`<span class="hljs-meta">>>> </span>language_id = tokenizer.lang2id[<span class="hljs-string">"en"</span>] <span class="hljs-comment"># 0</span>
<span class="hljs-meta">>>> </span>langs = torch.tensor([language_id] * input_ids.shape[<span class="hljs-number">1</span>]) <span class="hljs-comment"># torch.tensor([0, 0, 0, ..., 0])</span>
<span class="hljs-meta">>>> </span><span class="hljs-comment"># We reshape it to be of size (batch_size, sequence_length)</span>
<span class="hljs-meta">>>> </span>langs = langs.view(<span class="hljs-number">1</span>, -<span class="hljs-number">1</span>) <span class="hljs-comment"># is now of shape [1, sequence_length] (we have a batch size of 1)</span>`}}),_e=new z({props:{code:"outputs = model(input_ids, langs=langs)",highlighted:'<span class="hljs-meta">>>> </span>outputs = model(input_ids, langs=langs)'}}),be=new re({}),Ee=new re({}),ke=new re({}),$e=new re({}),Me=new z({props:{code:`from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
chinese_text = "\u4E0D\u8981\u63D2\u624B\u5DEB\u5E2B\u7684\u4E8B\u52D9, \u56E0\u70BA\u4ED6\u5011\u662F\u5FAE\u5999\u7684, \u5F88\u5FEB\u5C31\u6703\u767C\u6012."
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh")
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> M2M100ForConditionalGeneration, M2M100Tokenizer
<span class="hljs-meta">>>> </span>en_text = <span class="hljs-string">"Do not meddle in the affairs of wizards, for they are subtle and quick to anger."</span>
<span class="hljs-meta">>>> </span>chinese_text = <span class="hljs-string">"\u4E0D\u8981\u63D2\u624B\u5DEB\u5E2B\u7684\u4E8B\u52D9, \u56E0\u70BA\u4ED6\u5011\u662F\u5FAE\u5999\u7684, \u5F88\u5FEB\u5C31\u6703\u767C\u6012."</span>
<span class="hljs-meta">>>> </span>tokenizer = M2M100Tokenizer.from_pretrained(<span class="hljs-string">"facebook/m2m100_418M"</span>, src_lang=<span class="hljs-string">"zh"</span>)
<span class="hljs-meta">>>> </span>model = M2M100ForConditionalGeneration.from_pretrained(<span class="hljs-string">"facebook/m2m100_418M"</span>)`}}),xe=new z({props:{code:'encoded_zh = tokenizer(chinese_text, return_tensors="pt")',highlighted:'<span class="hljs-meta">>>> </span>encoded_zh = tokenizer(chinese_text, return_tensors=<span class="hljs-string">"pt"</span>)'}}),we=new z({props:{code:`generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span>generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id(<span class="hljs-string">"en"</span>))
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(generated_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.'</span>`}}),ze=new re({}),ye=new z({props:{code:`from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
fi_text = "\xC4l\xE4 sekaannu velhojen asioihin, sill\xE4 ne ovat hienovaraisia ja nopeasti vihaisia."
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>en_text = <span class="hljs-string">"Do not meddle in the affairs of wizards, for they are subtle and quick to anger."</span>
<span class="hljs-meta">>>> </span>fi_text = <span class="hljs-string">"\xC4l\xE4 sekaannu velhojen asioihin, sill\xE4 ne ovat hienovaraisia ja nopeasti vihaisia."</span>
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"facebook/mbart-large-50-many-to-many-mmt"</span>, src_lang=<span class="hljs-string">"fi_FI"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"facebook/mbart-large-50-many-to-many-mmt"</span>)`}}),qe=new z({props:{code:'encoded_en = tokenizer(en_text, return_tensors="pt")',highlighted:'<span class="hljs-meta">>>> </span>encoded_en = tokenizer(en_text, return_tensors=<span class="hljs-string">"pt"</span>)'}}),je=new z({props:{code:`generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id("en_XX"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)`,highlighted:`<span class="hljs-meta">>>> </span>generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id(<span class="hljs-string">"en_XX"</span>))
<span class="hljs-meta">>>> </span>tokenizer.batch_decode(generated_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">"Don't interfere with the wizard's affairs, because they are subtle, will soon get angry."</span>`}}),{c(){L=t("meta"),fa=d(),O=t("h1"),S=t("a"),po=t("span"),u(ne.$$.fragment),Lt=d(),co=t("span"),Ot=i("Modelos multilingu\xEDsticos para infer\xEAncia"),ga=d(),u(me.$$.fragment),ha=d(),y=t("p"),Ct=i(`Existem v\xE1rios modelos multilingu\xEDsticos no \u{1F917} Transformers e seus usos para infer\xEAncia diferem dos modelos monol\xEDngues.
No entanto, nem `),uo=t("em"),Tt=i("todos"),Pt=i(` os usos dos modelos multil\xEDngues s\xE3o t\xE3o diferentes.
Alguns modelos, como o `),de=t("a"),Dt=i("bert-base-multilingual-uncased"),Xt=i(`,
podem ser usados como se fossem monol\xEDngues. Este guia ir\xE1 te ajudar a usar modelos multil\xEDngues cujo uso difere
para o prop\xF3sito de infer\xEAncia.`),_a=d(),C=t("h2"),I=t("a"),fo=t("span"),u(pe.$$.fragment),At=d(),go=t("span"),Nt=i("XLM"),va=d(),Le=t("p"),St=i(`O XLM tem dez checkpoints diferentes dos quais apenas um \xE9 monol\xEDngue.
Os nove checkpoints restantes do modelo s\xE3o subdivididos em duas categorias:
checkpoints que usam de language embeddings e os que n\xE3o.`),ba=d(),T=t("h3"),B=t("a"),ho=t("span"),u(ce.$$.fragment),It=d(),_o=t("span"),Bt=i("XLM com language embeddings"),Ea=d(),Oe=t("p"),Rt=i("Os seguintes modelos de XLM usam language embeddings para especificar a linguagem utilizada para a infer\xEAncia."),ka=d(),b=t("ul"),Ce=t("li"),vo=t("code"),Ft=i("xlm-mlm-ende-1024"),Ht=i(" (Masked language modeling, English-German)"),Gt=d(),Te=t("li"),bo=t("code"),Wt=i("xlm-mlm-enfr-1024"),Ut=i(" (Masked language modeling, English-French)"),Jt=d(),Pe=t("li"),Eo=t("code"),Kt=i("xlm-mlm-enro-1024"),Qt=i(" (Masked language modeling, English-Romanian)"),Vt=d(),De=t("li"),ko=t("code"),Yt=i("xlm-mlm-xnli15-1024"),Zt=i(" (Masked language modeling, XNLI languages)"),es=d(),Xe=t("li"),$o=t("code"),os=i("xlm-mlm-tlm-xnli15-1024"),as=i(" (Masked language modeling + translation, XNLI languages)"),ts=d(),Ae=t("li"),Mo=t("code"),ss=i("xlm-clm-enfr-1024"),ls=i(" (Causal language modeling, English-French)"),is=d(),Ne=t("li"),xo=t("code"),rs=i("xlm-clm-ende-1024"),ns=i(" (Causal language modeling, English-German)"),$a=d(),$=t("p"),ms=i("Os language embeddings s\xE3o representados por um tensor de mesma dimens\xE3o que os "),wo=t("code"),ds=i("input_ids"),ps=i(` passados ao modelo.
Os valores destes tensores dependem do idioma utilizado e se identificam pelos atributos `),zo=t("code"),cs=i("lang2id"),us=i(" e "),yo=t("code"),fs=i("id2lang"),gs=i(" do tokenizador."),Ma=d(),R=t("p"),hs=i("Neste exemplo, carregamos o checkpoint "),qo=t("code"),_s=i("xlm-clm-enfr-1024"),vs=i("(Causal language modeling, English-French):"),xa=d(),u(ue.$$.fragment),wa=d(),F=t("p"),bs=i("O atributo "),jo=t("code"),Es=i("lang2id"),ks=i(" do tokenizador mostra os idiomas deste modelo e seus ids:"),za=d(),u(fe.$$.fragment),ya=d(),Se=t("p"),$s=i("Em seguida, cria-se um input de exemplo:"),qa=d(),u(ge.$$.fragment),ja=d(),M=t("p"),Ms=i("Estabelece-se o id do idioma, por exemplo "),Lo=t("code"),xs=i('"en"'),ws=i(`, e utiliza-se o mesmo para definir a language embedding.
A language embedding \xE9 um tensor preenchido com `),Oo=t("code"),zs=i("0"),ys=i(`, que \xE9 o id de idioma para o ingl\xEAs.
Este tensor deve ser do mesmo tamanho que os `),Co=t("code"),qs=i("input_ids"),js=i("."),La=d(),u(he.$$.fragment),Oa=d(),H=t("p"),Ls=i("Agora voc\xEA pode passar os "),To=t("code"),Os=i("input_ids"),Cs=i(" e a language embedding ao modelo:"),Ca=d(),u(_e.$$.fragment),Ta=d(),q=t("p"),Ts=i("O script "),ve=t("a"),Ps=i("run_generation.py"),Ds=i(" pode gerar um texto com language embeddings utilizando os checkpoints "),Po=t("code"),Xs=i("xlm-clm"),As=i("."),Pa=d(),P=t("h3"),G=t("a"),Do=t("span"),u(be.$$.fragment),Ns=d(),Xo=t("span"),Ss=i("XLM sem language embeddings"),Da=d(),Ie=t("p"),Is=i("Os seguintes modelos XLM n\xE3o requerem o uso de language embeddings durante a infer\xEAncia:"),Xa=d(),W=t("ul"),Be=t("li"),Ao=t("code"),Bs=i("xlm-mlm-17-1280"),Rs=i(" (Modelagem de linguagem com m\xE1scara, 17 idiomas)"),Fs=d(),Re=t("li"),No=t("code"),Hs=i("xlm-mlm-100-1280"),Gs=i(" (Modelagem de linguagem com m\xE1scara, 100 idiomas)"),Aa=d(),Fe=t("p"),Ws=i("Estes modelos s\xE3o utilizados para representa\xE7\xF5es gen\xE9ricas de frase diferentemente dos checkpoints XLM anteriores."),Na=d(),D=t("h2"),U=t("a"),So=t("span"),u(Ee.$$.fragment),Us=d(),Io=t("span"),Js=i("BERT"),Sa=d(),He=t("p"),Ks=i("Os seguintes modelos do BERT podem ser utilizados para tarefas multilingu\xEDsticas:"),Ia=d(),J=t("ul"),Ge=t("li"),Bo=t("code"),Qs=i("bert-base-multilingual-uncased"),Vs=i(" (Modelagem de linguagem com m\xE1scara + Previs\xE3o de frases, 102 idiomas)"),Ys=d(),We=t("li"),Ro=t("code"),Zs=i("bert-base-multilingual-cased"),el=i(" (Modelagem de linguagem com m\xE1scara + Previs\xE3o de frases, 104 idiomas)"),Ba=d(),Ue=t("p"),ol=i(`Estes modelos n\xE3o requerem language embeddings durante a infer\xEAncia. Devem identificar a linguagem a partir
do contexto e realizar a infer\xEAncia em sequ\xEAncia.`),Ra=d(),X=t("h2"),K=t("a"),Fo=t("span"),u(ke.$$.fragment),al=d(),Ho=t("span"),tl=i("XLM-RoBERTa"),Fa=d(),Je=t("p"),sl=i("Os seguintes modelos do XLM-RoBERTa podem ser utilizados para tarefas multilingu\xEDsticas:"),Ha=d(),Q=t("ul"),Ke=t("li"),Go=t("code"),ll=i("xlm-roberta-base"),il=i(" (Modelagem de linguagem com m\xE1scara, 100 idiomas)"),rl=d(),Qe=t("li"),Wo=t("code"),nl=i("xlm-roberta-large"),ml=i(" Modelagem de linguagem com m\xE1scara, 100 idiomas)"),Ga=d(),Ve=t("p"),dl=i(`O XLM-RoBERTa foi treinado com 2,5 TB de dados do CommonCrawl rec\xE9m-criados e testados em 100 idiomas.
Proporciona fortes vantagens sobre os modelos multilingu\xEDsticos publicados anteriormente como o mBERT e o XLM em tarefas
subsequentes como a classifica\xE7\xE3o, a rotulagem de sequ\xEAncias e \xE0 respostas a perguntas.`),Wa=d(),A=t("h2"),V=t("a"),Uo=t("span"),u($e.$$.fragment),pl=d(),Jo=t("span"),cl=i("M2M100"),Ua=d(),Ye=t("p"),ul=i("Os seguintes modelos de M2M100 podem ser utilizados para tradu\xE7\xF5es multilingu\xEDsticas:"),Ja=d(),Y=t("ul"),Ze=t("li"),Ko=t("code"),fl=i("facebook/m2m100_418M"),gl=i(" (Tradu\xE7\xE3o)"),hl=d(),eo=t("li"),Qo=t("code"),_l=i("facebook/m2m100_1.2B"),vl=i(" (Tradu\xE7\xE3o)"),Ka=d(),Z=t("p"),bl=i("Neste exemplo, o checkpoint "),Vo=t("code"),El=i("facebook/m2m100_418M"),kl=i(` \xE9 carregado para traduzir do mandarim ao ingl\xEAs. \xC9 poss\xEDvel
estabelecer o idioma de origem no tokenizador:`),Qa=d(),u(Me.$$.fragment),Va=d(),oo=t("p"),$l=i("Tokeniza\xE7\xE3o do texto:"),Ya=d(),u(xe.$$.fragment),Za=d(),x=t("p"),Ml=i(`O M2M100 for\xE7a o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
\xC9 definido o `),Yo=t("code"),xl=i("forced_bos_token_id"),wl=i(" como "),Zo=t("code"),zl=i("en"),yl=i(" no m\xE9todo "),ea=t("code"),ql=i("generate"),jl=i(" para traduzir ao ingl\xEAs."),et=d(),u(we.$$.fragment),ot=d(),N=t("h2"),ee=t("a"),oa=t("span"),u(ze.$$.fragment),Ll=d(),aa=t("span"),Ol=i("MBart"),at=d(),ao=t("p"),Cl=i("Os seguintes modelos do MBart podem ser utilizados para tradu\xE7\xE3o multilingu\xEDstica:"),tt=d(),E=t("ul"),to=t("li"),ta=t("code"),Tl=i("facebook/mbart-large-50-one-to-many-mmt"),Pl=i(" (Tradu\xE7\xE3o autom\xE1tica multilingu\xEDstica de um a v\xE1rios, 50 idiomas)"),Dl=d(),so=t("li"),sa=t("code"),Xl=i("facebook/mbart-large-50-many-to-many-mmt"),Al=i(" (Tradu\xE7\xE3o autom\xE1tica multilingu\xEDstica de v\xE1rios a v\xE1rios, 50 idiomas)"),Nl=d(),lo=t("li"),la=t("code"),Sl=i("facebook/mbart-large-50-many-to-one-mmt"),Il=i(" (Tradu\xE7\xE3o autom\xE1tica multilingu\xEDstica v\xE1rios a um, 50 idiomas)"),Bl=d(),io=t("li"),ia=t("code"),Rl=i("facebook/mbart-large-50"),Fl=i(" (Tradu\xE7\xE3o multilingu\xEDstica, 50 idiomas)"),Hl=d(),ra=t("li"),na=t("code"),Gl=i("facebook/mbart-large-cc25"),st=d(),oe=t("p"),Wl=i("Neste exemplo, carrega-se o checkpoint "),ma=t("code"),Ul=i("facebook/mbart-large-50-many-to-many-mmt"),Jl=i(` para traduzir do finland\xEAs ao ingl\xEAs.
Pode-se definir o idioma de origem no tokenizador:`),lt=d(),u(ye.$$.fragment),it=d(),ro=t("p"),Kl=i("Tokenizando o texto:"),rt=d(),u(qe.$$.fragment),nt=d(),w=t("p"),Ql=i(`O MBart for\xE7a o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
\xC9 definido o `),da=t("code"),Vl=i("forced_bos_token_id"),Yl=i(" como "),pa=t("code"),Zl=i("en"),ei=i(" no m\xE9todo "),ca=t("code"),oi=i("generate"),ai=i(" para traduzir ao ingl\xEAs."),mt=d(),u(je.$$.fragment),dt=d(),ae=t("p"),ti=i("Se estiver usando o checkpoint "),ua=t("code"),si=i("facebook/mbart-large-50-many-to-one-mmt"),li=i(` n\xE3o ser\xE1 necess\xE1rio for\xE7ar o id do idioma de destino
como sendo o primeiro token generado, caso contr\xE1rio a usagem \xE9 a mesma.`),this.h()},l(e){const n=on('[data-svelte="svelte-1phssyn"]',document.head);L=s(n,"META",{name:!0,content:!0}),n.forEach(a),fa=p(e),O=s(e,"H1",{class:!0});var ct=l(O);S=s(ct,"A",{id:!0,class:!0,href:!0});var zi=l(S);po=s(zi,"SPAN",{});var yi=l(po);f(ne.$$.fragment,yi),yi.forEach(a),zi.forEach(a),Lt=p(ct),co=s(ct,"SPAN",{});var qi=l(co);Ot=r(qi,"Modelos multilingu\xEDsticos para infer\xEAncia"),qi.forEach(a),ct.forEach(a),ga=p(e),f(me.$$.fragment,e),ha=p(e),y=s(e,"P",{});var no=l(y);Ct=r(no,`Existem v\xE1rios modelos multilingu\xEDsticos no \u{1F917} Transformers e seus usos para infer\xEAncia diferem dos modelos monol\xEDngues.
No entanto, nem `),uo=s(no,"EM",{});var ji=l(uo);Tt=r(ji,"todos"),ji.forEach(a),Pt=r(no,` os usos dos modelos multil\xEDngues s\xE3o t\xE3o diferentes.
Alguns modelos, como o `),de=s(no,"A",{href:!0,rel:!0});var Li=l(de);Dt=r(Li,"bert-base-multilingual-uncased"),Li.forEach(a),Xt=r(no,`,
podem ser usados como se fossem monol\xEDngues. Este guia ir\xE1 te ajudar a usar modelos multil\xEDngues cujo uso difere
para o prop\xF3sito de infer\xEAncia.`),no.forEach(a),_a=p(e),C=s(e,"H2",{class:!0});var ut=l(C);I=s(ut,"A",{id:!0,class:!0,href:!0});var Oi=l(I);fo=s(Oi,"SPAN",{});var Ci=l(fo);f(pe.$$.fragment,Ci),Ci.forEach(a),Oi.forEach(a),At=p(ut),go=s(ut,"SPAN",{});var Ti=l(go);Nt=r(Ti,"XLM"),Ti.forEach(a),ut.forEach(a),va=p(e),Le=s(e,"P",{});var Pi=l(Le);St=r(Pi,`O XLM tem dez checkpoints diferentes dos quais apenas um \xE9 monol\xEDngue.
Os nove checkpoints restantes do modelo s\xE3o subdivididos em duas categorias:
checkpoints que usam de language embeddings e os que n\xE3o.`),Pi.forEach(a),ba=p(e),T=s(e,"H3",{class:!0});var ft=l(T);B=s(ft,"A",{id:!0,class:!0,href:!0});var Di=l(B);ho=s(Di,"SPAN",{});var Xi=l(ho);f(ce.$$.fragment,Xi),Xi.forEach(a),Di.forEach(a),It=p(ft),_o=s(ft,"SPAN",{});var Ai=l(_o);Bt=r(Ai,"XLM com language embeddings"),Ai.forEach(a),ft.forEach(a),Ea=p(e),Oe=s(e,"P",{});var Ni=l(Oe);Rt=r(Ni,"Os seguintes modelos de XLM usam language embeddings para especificar a linguagem utilizada para a infer\xEAncia."),Ni.forEach(a),ka=p(e),b=s(e,"UL",{});var k=l(b);Ce=s(k,"LI",{});var ii=l(Ce);vo=s(ii,"CODE",{});var Si=l(vo);Ft=r(Si,"xlm-mlm-ende-1024"),Si.forEach(a),Ht=r(ii," (Masked language modeling, English-German)"),ii.forEach(a),Gt=p(k),Te=s(k,"LI",{});var ri=l(Te);bo=s(ri,"CODE",{});var Ii=l(bo);Wt=r(Ii,"xlm-mlm-enfr-1024"),Ii.forEach(a),Ut=r(ri," (Masked language modeling, English-French)"),ri.forEach(a),Jt=p(k),Pe=s(k,"LI",{});var ni=l(Pe);Eo=s(ni,"CODE",{});var Bi=l(Eo);Kt=r(Bi,"xlm-mlm-enro-1024"),Bi.forEach(a),Qt=r(ni," (Masked language modeling, English-Romanian)"),ni.forEach(a),Vt=p(k),De=s(k,"LI",{});var mi=l(De);ko=s(mi,"CODE",{});var Ri=l(ko);Yt=r(Ri,"xlm-mlm-xnli15-1024"),Ri.forEach(a),Zt=r(mi," (Masked language modeling, XNLI languages)"),mi.forEach(a),es=p(k),Xe=s(k,"LI",{});var di=l(Xe);$o=s(di,"CODE",{});var Fi=l($o);os=r(Fi,"xlm-mlm-tlm-xnli15-1024"),Fi.forEach(a),as=r(di," (Masked language modeling + translation, XNLI languages)"),di.forEach(a),ts=p(k),Ae=s(k,"LI",{});var pi=l(Ae);Mo=s(pi,"CODE",{});var Hi=l(Mo);ss=r(Hi,"xlm-clm-enfr-1024"),Hi.forEach(a),ls=r(pi," (Causal language modeling, English-French)"),pi.forEach(a),is=p(k),Ne=s(k,"LI",{});var ci=l(Ne);xo=s(ci,"CODE",{});var Gi=l(xo);rs=r(Gi,"xlm-clm-ende-1024"),Gi.forEach(a),ns=r(ci," (Causal language modeling, English-German)"),ci.forEach(a),k.forEach(a),$a=p(e),$=s(e,"P",{});var te=l($);ms=r(te,"Os language embeddings s\xE3o representados por um tensor de mesma dimens\xE3o que os "),wo=s(te,"CODE",{});var Wi=l(wo);ds=r(Wi,"input_ids"),Wi.forEach(a),ps=r(te,` passados ao modelo.
Os valores destes tensores dependem do idioma utilizado e se identificam pelos atributos `),zo=s(te,"CODE",{});var Ui=l(zo);cs=r(Ui,"lang2id"),Ui.forEach(a),us=r(te," e "),yo=s(te,"CODE",{});var Ji=l(yo);fs=r(Ji,"id2lang"),Ji.forEach(a),gs=r(te," do tokenizador."),te.forEach(a),Ma=p(e),R=s(e,"P",{});var gt=l(R);hs=r(gt,"Neste exemplo, carregamos o checkpoint "),qo=s(gt,"CODE",{});var Ki=l(qo);_s=r(Ki,"xlm-clm-enfr-1024"),Ki.forEach(a),vs=r(gt,"(Causal language modeling, English-French):"),gt.forEach(a),xa=p(e),f(ue.$$.fragment,e),wa=p(e),F=s(e,"P",{});var ht=l(F);bs=r(ht,"O atributo "),jo=s(ht,"CODE",{});var Qi=l(jo);Es=r(Qi,"lang2id"),Qi.forEach(a),ks=r(ht," do tokenizador mostra os idiomas deste modelo e seus ids:"),ht.forEach(a),za=p(e),f(fe.$$.fragment,e),ya=p(e),Se=s(e,"P",{});var Vi=l(Se);$s=r(Vi,"Em seguida, cria-se um input de exemplo:"),Vi.forEach(a),qa=p(e),f(ge.$$.fragment,e),ja=p(e),M=s(e,"P",{});var se=l(M);Ms=r(se,"Estabelece-se o id do idioma, por exemplo "),Lo=s(se,"CODE",{});var Yi=l(Lo);xs=r(Yi,'"en"'),Yi.forEach(a),ws=r(se,`, e utiliza-se o mesmo para definir a language embedding.
A language embedding \xE9 um tensor preenchido com `),Oo=s(se,"CODE",{});var Zi=l(Oo);zs=r(Zi,"0"),Zi.forEach(a),ys=r(se,`, que \xE9 o id de idioma para o ingl\xEAs.
Este tensor deve ser do mesmo tamanho que os `),Co=s(se,"CODE",{});var er=l(Co);qs=r(er,"input_ids"),er.forEach(a),js=r(se,"."),se.forEach(a),La=p(e),f(he.$$.fragment,e),Oa=p(e),H=s(e,"P",{});var _t=l(H);Ls=r(_t,"Agora voc\xEA pode passar os "),To=s(_t,"CODE",{});var or=l(To);Os=r(or,"input_ids"),or.forEach(a),Cs=r(_t," e a language embedding ao modelo:"),_t.forEach(a),Ca=p(e),f(_e.$$.fragment,e),Ta=p(e),q=s(e,"P",{});var mo=l(q);Ts=r(mo,"O script "),ve=s(mo,"A",{href:!0,rel:!0});var ar=l(ve);Ps=r(ar,"run_generation.py"),ar.forEach(a),Ds=r(mo," pode gerar um texto com language embeddings utilizando os checkpoints "),Po=s(mo,"CODE",{});var tr=l(Po);Xs=r(tr,"xlm-clm"),tr.forEach(a),As=r(mo,"."),mo.forEach(a),Pa=p(e),P=s(e,"H3",{class:!0});var vt=l(P);G=s(vt,"A",{id:!0,class:!0,href:!0});var sr=l(G);Do=s(sr,"SPAN",{});var lr=l(Do);f(be.$$.fragment,lr),lr.forEach(a),sr.forEach(a),Ns=p(vt),Xo=s(vt,"SPAN",{});var ir=l(Xo);Ss=r(ir,"XLM sem language embeddings"),ir.forEach(a),vt.forEach(a),Da=p(e),Ie=s(e,"P",{});var rr=l(Ie);Is=r(rr,"Os seguintes modelos XLM n\xE3o requerem o uso de language embeddings durante a infer\xEAncia:"),rr.forEach(a),Xa=p(e),W=s(e,"UL",{});var bt=l(W);Be=s(bt,"LI",{});var ui=l(Be);Ao=s(ui,"CODE",{});var nr=l(Ao);Bs=r(nr,"xlm-mlm-17-1280"),nr.forEach(a),Rs=r(ui," (Modelagem de linguagem com m\xE1scara, 17 idiomas)"),ui.forEach(a),Fs=p(bt),Re=s(bt,"LI",{});var fi=l(Re);No=s(fi,"CODE",{});var mr=l(No);Hs=r(mr,"xlm-mlm-100-1280"),mr.forEach(a),Gs=r(fi," (Modelagem de linguagem com m\xE1scara, 100 idiomas)"),fi.forEach(a),bt.forEach(a),Aa=p(e),Fe=s(e,"P",{});var dr=l(Fe);Ws=r(dr,"Estes modelos s\xE3o utilizados para representa\xE7\xF5es gen\xE9ricas de frase diferentemente dos checkpoints XLM anteriores."),dr.forEach(a),Na=p(e),D=s(e,"H2",{class:!0});var Et=l(D);U=s(Et,"A",{id:!0,class:!0,href:!0});var pr=l(U);So=s(pr,"SPAN",{});var cr=l(So);f(Ee.$$.fragment,cr),cr.forEach(a),pr.forEach(a),Us=p(Et),Io=s(Et,"SPAN",{});var ur=l(Io);Js=r(ur,"BERT"),ur.forEach(a),Et.forEach(a),Sa=p(e),He=s(e,"P",{});var fr=l(He);Ks=r(fr,"Os seguintes modelos do BERT podem ser utilizados para tarefas multilingu\xEDsticas:"),fr.forEach(a),Ia=p(e),J=s(e,"UL",{});var kt=l(J);Ge=s(kt,"LI",{});var gi=l(Ge);Bo=s(gi,"CODE",{});var gr=l(Bo);Qs=r(gr,"bert-base-multilingual-uncased"),gr.forEach(a),Vs=r(gi," (Modelagem de linguagem com m\xE1scara + Previs\xE3o de frases, 102 idiomas)"),gi.forEach(a),Ys=p(kt),We=s(kt,"LI",{});var hi=l(We);Ro=s(hi,"CODE",{});var hr=l(Ro);Zs=r(hr,"bert-base-multilingual-cased"),hr.forEach(a),el=r(hi," (Modelagem de linguagem com m\xE1scara + Previs\xE3o de frases, 104 idiomas)"),hi.forEach(a),kt.forEach(a),Ba=p(e),Ue=s(e,"P",{});var _r=l(Ue);ol=r(_r,`Estes modelos n\xE3o requerem language embeddings durante a infer\xEAncia. Devem identificar a linguagem a partir
do contexto e realizar a infer\xEAncia em sequ\xEAncia.`),_r.forEach(a),Ra=p(e),X=s(e,"H2",{class:!0});var $t=l(X);K=s($t,"A",{id:!0,class:!0,href:!0});var vr=l(K);Fo=s(vr,"SPAN",{});var br=l(Fo);f(ke.$$.fragment,br),br.forEach(a),vr.forEach(a),al=p($t),Ho=s($t,"SPAN",{});var Er=l(Ho);tl=r(Er,"XLM-RoBERTa"),Er.forEach(a),$t.forEach(a),Fa=p(e),Je=s(e,"P",{});var kr=l(Je);sl=r(kr,"Os seguintes modelos do XLM-RoBERTa podem ser utilizados para tarefas multilingu\xEDsticas:"),kr.forEach(a),Ha=p(e),Q=s(e,"UL",{});var Mt=l(Q);Ke=s(Mt,"LI",{});var _i=l(Ke);Go=s(_i,"CODE",{});var $r=l(Go);ll=r($r,"xlm-roberta-base"),$r.forEach(a),il=r(_i," (Modelagem de linguagem com m\xE1scara, 100 idiomas)"),_i.forEach(a),rl=p(Mt),Qe=s(Mt,"LI",{});var vi=l(Qe);Wo=s(vi,"CODE",{});var Mr=l(Wo);nl=r(Mr,"xlm-roberta-large"),Mr.forEach(a),ml=r(vi," Modelagem de linguagem com m\xE1scara, 100 idiomas)"),vi.forEach(a),Mt.forEach(a),Ga=p(e),Ve=s(e,"P",{});var xr=l(Ve);dl=r(xr,`O XLM-RoBERTa foi treinado com 2,5 TB de dados do CommonCrawl rec\xE9m-criados e testados em 100 idiomas.
Proporciona fortes vantagens sobre os modelos multilingu\xEDsticos publicados anteriormente como o mBERT e o XLM em tarefas
subsequentes como a classifica\xE7\xE3o, a rotulagem de sequ\xEAncias e \xE0 respostas a perguntas.`),xr.forEach(a),Wa=p(e),A=s(e,"H2",{class:!0});var xt=l(A);V=s(xt,"A",{id:!0,class:!0,href:!0});var wr=l(V);Uo=s(wr,"SPAN",{});var zr=l(Uo);f($e.$$.fragment,zr),zr.forEach(a),wr.forEach(a),pl=p(xt),Jo=s(xt,"SPAN",{});var yr=l(Jo);cl=r(yr,"M2M100"),yr.forEach(a),xt.forEach(a),Ua=p(e),Ye=s(e,"P",{});var qr=l(Ye);ul=r(qr,"Os seguintes modelos de M2M100 podem ser utilizados para tradu\xE7\xF5es multilingu\xEDsticas:"),qr.forEach(a),Ja=p(e),Y=s(e,"UL",{});var wt=l(Y);Ze=s(wt,"LI",{});var bi=l(Ze);Ko=s(bi,"CODE",{});var jr=l(Ko);fl=r(jr,"facebook/m2m100_418M"),jr.forEach(a),gl=r(bi," (Tradu\xE7\xE3o)"),bi.forEach(a),hl=p(wt),eo=s(wt,"LI",{});var Ei=l(eo);Qo=s(Ei,"CODE",{});var Lr=l(Qo);_l=r(Lr,"facebook/m2m100_1.2B"),Lr.forEach(a),vl=r(Ei," (Tradu\xE7\xE3o)"),Ei.forEach(a),wt.forEach(a),Ka=p(e),Z=s(e,"P",{});var zt=l(Z);bl=r(zt,"Neste exemplo, o checkpoint "),Vo=s(zt,"CODE",{});var Or=l(Vo);El=r(Or,"facebook/m2m100_418M"),Or.forEach(a),kl=r(zt,` \xE9 carregado para traduzir do mandarim ao ingl\xEAs. \xC9 poss\xEDvel
estabelecer o idioma de origem no tokenizador:`),zt.forEach(a),Qa=p(e),f(Me.$$.fragment,e),Va=p(e),oo=s(e,"P",{});var Cr=l(oo);$l=r(Cr,"Tokeniza\xE7\xE3o do texto:"),Cr.forEach(a),Ya=p(e),f(xe.$$.fragment,e),Za=p(e),x=s(e,"P",{});var le=l(x);Ml=r(le,`O M2M100 for\xE7a o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
\xC9 definido o `),Yo=s(le,"CODE",{});var Tr=l(Yo);xl=r(Tr,"forced_bos_token_id"),Tr.forEach(a),wl=r(le," como "),Zo=s(le,"CODE",{});var Pr=l(Zo);zl=r(Pr,"en"),Pr.forEach(a),yl=r(le," no m\xE9todo "),ea=s(le,"CODE",{});var Dr=l(ea);ql=r(Dr,"generate"),Dr.forEach(a),jl=r(le," para traduzir ao ingl\xEAs."),le.forEach(a),et=p(e),f(we.$$.fragment,e),ot=p(e),N=s(e,"H2",{class:!0});var yt=l(N);ee=s(yt,"A",{id:!0,class:!0,href:!0});var Xr=l(ee);oa=s(Xr,"SPAN",{});var Ar=l(oa);f(ze.$$.fragment,Ar),Ar.forEach(a),Xr.forEach(a),Ll=p(yt),aa=s(yt,"SPAN",{});var Nr=l(aa);Ol=r(Nr,"MBart"),Nr.forEach(a),yt.forEach(a),at=p(e),ao=s(e,"P",{});var Sr=l(ao);Cl=r(Sr,"Os seguintes modelos do MBart podem ser utilizados para tradu\xE7\xE3o multilingu\xEDstica:"),Sr.forEach(a),tt=p(e),E=s(e,"UL",{});var j=l(E);to=s(j,"LI",{});var ki=l(to);ta=s(ki,"CODE",{});var Ir=l(ta);Tl=r(Ir,"facebook/mbart-large-50-one-to-many-mmt"),Ir.forEach(a),Pl=r(ki," (Tradu\xE7\xE3o autom\xE1tica multilingu\xEDstica de um a v\xE1rios, 50 idiomas)"),ki.forEach(a),Dl=p(j),so=s(j,"LI",{});var $i=l(so);sa=s($i,"CODE",{});var Br=l(sa);Xl=r(Br,"facebook/mbart-large-50-many-to-many-mmt"),Br.forEach(a),Al=r($i," (Tradu\xE7\xE3o autom\xE1tica multilingu\xEDstica de v\xE1rios a v\xE1rios, 50 idiomas)"),$i.forEach(a),Nl=p(j),lo=s(j,"LI",{});var Mi=l(lo);la=s(Mi,"CODE",{});var Rr=l(la);Sl=r(Rr,"facebook/mbart-large-50-many-to-one-mmt"),Rr.forEach(a),Il=r(Mi," (Tradu\xE7\xE3o autom\xE1tica multilingu\xEDstica v\xE1rios a um, 50 idiomas)"),Mi.forEach(a),Bl=p(j),io=s(j,"LI",{});var xi=l(io);ia=s(xi,"CODE",{});var Fr=l(ia);Rl=r(Fr,"facebook/mbart-large-50"),Fr.forEach(a),Fl=r(xi," (Tradu\xE7\xE3o multilingu\xEDstica, 50 idiomas)"),xi.forEach(a),Hl=p(j),ra=s(j,"LI",{});var Hr=l(ra);na=s(Hr,"CODE",{});var Gr=l(na);Gl=r(Gr,"facebook/mbart-large-cc25"),Gr.forEach(a),Hr.forEach(a),j.forEach(a),st=p(e),oe=s(e,"P",{});var qt=l(oe);Wl=r(qt,"Neste exemplo, carrega-se o checkpoint "),ma=s(qt,"CODE",{});var Wr=l(ma);Ul=r(Wr,"facebook/mbart-large-50-many-to-many-mmt"),Wr.forEach(a),Jl=r(qt,` para traduzir do finland\xEAs ao ingl\xEAs.
Pode-se definir o idioma de origem no tokenizador:`),qt.forEach(a),lt=p(e),f(ye.$$.fragment,e),it=p(e),ro=s(e,"P",{});var Ur=l(ro);Kl=r(Ur,"Tokenizando o texto:"),Ur.forEach(a),rt=p(e),f(qe.$$.fragment,e),nt=p(e),w=s(e,"P",{});var ie=l(w);Ql=r(ie,`O MBart for\xE7a o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
\xC9 definido o `),da=s(ie,"CODE",{});var Jr=l(da);Vl=r(Jr,"forced_bos_token_id"),Jr.forEach(a),Yl=r(ie," como "),pa=s(ie,"CODE",{});var Kr=l(pa);Zl=r(Kr,"en"),Kr.forEach(a),ei=r(ie," no m\xE9todo "),ca=s(ie,"CODE",{});var Qr=l(ca);oi=r(Qr,"generate"),Qr.forEach(a),ai=r(ie," para traduzir ao ingl\xEAs."),ie.forEach(a),mt=p(e),f(je.$$.fragment,e),dt=p(e),ae=s(e,"P",{});var jt=l(ae);ti=r(jt,"Se estiver usando o checkpoint "),ua=s(jt,"CODE",{});var Vr=l(ua);si=r(Vr,"facebook/mbart-large-50-many-to-one-mmt"),Vr.forEach(a),li=r(jt,` n\xE3o ser\xE1 necess\xE1rio for\xE7ar o id do idioma de destino
como sendo o primeiro token generado, caso contr\xE1rio a usagem \xE9 a mesma.`),jt.forEach(a),this.h()},h(){c(L,"name","hf:doc:metadata"),c(L,"content",JSON.stringify(rn)),c(S,"id","modelos-multilingusticos-para-inferncia"),c(S,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(S,"href","#modelos-multilingusticos-para-inferncia"),c(O,"class","relative group"),c(de,"href","https://huggingface.co/bert-base-multilingual-uncased"),c(de,"rel","nofollow"),c(I,"id","xlm"),c(I,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),c(I,"href","#xlm"),c(C,"class","relative group"),c(B,"id","xlm-com-language-embeddings"),c(B,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/tasks/sequence_classification.mdx-hf-doc-builder.js | import{S as Ba,i as Ra,s as La,e as i,k as $,w as q,t as r,M as Wa,c as p,d as a,m as g,a as c,x as C,h as n,b as w,G as t,g as m,y as T,q as D,o as y,B as P,v as Ua,L as Na}from"../../chunks/vendor-hf-doc-builder.js";import{T as ut}from"../../chunks/Tip-hf-doc-builder.js";import{Y as Va}from"../../chunks/Youtube-hf-doc-builder.js";import{I as St}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as le}from"../../chunks/CodeBlock-hf-doc-builder.js";import{F as Ia,M as Ot}from"../../chunks/Markdown-hf-doc-builder.js";function Ga(S){let s,f,o,u,b;return{c(){s=i("p"),f=r("Consulte a "),o=i("a"),u=r("p\xE1gina de tarefas de classifica\xE7\xE3o de texto"),b=r(" para obter mais informa\xE7\xF5es sobre outras formas de classifica\xE7\xE3o de texto e seus modelos, conjuntos de dados e m\xE9tricas associados."),this.h()},l(_){s=p(_,"P",{});var v=c(s);f=n(v,"Consulte a "),o=p(v,"A",{href:!0,rel:!0});var z=c(o);u=n(z,"p\xE1gina de tarefas de classifica\xE7\xE3o de texto"),z.forEach(a),b=n(v," para obter mais informa\xE7\xF5es sobre outras formas de classifica\xE7\xE3o de texto e seus modelos, conjuntos de dados e m\xE9tricas associados."),v.forEach(a),this.h()},h(){w(o,"href","https://huggingface.co/tasks/text-classification"),w(o,"rel","nofollow")},m(_,v){m(_,s,v),t(s,f),t(s,o),t(o,u),t(s,b)},d(_){_&&a(s)}}}function Ha(S){let s,f;return s=new le({props:{code:`from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding
<span class="hljs-meta">>>> </span>data_collator = DataCollatorWithPadding(tokenizer=tokenizer)`}}),{c(){q(s.$$.fragment)},l(o){C(s.$$.fragment,o)},m(o,u){T(s,o,u),f=!0},p:Na,i(o){f||(D(s.$$.fragment,o),f=!0)},o(o){y(s.$$.fragment,o),f=!1},d(o){P(s,o)}}}function Ka(S){let s,f;return s=new Ot({props:{$$slots:{default:[Ha]},$$scope:{ctx:S}}}),{c(){q(s.$$.fragment)},l(o){C(s.$$.fragment,o)},m(o,u){T(s,o,u),f=!0},p(o,u){const b={};u&2&&(b.$$scope={dirty:u,ctx:o}),s.$set(b)},i(o){f||(D(s.$$.fragment,o),f=!0)},o(o){y(s.$$.fragment,o),f=!1},d(o){P(s,o)}}}function Ja(S){let s,f;return s=new le({props:{code:`from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding
<span class="hljs-meta">>>> </span>data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=<span class="hljs-string">"tf"</span>)`}}),{c(){q(s.$$.fragment)},l(o){C(s.$$.fragment,o)},m(o,u){T(s,o,u),f=!0},p:Na,i(o){f||(D(s.$$.fragment,o),f=!0)},o(o){y(s.$$.fragment,o),f=!1},d(o){P(s,o)}}}function Ya(S){let s,f;return s=new Ot({props:{$$slots:{default:[Ja]},$$scope:{ctx:S}}}),{c(){q(s.$$.fragment)},l(o){C(s.$$.fragment,o)},m(o,u){T(s,o,u),f=!0},p(o,u){const b={};u&2&&(b.$$scope={dirty:u,ctx:o}),s.$set(b)},i(o){f||(D(s.$$.fragment,o),f=!0)},o(o){y(s.$$.fragment,o),f=!1},d(o){P(s,o)}}}function Qa(S){let s,f,o,u,b,_,v,z;return{c(){s=i("p"),f=r("Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o "),o=i("code"),u=r("Trainer"),b=r(", d\xEA uma olhada no tutorial b\xE1sico "),_=i("a"),v=r("aqui"),z=r("!"),this.h()},l(x){s=p(x,"P",{});var j=c(s);f=n(j,"Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o "),o=p(j,"CODE",{});var A=c(o);u=n(A,"Trainer"),A.forEach(a),b=n(j,", d\xEA uma olhada no tutorial b\xE1sico "),_=p(j,"A",{href:!0});var I=c(_);v=n(I,"aqui"),I.forEach(a),z=n(j,"!"),j.forEach(a),this.h()},h(){w(_,"href","../training#finetune-with-trainer")},m(x,j){m(x,s,j),t(s,f),t(s,o),t(o,u),t(s,b),t(s,_),t(_,v),t(s,z)},d(x){x&&a(s)}}}function Xa(S){let s,f,o,u,b,_,v,z;return{c(){s=i("p"),f=r("O "),o=i("code"),u=r("Trainer"),b=r(" aplicar\xE1 o preenchimento din\xE2mico por padr\xE3o quando voc\xEA definir o argumento "),_=i("code"),v=r("tokenizer"),z=r(" dele. Nesse caso, voc\xEA n\xE3o precisa especificar um data collator explicitamente.")},l(x){s=p(x,"P",{});var j=c(s);f=n(j,"O "),o=p(j,"CODE",{});var A=c(o);u=n(A,"Trainer"),A.forEach(a),b=n(j," aplicar\xE1 o preenchimento din\xE2mico por padr\xE3o quando voc\xEA definir o argumento "),_=p(j,"CODE",{});var I=c(_);v=n(I,"tokenizer"),I.forEach(a),z=n(j," dele. Nesse caso, voc\xEA n\xE3o precisa especificar um data collator explicitamente."),j.forEach(a)},m(x,j){m(x,s,j),t(s,f),t(s,o),t(o,u),t(s,b),t(s,_),t(_,v),t(s,z)},d(x){x&&a(s)}}}function Za(S){let s,f,o,u,b,_,v,z,x,j,A,I,Y,M,H,O,se,U,be,fe,F,ue,V,he,G,_e,N,K,R,Q,X,oe,L,ee,W,$e;return v=new le({props:{code:`from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)`}}),x=new ut({props:{$$slots:{default:[Qa]},$$scope:{ctx:S}}}),L=new le({props:{code:`training_args = TrainingArguments(
output_dir="./results",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_imdb["train"],
eval_dataset=tokenized_imdb["test"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">5</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_imdb[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_imdb[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),W=new ut({props:{$$slots:{default:[Xa]},$$scope:{ctx:S}}}),{c(){s=i("p"),f=r("Carregue o DistilBERT com "),o=i("code"),u=r("AutoModelForSequenceClassification"),b=r(" junto com o n\xFAmero de r\xF3tulos esperados:"),_=$(),q(v.$$.fragment),z=$(),q(x.$$.fragment),j=$(),A=i("p"),I=r("Nesse ponto, restam apenas tr\xEAs passos:"),Y=$(),M=i("ol"),H=i("li"),O=r("Definir seus hiperpar\xE2metros de treinamento em "),se=i("code"),U=r("TrainingArguments"),be=r("."),fe=$(),F=i("li"),ue=r("Passar os argumentos de treinamento para o "),V=i("code"),he=r("Trainer"),G=r(" junto com o modelo, conjunto de dados, tokenizador e o data collator."),_e=$(),N=i("li"),K=r("Chamar a fun\xE7\xE3o "),R=i("code"),Q=r("train()"),X=r(" para executar o fine-tuning do seu modelo."),oe=$(),q(L.$$.fragment),ee=$(),q(W.$$.fragment)},l(d){s=p(d,"P",{});var k=c(s);f=n(k,"Carregue o DistilBERT com "),o=p(k,"CODE",{});var te=c(o);u=n(te,"AutoModelForSequenceClassification"),te.forEach(a),b=n(k," junto com o n\xFAmero de r\xF3tulos esperados:"),k.forEach(a),_=g(d),C(v.$$.fragment,d),z=g(d),C(x.$$.fragment,d),j=g(d),A=p(d,"P",{});var B=c(A);I=n(B,"Nesse ponto, restam apenas tr\xEAs passos:"),B.forEach(a),Y=g(d),M=p(d,"OL",{});var re=c(M);H=p(re,"LI",{});var J=c(H);O=n(J,"Definir seus hiperpar\xE2metros de treinamento em "),se=p(J,"CODE",{});var Z=c(se);U=n(Z,"TrainingArguments"),Z.forEach(a),be=n(J,"."),J.forEach(a),fe=g(re),F=p(re,"LI",{});var ie=c(F);ue=n(ie,"Passar os argumentos de treinamento para o "),V=p(ie,"CODE",{});var pe=c(V);he=n(pe,"Trainer"),pe.forEach(a),G=n(ie," junto com o modelo, conjunto de dados, tokenizador e o data collator."),ie.forEach(a),_e=g(re),N=p(re,"LI",{});var ce=c(N);K=n(ce,"Chamar a fun\xE7\xE3o "),R=p(ce,"CODE",{});var ae=c(R);Q=n(ae,"train()"),ae.forEach(a),X=n(ce," para executar o fine-tuning do seu modelo."),ce.forEach(a),re.forEach(a),oe=g(d),C(L.$$.fragment,d),ee=g(d),C(W.$$.fragment,d)},m(d,k){m(d,s,k),t(s,f),t(s,o),t(o,u),t(s,b),m(d,_,k),T(v,d,k),m(d,z,k),T(x,d,k),m(d,j,k),m(d,A,k),t(A,I),m(d,Y,k),m(d,M,k),t(M,H),t(H,O),t(H,se),t(se,U),t(H,be),t(M,fe),t(M,F),t(F,ue),t(F,V),t(V,he),t(F,G),t(M,_e),t(M,N),t(N,K),t(N,R),t(R,Q),t(N,X),m(d,oe,k),T(L,d,k),m(d,ee,k),T(W,d,k),$e=!0},p(d,k){const te={};k&2&&(te.$$scope={dirty:k,ctx:d}),x.$set(te);const B={};k&2&&(B.$$scope={dirty:k,ctx:d}),W.$set(B)},i(d){$e||(D(v.$$.fragment,d),D(x.$$.fragment,d),D(L.$$.fragment,d),D(W.$$.fragment,d),$e=!0)},o(d){y(v.$$.fragment,d),y(x.$$.fragment,d),y(L.$$.fragment,d),y(W.$$.fragment,d),$e=!1},d(d){d&&a(s),d&&a(_),P(v,d),d&&a(z),P(x,d),d&&a(j),d&&a(A),d&&a(Y),d&&a(M),d&&a(oe),P(L,d),d&&a(ee),P(W,d)}}}function es(S){let s,f;return s=new Ot({props:{$$slots:{default:[Za]},$$scope:{ctx:S}}}),{c(){q(s.$$.fragment)},l(o){C(s.$$.fragment,o)},m(o,u){T(s,o,u),f=!0},p(o,u){const b={};u&2&&(b.$$scope={dirty:u,ctx:o}),s.$set(b)},i(o){f||(D(s.$$.fragment,o),f=!0)},o(o){y(s.$$.fragment,o),f=!1},d(o){P(s,o)}}}function ts(S){let s,f,o,u,b;return{c(){s=i("p"),f=r("Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o Keras, d\xEA uma olhada no tutorial b\xE1sico "),o=i("a"),u=r("aqui"),b=r("!"),this.h()},l(_){s=p(_,"P",{});var v=c(s);f=n(v,"Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o Keras, d\xEA uma olhada no tutorial b\xE1sico "),o=p(v,"A",{href:!0});var z=c(o);u=n(z,"aqui"),z.forEach(a),b=n(v,"!"),v.forEach(a),this.h()},h(){w(o,"href","training#finetune-with-keras")},m(_,v){m(_,s,v),t(s,f),t(s,o),t(o,u),t(s,b)},d(_){_&&a(s)}}}function as(S){let s,f,o,u,b,_,v,z,x,j,A,I,Y,M,H,O,se,U,be,fe,F,ue,V,he,G,_e,N,K,R,Q,X,oe,L,ee,W,$e,d,k,te,B,re,J,Z,ie,pe,ce,ae,me;return M=new le({props:{code:`tf_train_set = tokenized_imdb["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "label"],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_validation_set = tokenized_imdb["test"].to_tf_dataset(
columns=["attention_mask", "input_ids", "label"],
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = tokenized_imdb[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_imdb[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),O=new ut({props:{$$slots:{default:[ts]},$$scope:{ctx:S}}}),F=new le({props:{code:`from transformers import create_optimizer
import tensorflow as tf
batch_size = 16
num_epochs = 5
batches_per_epoch = len(tokenized_imdb["train"]) // batch_size
total_train_steps = int(batches_per_epoch * num_epochs)
optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">5</span>
<span class="hljs-meta">>>> </span>batches_per_epoch = <span class="hljs-built_in">len</span>(tokenized_imdb[<span class="hljs-string">"train"</span>]) // batch_size
<span class="hljs-meta">>>> </span>total_train_steps = <span class="hljs-built_in">int</span>(batches_per_epoch * num_epochs)
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(init_lr=<span class="hljs-number">2e-5</span>, num_warmup_steps=<span class="hljs-number">0</span>, num_train_steps=total_train_steps)`}}),R=new le({props:{code:`from transformers import TFAutoModelForSequenceClassification
model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)`}}),k=new le({props:{code:`import tensorflow as tf
model.compile(optimizer=optimizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)`}}),ae=new le({props:{code:"model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)'}}),{c(){s=i("p"),f=r("Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato "),o=i("code"),u=r("tf.data.Dataset"),b=r(" com "),_=i("a"),v=i("code"),z=r("to_tf_dataset"),x=r(". Nessa execu\xE7\xE3o voc\xEA dever\xE1 especificar as entradas e r\xF3tulos (no par\xE2metro "),j=i("code"),A=r("columns"),I=r("), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:"),Y=$(),q(M.$$.fragment),H=$(),q(O.$$.fragment),se=$(),U=i("p"),be=r("Configure o otimizador e alguns hiperpar\xE2metros de treinamento:"),fe=$(),q(F.$$.fragment),ue=$(),V=i("p"),he=r("Carregue o DistilBERT com "),G=i("code"),_e=r("TFAutoModelForSequenceClassification"),N=r(" junto com o n\xFAmero de r\xF3tulos esperados:"),K=$(),q(R.$$.fragment),Q=$(),X=i("p"),oe=r("Configure o modelo para treinamento com o m\xE9todo "),L=i("a"),ee=i("code"),W=r("compile"),$e=r(":"),d=$(),q(k.$$.fragment),te=$(),B=i("p"),re=r("Chame o m\xE9todo "),J=i("a"),Z=i("code"),ie=r("fit"),pe=r(" para executar o fine-tuning do modelo:"),ce=$(),q(ae.$$.fragment),this.h()},l(l){s=p(l,"P",{});var E=c(s);f=n(E,"Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato "),o=p(E,"CODE",{});var Ee=c(o);u=n(Ee,"tf.data.Dataset"),Ee.forEach(a),b=n(E," com "),_=p(E,"A",{href:!0,rel:!0});var Ue=c(_);v=p(Ue,"CODE",{});var Ve=c(v);z=n(Ve,"to_tf_dataset"),Ve.forEach(a),Ue.forEach(a),x=n(E,". Nessa execu\xE7\xE3o voc\xEA dever\xE1 especificar as entradas e r\xF3tulos (no par\xE2metro "),j=p(E,"CODE",{});var de=c(j);A=n(de,"columns"),de.forEach(a),I=n(E,"), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:"),E.forEach(a),Y=g(l),C(M.$$.fragment,l),H=g(l),C(O.$$.fragment,l),se=g(l),U=p(l,"P",{});var ze=c(U);be=n(ze,"Configure o otimizador e alguns hiperpar\xE2metros de treinamento:"),ze.forEach(a),fe=g(l),C(F.$$.fragment,l),ue=g(l),V=p(l,"P",{});var qe=c(V);he=n(qe,"Carregue o DistilBERT com "),G=p(qe,"CODE",{});var Ge=c(G);_e=n(Ge,"TFAutoModelForSequenceClassification"),Ge.forEach(a),N=n(qe," junto com o n\xFAmero de r\xF3tulos esperados:"),qe.forEach(a),K=g(l),C(R.$$.fragment,l),Q=g(l),X=p(l,"P",{});var ve=c(X);oe=n(ve,"Configure o modelo para treinamento com o m\xE9todo "),L=p(ve,"A",{href:!0,rel:!0});var He=c(L);ee=p(He,"CODE",{});var Ke=c(ee);W=n(Ke,"compile"),Ke.forEach(a),He.forEach(a),$e=n(ve,":"),ve.forEach(a),d=g(l),C(k.$$.fragment,l),te=g(l),B=p(l,"P",{});var je=c(B);re=n(je,"Chame o m\xE9todo "),J=p(je,"A",{href:!0,rel:!0});var Je=c(J);Z=p(Je,"CODE",{});var Ye=c(Z);ie=n(Ye,"fit"),Ye.forEach(a),Je.forEach(a),pe=n(je," para executar o fine-tuning do modelo:"),je.forEach(a),ce=g(l),C(ae.$$.fragment,l),this.h()},h(){w(_,"href","https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset"),w(_,"rel","nofollow"),w(L,"href","https://keras.io/api/models/model_training_apis/#compile-method"),w(L,"rel","nofollow"),w(J,"href","https://keras.io/api/models/model_training_apis/#fit-method"),w(J,"rel","nofollow")},m(l,E){m(l,s,E),t(s,f),t(s,o),t(o,u),t(s,b),t(s,_),t(_,v),t(v,z),t(s,x),t(s,j),t(j,A),t(s,I),m(l,Y,E),T(M,l,E),m(l,H,E),T(O,l,E),m(l,se,E),m(l,U,E),t(U,be),m(l,fe,E),T(F,l,E),m(l,ue,E),m(l,V,E),t(V,he),t(V,G),t(G,_e),t(V,N),m(l,K,E),T(R,l,E),m(l,Q,E),m(l,X,E),t(X,oe),t(X,L),t(L,ee),t(ee,W),t(X,$e),m(l,d,E),T(k,l,E),m(l,te,E),m(l,B,E),t(B,re),t(B,J),t(J,Z),t(Z,ie),t(B,pe),m(l,ce,E),T(ae,l,E),me=!0},p(l,E){const Ee={};E&2&&(Ee.$$scope={dirty:E,ctx:l}),O.$set(Ee)},i(l){me||(D(M.$$.fragment,l),D(O.$$.fragment,l),D(F.$$.fragment,l),D(R.$$.fragment,l),D(k.$$.fragment,l),D(ae.$$.fragment,l),me=!0)},o(l){y(M.$$.fragment,l),y(O.$$.fragment,l),y(F.$$.fragment,l),y(R.$$.fragment,l),y(k.$$.fragment,l),y(ae.$$.fragment,l),me=!1},d(l){l&&a(s),l&&a(Y),P(M,l),l&&a(H),P(O,l),l&&a(se),l&&a(U),l&&a(fe),P(F,l),l&&a(ue),l&&a(V),l&&a(K),P(R,l),l&&a(Q),l&&a(X),l&&a(d),P(k,l),l&&a(te),l&&a(B),l&&a(ce),P(ae,l)}}}function ss(S){let s,f;return s=new Ot({props:{$$slots:{default:[as]},$$scope:{ctx:S}}}),{c(){q(s.$$.fragment)},l(o){C(s.$$.fragment,o)},m(o,u){T(s,o,u),f=!0},p(o,u){const b={};u&2&&(b.$$scope={dirty:u,ctx:o}),s.$set(b)},i(o){f||(D(s.$$.fragment,o),f=!0)},o(o){y(s.$$.fragment,o),f=!1},d(o){P(s,o)}}}function os(S){let s,f,o,u,b,_,v,z;return{c(){s=i("p"),f=r("Para obter um exemplo mais aprofundado de como executar o fine-tuning de um modelo para classifica\xE7\xE3o de texto, d\xEA uma olhada nesse "),o=i("a"),u=r("notebook utilizando PyTorch"),b=r(" ou nesse "),_=i("a"),v=r("notebook utilizando TensorFlow"),z=r("."),this.h()},l(x){s=p(x,"P",{});var j=c(s);f=n(j,"Para obter um exemplo mais aprofundado de como executar o fine-tuning de um modelo para classifica\xE7\xE3o de texto, d\xEA uma olhada nesse "),o=p(j,"A",{href:!0,rel:!0});var A=c(o);u=n(A,"notebook utilizando PyTorch"),A.forEach(a),b=n(j," ou nesse "),_=p(j,"A",{href:!0,rel:!0});var I=c(_);v=n(I,"notebook utilizando TensorFlow"),I.forEach(a),z=n(j,"."),j.forEach(a),this.h()},h(){w(o,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb"),w(o,"rel","nofollow"),w(_,"href","https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb"),w(_,"rel","nofollow")},m(x,j){m(x,s,j),t(s,f),t(s,o),t(o,u),t(s,b),t(s,_),t(_,v),t(s,z)},d(x){x&&a(s)}}}function rs(S){let s,f,o,u,b,_,v,z,x,j,A,I,Y,M,H,O,se,U,be,fe,F,ue,V,he,G,_e,N,K,R,Q,X,oe,L,ee,W,$e,d,k,te,B,re,J,Z,ie,pe,ce,ae,me,l,E,Ee,Ue,Ve,de,ze,qe,Ge,ve,He,Ke,je,Je,Ye,ht,we,Ce,Xe,Fe,Ft,Ze,Mt,_t,Te,It,et,Nt,Bt,$t,Me,gt,De,Rt,tt,Lt,Wt,bt,Ie,vt,ge,Ut,Ne,at,Vt,Gt,st,Ht,Kt,ot,Jt,Yt,jt,Be,kt,ne,Qt,rt,Xt,Zt,nt,ea,ta,lt,aa,sa,it,oa,ra,Et,ye,wt,xe,Pe,pt,Re,na,ct,la,xt,Ae,zt,Se,qt;return _=new St({}),A=new Va({props:{id:"leNG9fN9FQU"}}),G=new ut({props:{$$slots:{default:[Ga]},$$scope:{ctx:S}}}),Q=new St({}),k=new le({props:{code:`from datasets import load_dataset
imdb = load_dataset("imdb")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>imdb = load_dataset(<span class="hljs-string">"imdb"</span>)`}}),Z=new le({props:{code:'imdb["test"][0]',highlighted:`<span class="hljs-meta">>>> </span>imdb[<span class="hljs-string">"test"</span>][<span class="hljs-number">0</span>]
{
<span class="hljs-string">"label"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"text"</span>: <span class="hljs-string">"I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clich\xE9d and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \\"Gene Roddenberry's Earth...\\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again."</span>,
}`}}),Fe=new St({}),Me=new le({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),Ie=new le({props:{code:`def preprocess_function(examples):
return tokenizer(examples["text"], truncation=True)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
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0 | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages | hf_public_repos/doc-build-dev/transformers/pr_18789/pt/_app/pages/tasks/token_classification.mdx-hf-doc-builder.js | import{S as ho,i as _o,s as go,e as r,k as _,w as x,t as n,M as $o,c as i,d as e,m as g,a as p,x as E,h as l,b as k,G as a,g as d,y as q,q as z,o as C,B as T,v as jo,L as fo}from"../../chunks/vendor-hf-doc-builder.js";import{T as he}from"../../chunks/Tip-hf-doc-builder.js";import{Y as mo}from"../../chunks/Youtube-hf-doc-builder.js";import{I as fe}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as as}from"../../chunks/CodeBlock-hf-doc-builder.js";import{F as uo,M as _e}from"../../chunks/Markdown-hf-doc-builder.js";function ko(P){let t,u,o,f,j;return{c(){t=r("p"),u=n("Consulte a "),o=r("a"),f=n("p\xE1gina de tarefas de classifica\xE7\xE3o de tokens"),j=n(" para obter mais informa\xE7\xF5es sobre outras formas de classifica\xE7\xE3o de tokens e seus modelos, conjuntos de dados e m\xE9tricas associadas."),this.h()},l($){t=i($,"P",{});var v=p(t);u=l(v,"Consulte a "),o=i(v,"A",{href:!0,rel:!0});var D=p(o);f=l(D,"p\xE1gina de tarefas de classifica\xE7\xE3o de tokens"),D.forEach(e),j=l(v," para obter mais informa\xE7\xF5es sobre outras formas de classifica\xE7\xE3o de tokens e seus modelos, conjuntos de dados e m\xE9tricas associadas."),v.forEach(e),this.h()},h(){k(o,"href","https://huggingface.co/tasks/token-classification"),k(o,"rel","nofollow")},m($,v){d($,t,v),a(t,u),a(t,o),a(o,f),a(t,j)},d($){$&&e(t)}}}function vo(P){let t,u;return t=new as({props:{code:`from transformers import DataCollatorForTokenClassification
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<span class="hljs-meta">>>> </span>data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)`}}),{c(){x(t.$$.fragment)},l(o){E(t.$$.fragment,o)},m(o,f){q(t,o,f),u=!0},p:fo,i(o){u||(z(t.$$.fragment,o),u=!0)},o(o){C(t.$$.fragment,o),u=!1},d(o){T(t,o)}}}function bo(P){let t,u;return t=new _e({props:{$$slots:{default:[vo]},$$scope:{ctx:P}}}),{c(){x(t.$$.fragment)},l(o){E(t.$$.fragment,o)},m(o,f){q(t,o,f),u=!0},p(o,f){const j={};f&2&&(j.$$scope={dirty:f,ctx:o}),t.$set(j)},i(o){u||(z(t.$$.fragment,o),u=!0)},o(o){C(t.$$.fragment,o),u=!1},d(o){T(t,o)}}}function wo(P){let t,u;return t=new as({props:{code:`from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForTokenClassification
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors=<span class="hljs-string">"tf"</span>)`}}),{c(){x(t.$$.fragment)},l(o){E(t.$$.fragment,o)},m(o,f){q(t,o,f),u=!0},p:fo,i(o){u||(z(t.$$.fragment,o),u=!0)},o(o){C(t.$$.fragment,o),u=!1},d(o){T(t,o)}}}function xo(P){let t,u;return t=new _e({props:{$$slots:{default:[wo]},$$scope:{ctx:P}}}),{c(){x(t.$$.fragment)},l(o){E(t.$$.fragment,o)},m(o,f){q(t,o,f),u=!0},p(o,f){const j={};f&2&&(j.$$scope={dirty:f,ctx:o}),t.$set(j)},i(o){u||(z(t.$$.fragment,o),u=!0)},o(o){C(t.$$.fragment,o),u=!1},d(o){T(t,o)}}}function Eo(P){let t,u,o,f,j,$,v,D;return{c(){t=r("p"),u=n("Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o "),o=r("code"),f=n("Trainer"),j=n(", d\xEA uma olhada no tutorial b\xE1sico "),$=r("a"),v=n("aqui"),D=n("!"),this.h()},l(A){t=i(A,"P",{});var w=p(t);u=l(w,"Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o "),o=i(w,"CODE",{});var O=p(o);f=l(O,"Trainer"),O.forEach(e),j=l(w,", d\xEA uma olhada no tutorial b\xE1sico "),$=i(w,"A",{href:!0});var L=p($);v=l(L,"aqui"),L.forEach(e),D=l(w,"!"),w.forEach(e),this.h()},h(){k($,"href","../training#finetune-with-trainer")},m(A,w){d(A,t,w),a(t,u),a(t,o),a(o,f),a(t,j),a(t,$),a($,v),a(t,D)},d(A){A&&e(t)}}}function qo(P){let t,u,o,f,j,$,v,D,A,w,O,L,G,N,Y,F,ns,U,$s,us,S,fs,W,hs,V,_s,B,K,I,J,Q,ls,R,es;return v=new as({props:{code:`from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=14)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">14</span>)`}}),A=new he({props:{$$slots:{default:[Eo]},$$scope:{ctx:P}}}),R=new as({props:{code:`training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=3,
weight_decay=0.01,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_wnut["train"],
eval_dataset=tokenized_wnut["test"],
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.train()`,highlighted:`<span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_wnut[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_wnut[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()`}}),{c(){t=r("p"),u=n("Carregue o DistilBERT com o "),o=r("code"),f=n("AutoModelForTokenClassification"),j=n(" junto com o n\xFAmero de r\xF3tulos esperados:"),$=_(),x(v.$$.fragment),D=_(),x(A.$$.fragment),w=_(),O=r("p"),L=n("Nesse ponto, restam apenas tr\xEAs passos:"),G=_(),N=r("ol"),Y=r("li"),F=n("Definir seus hiperpar\xE2metros de treinamento em "),ns=r("code"),U=n("TrainingArguments"),$s=n("."),us=_(),S=r("li"),fs=n("Passar os argumentos de treinamento para o "),W=r("code"),hs=n("Trainer"),V=n(" junto com o modelo, conjunto de dados, tokenizador e o data collator."),_s=_(),B=r("li"),K=n("Chamar a fun\xE7\xE3o "),I=r("code"),J=n("train()"),Q=n(" para executar o fine-tuning do seu modelo."),ls=_(),x(R.$$.fragment)},l(h){t=i(h,"P",{});var y=p(t);u=l(y,"Carregue o DistilBERT com o "),o=i(y,"CODE",{});var ts=p(o);f=l(ts,"AutoModelForTokenClassification"),ts.forEach(e),j=l(y," junto com o n\xFAmero de r\xF3tulos esperados:"),y.forEach(e),$=g(h),E(v.$$.fragment,h),D=g(h),E(A.$$.fragment,h),w=g(h),O=i(h,"P",{});var H=p(O);L=l(H,"Nesse ponto, restam apenas tr\xEAs passos:"),H.forEach(e),G=g(h),N=i(h,"OL",{});var X=p(N);Y=i(X,"LI",{});var M=p(Y);F=l(M,"Definir seus hiperpar\xE2metros de treinamento em "),ns=i(M,"CODE",{});var ks=p(ns);U=l(ks,"TrainingArguments"),ks.forEach(e),$s=l(M,"."),M.forEach(e),us=g(X),S=i(X,"LI",{});var Z=p(S);fs=l(Z,"Passar os argumentos de treinamento para o "),W=i(Z,"CODE",{});var ss=p(W);hs=l(ss,"Trainer"),ss.forEach(e),V=l(Z," junto com o modelo, conjunto de dados, tokenizador e o data collator."),Z.forEach(e),_s=g(X),B=i(X,"LI",{});var is=p(B);K=l(is,"Chamar a fun\xE7\xE3o "),I=i(is,"CODE",{});var os=p(I);J=l(os,"train()"),os.forEach(e),Q=l(is," para executar o fine-tuning do seu modelo."),is.forEach(e),X.forEach(e),ls=g(h),E(R.$$.fragment,h)},m(h,y){d(h,t,y),a(t,u),a(t,o),a(o,f),a(t,j),d(h,$,y),q(v,h,y),d(h,D,y),q(A,h,y),d(h,w,y),d(h,O,y),a(O,L),d(h,G,y),d(h,N,y),a(N,Y),a(Y,F),a(Y,ns),a(ns,U),a(Y,$s),a(N,us),a(N,S),a(S,fs),a(S,W),a(W,hs),a(S,V),a(N,_s),a(N,B),a(B,K),a(B,I),a(I,J),a(B,Q),d(h,ls,y),q(R,h,y),es=!0},p(h,y){const ts={};y&2&&(ts.$$scope={dirty:y,ctx:h}),A.$set(ts)},i(h){es||(z(v.$$.fragment,h),z(A.$$.fragment,h),z(R.$$.fragment,h),es=!0)},o(h){C(v.$$.fragment,h),C(A.$$.fragment,h),C(R.$$.fragment,h),es=!1},d(h){h&&e(t),h&&e($),T(v,h),h&&e(D),T(A,h),h&&e(w),h&&e(O),h&&e(G),h&&e(N),h&&e(ls),T(R,h)}}}function zo(P){let t,u;return t=new _e({props:{$$slots:{default:[qo]},$$scope:{ctx:P}}}),{c(){x(t.$$.fragment)},l(o){E(t.$$.fragment,o)},m(o,f){q(t,o,f),u=!0},p(o,f){const j={};f&2&&(j.$$scope={dirty:f,ctx:o}),t.$set(j)},i(o){u||(z(t.$$.fragment,o),u=!0)},o(o){C(t.$$.fragment,o),u=!1},d(o){T(t,o)}}}function Co(P){let t,u,o,f,j;return{c(){t=r("p"),u=n("Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o Keras, d\xEA uma olhada no tutorial b\xE1sico "),o=r("a"),f=n("aqui"),j=n("!"),this.h()},l($){t=i($,"P",{});var v=p(t);u=l(v,"Se voc\xEA n\xE3o estiver familiarizado com o fine-tuning de um modelo com o Keras, d\xEA uma olhada no tutorial b\xE1sico "),o=i(v,"A",{href:!0});var D=p(o);f=l(D,"aqui"),D.forEach(e),j=l(v,"!"),v.forEach(e),this.h()},h(){k(o,"href","training#finetune-with-keras")},m($,v){d($,t,v),a(t,u),a(t,o),a(o,f),a(t,j)},d($){$&&e(t)}}}function To(P){let t,u,o,f,j,$,v,D,A,w,O,L,G,N,Y,F,ns,U,$s,us,S,fs,W,hs,V,_s,B,K,I,J,Q,ls,R,es,h,y,ts,H,X,M,ks,Z,ss,is,os,Ds,ps,As;return N=new as({props:{code:`tf_train_set = tokenized_wnut["train"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_validation_set = tokenized_wnut["validation"].to_tf_dataset(
columns=["attention_mask", "input_ids", "labels"],
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)`,highlighted:`<span class="hljs-meta">>>> </span>tf_train_set = tokenized_wnut[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_wnut[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)`}}),F=new he({props:{$$slots:{default:[Co]},$$scope:{ctx:P}}}),S=new as({props:{code:`from transformers import create_optimizer
batch_size = 16
num_train_epochs = 3
num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs
optimizer, lr_schedule = create_optimizer(
init_lr=2e-5,
num_train_steps=num_train_steps,
weight_decay_rate=0.01,
num_warmup_steps=0,
)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_train_epochs = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span>num_train_steps = (<span class="hljs-built_in">len</span>(tokenized_wnut[<span class="hljs-string">"train"</span>]) // batch_size) * num_train_epochs
<span class="hljs-meta">>>> </span>optimizer, lr_schedule = create_optimizer(
<span class="hljs-meta">... </span> init_lr=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_train_steps=num_train_steps,
<span class="hljs-meta">... </span> weight_decay_rate=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> num_warmup_steps=<span class="hljs-number">0</span>,
<span class="hljs-meta">... </span>)`}}),I=new as({props:{code:`from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=2)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)`}}),H=new as({props:{code:`import tensorflow as tf
model.compile(optimizer=optimizer)`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)`}}),ps=new as({props:{code:"model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)",highlighted:'<span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)'}}),{c(){t=r("p"),u=n("Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato "),o=r("code"),f=n("tf.data.Dataset"),j=n(" com "),$=r("a"),v=r("code"),D=n("to_tf_dataset"),A=n(". Nessa execu\xE7\xE3o voc\xEA dever\xE1 especificar as entradas e r\xF3tulos (no par\xE2metro "),w=r("code"),O=n("columns"),L=n("), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:"),G=_(),x(N.$$.fragment),Y=_(),x(F.$$.fragment),ns=_(),U=r("p"),$s=n("Configure o otimizador e alguns hiperpar\xE2metros de treinamento:"),us=_(),x(S.$$.fragment),fs=_(),W=r("p"),hs=n("Carregue o DistilBERT com o "),V=r("code"),_s=n("TFAutoModelForTokenClassification"),B=n(" junto com o n\xFAmero de r\xF3tulos esperados:"),K=_(),x(I.$$.fragment),J=_(),Q=r("p"),ls=n("Configure o modelo para treinamento com o m\xE9todo "),R=r("a"),es=r("code"),h=n("compile"),y=n(":"),ts=_(),x(H.$$.fragment),X=_(),M=r("p"),ks=n("Chame o m\xE9todo "),Z=r("a"),ss=r("code"),is=n("fit"),os=n(" para executar o fine-tuning do modelo:"),Ds=_(),x(ps.$$.fragment),this.h()},l(c){t=i(c,"P",{});var b=p(t);u=l(b,"Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato "),o=i(b,"CODE",{});var cs=p(o);f=l(cs,"tf.data.Dataset"),cs.forEach(e),j=l(b," com "),$=i(b,"A",{href:!0,rel:!0});var Us=p($);v=i(Us,"CODE",{});var ds=p(v);D=l(ds,"to_tf_dataset"),ds.forEach(e),Us.forEach(e),A=l(b,". 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wnut = load_dataset("wnut_17")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>wnut = load_dataset(<span class="hljs-string">"wnut_17"</span>)`}}),ss=new as({props:{code:'wnut["train"][0]',highlighted:`<span class="hljs-meta">>>> </span>wnut[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'id'</span>: <span class="hljs-string">'0'</span>,
<span class="hljs-string">'ner_tags'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'tokens'</span>: [<span class="hljs-string">'@paulwalk'</span>, <span class="hljs-string">'It'</span>, <span class="hljs-string">"'s"</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'I'</span>, <span class="hljs-string">"'m"</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Empire'</span>, <span class="hljs-string">'State'</span>, <span class="hljs-string">'Building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'ESB'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>]
}`}}),cs=new as({props:{code:`label_list = wnut["train"].features[f"ner_tags"].feature.names
label_list`,highlighted:`<span class="hljs-meta">>>> </span>label_list = wnut[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">f"ner_tags"</span>].feature.names
<span class="hljs-meta">>>> </span>label_list
[
<span class="hljs-string">"O"</span>,
<span class="hljs-string">"B-corporation"</span>,
<span class="hljs-string">"I-corporation"</span>,
<span class="hljs-string">"B-creative-work"</span>,
<span class="hljs-string">"I-creative-work"</span>,
<span class="hljs-string">"B-group"</span>,
<span class="hljs-string">"I-group"</span>,
<span class="hljs-string">"B-location"</span>,
<span class="hljs-string">"I-location"</span>,
<span class="hljs-string">"B-person"</span>,
<span class="hljs-string">"I-person"</span>,
<span class="hljs-string">"B-product"</span>,
<span class="hljs-string">"I-product"</span>,
]`}}),Ws=new fe({}),Vs=new mo({props:{id:"iY2AZYdZAr0"}}),Hs=new as({props:{code:`from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)`}}),Ys=new as({props:{code:`tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
tokens`,highlighted:`<span class="hljs-meta">>>> </span>tokenized_input = tokenizer(example[<span class="hljs-string">"tokens"</span>], is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokens = tokenizer.convert_ids_to_tokens(tokenized_input[<span class="hljs-string">"input_ids"</span>])
<span class="hljs-meta">>>> </span>tokens
[<span class="hljs-string">'[CLS]'</span>, <span class="hljs-string">'@'</span>, <span class="hljs-string">'paul'</span>, <span class="hljs-string">'##walk'</span>, <span class="hljs-string">'it'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'s'</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'i'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'m'</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'empire'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'es'</span>, <span class="hljs-string">'##b'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'[SEP]'</span>]`}}),Js=new as({props:{code:`def tokenize_and_align_labels(examples):
tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
labels = []
for i, label in enumerate(examples[f"ner_tags"]):
word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
previous_word_idx = None
label_ids = []
for word_idx in word_ids: # Set the special tokens to -100.
if word_idx is None:
label_ids.append(-100)
elif word_idx != previous_word_idx: # Only label the first token of a given word.
label_ids.append(label[word_idx])
else:
label_ids.append(-100)
previous_word_idx = word_idx
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs`,highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_and_align_labels</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> tokenized_inputs = tokenizer(examples[<span class="hljs-string">"tokens"</span>], truncation=<span class="hljs-literal">True</span>, is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> labels = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(examples[<span class="hljs-string">f"ner_tags"</span>]):
<span class="hljs-meta">... </span> word_ids = tokenized_inputs.word_ids(batch_index=i) <span class="hljs-comment"># Map tokens to their respective word.</span>
<span class="hljs-meta">... </span> previous_word_idx = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> label_ids = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> word_idx <span class="hljs-keyword">in</span> word_ids: <span class="hljs-comment"># Set the special tokens to -100.</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> word_idx <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">elif</span> word_idx != previous_word_idx: <span class="hljs-comment"># Only label the first token of a given word.</span>
<span class="hljs-meta">... </span> label_ids.append(label[word_idx])
<span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> previous_word_idx = word_idx
<span class="hljs-meta">... </span> labels.append(label_ids)
<span class="hljs-meta">... </span> tokenized_inputs[<span class="hljs-string">"labels"</span>] = labels
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenized_inputs`}}),Xs=new as({props:{code:"tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)",highlighted:'<span class="hljs-meta">>>> </span>tokenized_wnut = wnut.<span class="hljs-built_in">map</span>(tokenize_and_align_labels, batched=<span class="hljs-literal">True</span>)'}}),Ns=new uo({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[xo],pytorch:[bo]},$$scope:{ctx:P}}}),sa=new fe({}),Ms=new uo({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Do],pytorch:[zo]},$$scope:{ctx:P}}}),Is=new he({props:{$$slots:{default:[Ao]},$$scope:{ctx:P}}}),{c(){t=r("meta"),u=_(),o=r("h1"),f=r("a"),j=r("span"),x($.$$.fragment),v=_(),D=r("span"),A=n("Classifica\xE7\xE3o de tokens"),w=_(),x(O.$$.fragment),L=_(),G=r("p"),N=n("A classifica\xE7\xE3o de tokens atribui um r\xF3tulo a tokens individuais em uma frase. Uma das tarefas de classifica\xE7\xE3o de tokens mais comuns \xE9 o Reconhecimento de Entidade Nomeada, tamb\xE9m chamada de NER (sigla em ingl\xEAs para Named Entity Recognition). O NER tenta encontrar um r\xF3tulo para cada entidade em uma frase, como uma pessoa, local ou organiza\xE7\xE3o."),Y=_(),F=r("p"),ns=n("Este guia mostrar\xE1 como realizar o fine-tuning do "),U=r("a"),$s=n("DistilBERT"),us=n(" no conjunto de dados "),S=r("a"),fs=n("WNUT 17"),W=n(" para detectar novas entidades."),hs=_(),x(V.$$.fragment),_s=_(),B=r("h2"),K=r("a"),I=r("span"),x(J.$$.fragment),Q=_(),ls=r("span"),R=n("Carregando o conjunto de dados WNUT 17"),es=_(),h=r("p"),y=n("Carregue o conjunto de dados WNUT 17 da biblioteca \u{1F917} Datasets:"),ts=_(),x(H.$$.fragment),X=_(),M=r("p"),ks=n("E d\xEA uma olhada em um exemplo:"),Z=_(),x(ss.$$.fragment),is=_(),os=r("p"),Ds=n("Cada n\xFAmero em "),ps=r("code"),As=n("ner_tags"),c=n(" representa uma entidade. Converta o n\xFAmero em um r\xF3tulo para obter mais informa\xE7\xF5es:"),b=_(),x(cs.$$.fragment),Us=_(),ds=r("p"),ta=n("O "),ys=r("code"),Ps=n("ner_tag"),oa=n(" descreve uma entidade, como uma organiza\xE7\xE3o, local ou pessoa. 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<h1 class="relative group"><a id="classificao-de-texto" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#classificao-de-texto"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Classificação de texto
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/leNG9fN9FQU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>A classificação de texto é uma tarefa comum de NLP que atribui um rótulo ou classe a um texto. Existem muitas aplicações práticas de classificação de texto amplamente utilizadas em produção por algumas das maiores empresas da atualidade. Uma das formas mais populares de classificação de texto é a análise de sentimento, que atribui um rótulo como positivo, negativo ou neutro a um texto.</p>
<p>Este guia mostrará como realizar o fine-tuning do <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">DistilBERT</a> no conjunto de dados <a href="https://huggingface.co/datasets/imdb" rel="nofollow">IMDb</a> para determinar se a crítica de filme é positiva ou negativa.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Consulte a <a href="https://huggingface.co/tasks/text-classification" rel="nofollow">página de tarefas de classificação de texto</a> para obter mais informações sobre outras formas de classificação de texto e seus modelos, conjuntos de dados e métricas associados.</p></div>
<h2 class="relative group"><a id="carregue-o-conjunto-de-dados-imdb" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#carregue-o-conjunto-de-dados-imdb"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Carregue o conjunto de dados IMDb
</span></h2>
<p>Carregue o conjunto de dados IMDb utilizando a biblioteca 🤗 Datasets:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>imdb = load_dataset(<span class="hljs-string">"imdb"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Em seguida, dê uma olhada em um exemplo:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>imdb[<span class="hljs-string">"test"</span>][<span class="hljs-number">0</span>]
{
<span class="hljs-string">"label"</span>: <span class="hljs-number">0</span>,
<span class="hljs-string">"text"</span>: <span class="hljs-string">"I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \"Gene Roddenberry's Earth...\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again."</span>,
}<!-- HTML_TAG_END --></pre></div>
<p>Existem dois campos neste dataset:</p>
<ul><li><code>text</code>: uma string contendo o texto da crítica do filme.</li>
<li><code>label</code>: um valor que pode ser <code>0</code> para uma crítica negativa ou <code>1</code> para uma crítica positiva.</li></ul>
<h2 class="relative group"><a id="prprocessamento-dos-dados" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#prprocessamento-dos-dados"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pré-processamento dos dados
</span></h2>
<p>Carregue o tokenizador do DistilBERT para processar o campo <code>text</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Crie uma função de pré-processamento para tokenizar o campo <code>text</code> e truncar as sequências para que não sejam maiores que o comprimento máximo de entrada do DistilBERT:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_function</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenizer(examples[<span class="hljs-string">"text"</span>], truncation=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use a função <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> do 🤗 Datasets para aplicar a função de pré-processamento em todo o conjunto de dados. Você pode acelerar a função <code>map</code> definindo <code>batched=True</code> para processar vários elementos do conjunto de dados de uma só vez:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->tokenized_imdb = imdb.<span class="hljs-built_in">map</span>(preprocess_function, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use o <code>DataCollatorWithPadding</code> para criar um batch de exemplos. Ele também <em>preencherá dinamicamente</em> seu texto até o comprimento do elemento mais longo em seu batch, para que os exemplos do batch tenham um comprimento uniforme. Embora seja possível preencher seu texto com a função <code>tokenizer</code> definindo <code>padding=True</code>, o preenchimento dinâmico utilizando um data collator é mais eficiente.</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding
<span class="hljs-meta">>>> </span>data_collator = DataCollatorWithPadding(tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorWithPadding
<span class="hljs-meta">>>> </span>data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=<span class="hljs-string">"tf"</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<h2 class="relative group"><a id="train" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#train"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Train
</span></h2>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
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<span>Pytorch</span></div>
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<p>Carregue o DistilBERT com <code>AutoModelForSequenceClassification</code> junto com o número de rótulos esperados:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Se você não estiver familiarizado com o fine-tuning de um modelo com o <code>Trainer</code>, dê uma olhada no tutorial básico <a href="../training#finetune-with-trainer">aqui</a>!</p></div>
<p>Nesse ponto, restam apenas três passos:</p>
<ol><li>Definir seus hiperparâmetros de treinamento em <code>TrainingArguments</code>.</li>
<li>Passar os argumentos de treinamento para o <code>Trainer</code> junto com o modelo, conjunto de dados, tokenizador e o data collator.</li>
<li>Chamar a função <code>train()</code> para executar o fine-tuning do seu modelo.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">5</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_imdb[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_imdb[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>O <code>Trainer</code> aplicará o preenchimento dinâmico por padrão quando você definir o argumento <code>tokenizer</code> dele. Nesse caso, você não precisa especificar um data collator explicitamente.</p></div></div></div>
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<span>TensorFlow</span></div>
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<p>Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato <code>tf.data.Dataset</code> com <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Nessa execução você deverá especificar as entradas e rótulos (no parâmetro <code>columns</code>), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = tokenized_imdb[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_imdb[<span class="hljs-string">"test"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"label"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Se você não estiver familiarizado com o fine-tuning de um modelo com o Keras, dê uma olhada no tutorial básico <a href="training#finetune-with-keras">aqui</a>!</p></div>
<p>Configure o otimizador e alguns hiperparâmetros de treinamento:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_epochs = <span class="hljs-number">5</span>
<span class="hljs-meta">>>> </span>batches_per_epoch = <span class="hljs-built_in">len</span>(tokenized_imdb[<span class="hljs-string">"train"</span>]) // batch_size
<span class="hljs-meta">>>> </span>total_train_steps = <span class="hljs-built_in">int</span>(batches_per_epoch * num_epochs)
<span class="hljs-meta">>>> </span>optimizer, schedule = create_optimizer(init_lr=<span class="hljs-number">2e-5</span>, num_warmup_steps=<span class="hljs-number">0</span>, num_train_steps=total_train_steps)<!-- HTML_TAG_END --></pre></div>
<p>Carregue o DistilBERT com <code>TFAutoModelForSequenceClassification</code> junto com o número de rótulos esperados:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure o modelo para treinamento com o método <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Chame o método <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> para executar o fine-tuning do modelo:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Para obter um exemplo mais aprofundado de como executar o fine-tuning de um modelo para classificação de texto, dê uma olhada nesse <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb" rel="nofollow">notebook utilizando PyTorch</a> ou nesse <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb" rel="nofollow">notebook utilizando TensorFlow</a>.</p></div>
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<h1 class="relative group"><a id="classificao-de-tokens" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#classificao-de-tokens"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Classificação de tokens
</span></h1>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/wVHdVlPScxA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>A classificação de tokens atribui um rótulo a tokens individuais em uma frase. Uma das tarefas de classificação de tokens mais comuns é o Reconhecimento de Entidade Nomeada, também chamada de NER (sigla em inglês para Named Entity Recognition). O NER tenta encontrar um rótulo para cada entidade em uma frase, como uma pessoa, local ou organização.</p>
<p>Este guia mostrará como realizar o fine-tuning do <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">DistilBERT</a> no conjunto de dados <a href="https://huggingface.co/datasets/wnut_17" rel="nofollow">WNUT 17</a> para detectar novas entidades.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Consulte a <a href="https://huggingface.co/tasks/token-classification" rel="nofollow">página de tarefas de classificação de tokens</a> para obter mais informações sobre outras formas de classificação de tokens e seus modelos, conjuntos de dados e métricas associadas.</p></div>
<h2 class="relative group"><a id="carregando-o-conjunto-de-dados-wnut-17" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#carregando-o-conjunto-de-dados-wnut-17"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Carregando o conjunto de dados WNUT 17
</span></h2>
<p>Carregue o conjunto de dados WNUT 17 da biblioteca 🤗 Datasets:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span>wnut = load_dataset(<span class="hljs-string">"wnut_17"</span>)<!-- HTML_TAG_END --></pre></div>
<p>E dê uma olhada em um exemplo:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>wnut[<span class="hljs-string">"train"</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">'id'</span>: <span class="hljs-string">'0'</span>,
<span class="hljs-string">'ner_tags'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>, <span class="hljs-number">0</span>, <span class="hljs-number">7</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'tokens'</span>: [<span class="hljs-string">'@paulwalk'</span>, <span class="hljs-string">'It'</span>, <span class="hljs-string">"'s"</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'I'</span>, <span class="hljs-string">"'m"</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Empire'</span>, <span class="hljs-string">'State'</span>, <span class="hljs-string">'Building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'ESB'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'Pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>]
}<!-- HTML_TAG_END --></pre></div>
<p>Cada número em <code>ner_tags</code> representa uma entidade. Converta o número em um rótulo para obter mais informações:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>label_list = wnut[<span class="hljs-string">"train"</span>].features[<span class="hljs-string">f"ner_tags"</span>].feature.names
<span class="hljs-meta">>>> </span>label_list
[
<span class="hljs-string">"O"</span>,
<span class="hljs-string">"B-corporation"</span>,
<span class="hljs-string">"I-corporation"</span>,
<span class="hljs-string">"B-creative-work"</span>,
<span class="hljs-string">"I-creative-work"</span>,
<span class="hljs-string">"B-group"</span>,
<span class="hljs-string">"I-group"</span>,
<span class="hljs-string">"B-location"</span>,
<span class="hljs-string">"I-location"</span>,
<span class="hljs-string">"B-person"</span>,
<span class="hljs-string">"I-person"</span>,
<span class="hljs-string">"B-product"</span>,
<span class="hljs-string">"I-product"</span>,
]<!-- HTML_TAG_END --></pre></div>
<p>O <code>ner_tag</code> descreve uma entidade, como uma organização, local ou pessoa. A letra que prefixa cada <code>ner_tag</code> indica a posição do token da entidade:</p>
<ul><li><code>B-</code> indica o início de uma entidade.</li>
<li><code>I-</code> indica que um token está contido dentro da mesma entidade (por exemplo, o token <code>State</code> pode fazer parte de uma entidade como <code>Empire State Building</code>).</li>
<li><code>0</code> indica que o token não corresponde a nenhuma entidade.</li></ul>
<h2 class="relative group"><a id="prprocessamento" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#prprocessamento"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pré-processamento
</span></h2>
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<p>Carregue o tokenizer do DistilBERT para processar os <code>tokens</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Como a entrada já foi dividida em palavras, defina <code>is_split_into_words=True</code> para tokenizar as palavras em subpalavras:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_input = tokenizer(example[<span class="hljs-string">"tokens"</span>], is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>tokens = tokenizer.convert_ids_to_tokens(tokenized_input[<span class="hljs-string">"input_ids"</span>])
<span class="hljs-meta">>>> </span>tokens
[<span class="hljs-string">'[CLS]'</span>, <span class="hljs-string">'@'</span>, <span class="hljs-string">'paul'</span>, <span class="hljs-string">'##walk'</span>, <span class="hljs-string">'it'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'s'</span>, <span class="hljs-string">'the'</span>, <span class="hljs-string">'view'</span>, <span class="hljs-string">'from'</span>, <span class="hljs-string">'where'</span>, <span class="hljs-string">'i'</span>, <span class="hljs-string">"'"</span>, <span class="hljs-string">'m'</span>, <span class="hljs-string">'living'</span>, <span class="hljs-string">'for'</span>, <span class="hljs-string">'two'</span>, <span class="hljs-string">'weeks'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'empire'</span>, <span class="hljs-string">'state'</span>, <span class="hljs-string">'building'</span>, <span class="hljs-string">'='</span>, <span class="hljs-string">'es'</span>, <span class="hljs-string">'##b'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'pretty'</span>, <span class="hljs-string">'bad'</span>, <span class="hljs-string">'storm'</span>, <span class="hljs-string">'here'</span>, <span class="hljs-string">'last'</span>, <span class="hljs-string">'evening'</span>, <span class="hljs-string">'.'</span>, <span class="hljs-string">'[SEP]'</span>]<!-- HTML_TAG_END --></pre></div>
<p>Ao adicionar os tokens especiais <code>[CLS]</code> e <code>[SEP]</code> e a tokenização de subpalavras uma incompatibilidade é gerada entre a entrada e os rótulos. Uma única palavra correspondente a um único rótulo pode ser dividida em duas subpalavras. Você precisará realinhar os tokens e os rótulos da seguinte forma:</p>
<ol><li>Mapeie todos os tokens para a palavra correspondente com o método <a href="https://huggingface.co/docs/tokenizers/python/latest/api/reference.html#tokenizers.Encoding.word_ids" rel="nofollow"><code>word_ids</code></a>.</li>
<li>Atribuindo o rótulo <code>-100</code> aos tokens especiais <code>[CLS]</code> e <code>[SEP]</code> para que a função de loss do PyTorch ignore eles.</li>
<li>Rotular apenas o primeiro token de uma determinada palavra. Atribuindo <code>-100</code> a outros subtokens da mesma palavra.</li></ol>
<p>Aqui está como você pode criar uma função para realinhar os tokens e rótulos e truncar sequências para não serem maiores que o comprimento máximo de entrada do DistilBERT:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">tokenize_and_align_labels</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span> tokenized_inputs = tokenizer(examples[<span class="hljs-string">"tokens"</span>], truncation=<span class="hljs-literal">True</span>, is_split_into_words=<span class="hljs-literal">True</span>)
<span class="hljs-meta">... </span> labels = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(examples[<span class="hljs-string">f"ner_tags"</span>]):
<span class="hljs-meta">... </span> word_ids = tokenized_inputs.word_ids(batch_index=i) <span class="hljs-comment"># Map tokens to their respective word.</span>
<span class="hljs-meta">... </span> previous_word_idx = <span class="hljs-literal">None</span>
<span class="hljs-meta">... </span> label_ids = []
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> word_idx <span class="hljs-keyword">in</span> word_ids: <span class="hljs-comment"># Set the special tokens to -100.</span>
<span class="hljs-meta">... </span> <span class="hljs-keyword">if</span> word_idx <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> <span class="hljs-keyword">elif</span> word_idx != previous_word_idx: <span class="hljs-comment"># Only label the first token of a given word.</span>
<span class="hljs-meta">... </span> label_ids.append(label[word_idx])
<span class="hljs-meta">... </span> <span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span> label_ids.append(-<span class="hljs-number">100</span>)
<span class="hljs-meta">... </span> previous_word_idx = word_idx
<span class="hljs-meta">... </span> labels.append(label_ids)
<span class="hljs-meta">... </span> tokenized_inputs[<span class="hljs-string">"labels"</span>] = labels
<span class="hljs-meta">... </span> <span class="hljs-keyword">return</span> tokenized_inputs<!-- HTML_TAG_END --></pre></div>
<p>Use a função <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map" rel="nofollow"><code>map</code></a> do 🤗 Datasets para tokenizar e alinhar os rótulos em todo o conjunto de dados. Você pode acelerar a função <code>map</code> configurando <code>batched=True</code> para processar vários elementos do conjunto de dados de uma só vez:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenized_wnut = wnut.<span class="hljs-built_in">map</span>(tokenize_and_align_labels, batched=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Use o <code>DataCollatorForTokenClassification</code> para criar um batch de exemplos. Ele também <em>preencherá dinamicamente</em> seu texto e rótulos para o comprimento do elemento mais longo em seu batch, para que tenham um comprimento uniforme. Embora seja possível preencher seu texto na função <code>tokenizer</code> configurando <code>padding=True</code>, o preenchimento dinâmico é mais eficiente.</p>
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<span>Pytorch</span></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForTokenClassification
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div></div></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DataCollatorForTokenClassification
<span class="hljs-meta">>>> </span>data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors=<span class="hljs-string">"tf"</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<h2 class="relative group"><a id="treinamento" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#treinamento"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Treinamento
</span></h2>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
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<span>Pytorch</span></div>
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<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>Carregue o DistilBERT com o <code>AutoModelForTokenClassification</code> junto com o número de rótulos esperados:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification, TrainingArguments, Trainer
<span class="hljs-meta">>>> </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">14</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Se você não estiver familiarizado com o fine-tuning de um modelo com o <code>Trainer</code>, dê uma olhada no tutorial básico <a href="../training#finetune-with-trainer">aqui</a>!</p></div>
<p>Nesse ponto, restam apenas três passos:</p>
<ol><li>Definir seus hiperparâmetros de treinamento em <code>TrainingArguments</code>.</li>
<li>Passar os argumentos de treinamento para o <code>Trainer</code> junto com o modelo, conjunto de dados, tokenizador e o data collator.</li>
<li>Chamar a função <code>train()</code> para executar o fine-tuning do seu modelo.</li></ol>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./results"</span>,
<span class="hljs-meta">... </span> evaluation_strategy=<span class="hljs-string">"epoch"</span>,
<span class="hljs-meta">... </span> learning_rate=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> per_device_train_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> per_device_eval_batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> num_train_epochs=<span class="hljs-number">3</span>,
<span class="hljs-meta">... </span> weight_decay=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=tokenized_wnut[<span class="hljs-string">"train"</span>],
<span class="hljs-meta">... </span> eval_dataset=tokenized_wnut[<span class="hljs-string">"test"</span>],
<span class="hljs-meta">... </span> tokenizer=tokenizer,
<span class="hljs-meta">... </span> data_collator=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>trainer.train()<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<div class="framework-content">
<p>Para executar o fine-tuning de um modelo no TensorFlow, comece convertendo seu conjunto de dados para o formato <code>tf.data.Dataset</code> com <a href="https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset" rel="nofollow"><code>to_tf_dataset</code></a>. Nessa execução você deverá especificar as entradas e rótulos (no parâmetro <code>columns</code>), se deseja embaralhar o conjunto de dados, o tamanho do batch e o data collator:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_train_set = tokenized_wnut[<span class="hljs-string">"train"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>tf_validation_set = tokenized_wnut[<span class="hljs-string">"validation"</span>].to_tf_dataset(
<span class="hljs-meta">... </span> columns=[<span class="hljs-string">"attention_mask"</span>, <span class="hljs-string">"input_ids"</span>, <span class="hljs-string">"labels"</span>],
<span class="hljs-meta">... </span> shuffle=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span> batch_size=<span class="hljs-number">16</span>,
<span class="hljs-meta">... </span> collate_fn=data_collator,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Se você não estiver familiarizado com o fine-tuning de um modelo com o Keras, dê uma olhada no tutorial básico <a href="training#finetune-with-keras">aqui</a>!</p></div>
<p>Configure o otimizador e alguns hiperparâmetros de treinamento:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> create_optimizer
<span class="hljs-meta">>>> </span>batch_size = <span class="hljs-number">16</span>
<span class="hljs-meta">>>> </span>num_train_epochs = <span class="hljs-number">3</span>
<span class="hljs-meta">>>> </span>num_train_steps = (<span class="hljs-built_in">len</span>(tokenized_wnut[<span class="hljs-string">"train"</span>]) // batch_size) * num_train_epochs
<span class="hljs-meta">>>> </span>optimizer, lr_schedule = create_optimizer(
<span class="hljs-meta">... </span> init_lr=<span class="hljs-number">2e-5</span>,
<span class="hljs-meta">... </span> num_train_steps=num_train_steps,
<span class="hljs-meta">... </span> weight_decay_rate=<span class="hljs-number">0.01</span>,
<span class="hljs-meta">... </span> num_warmup_steps=<span class="hljs-number">0</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Carregue o DistilBERT com o <code>TFAutoModelForTokenClassification</code> junto com o número de rótulos esperados:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, num_labels=<span class="hljs-number">2</span>)<!-- HTML_TAG_END --></pre></div>
<p>Configure o modelo para treinamento com o método <a href="https://keras.io/api/models/model_training_apis/#compile-method" rel="nofollow"><code>compile</code></a>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>model.<span class="hljs-built_in">compile</span>(optimizer=optimizer)<!-- HTML_TAG_END --></pre></div>
<p>Chame o método <a href="https://keras.io/api/models/model_training_apis/#fit-method" rel="nofollow"><code>fit</code></a> para executar o fine-tuning do modelo:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=<span class="hljs-number">3</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Para obter um exemplo mais aprofundado de como executar o fine-tuning de um modelo para classificação de tokens, dê uma olhada nesse <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb" rel="nofollow">notebook utilizando PyTorch</a> ou nesse <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb" rel="nofollow">notebook utilizando TensorFlow</a>.</p></div>
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<h1 class="relative group"><a id="installation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#installation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Installation
</span></h1>
<p>Installieren Sie 🤗 Transformers für die Deep-Learning-Bibliothek, mit der Sie arbeiten, richten Sie Ihren Cache ein und konfigurieren Sie 🤗 Transformers optional für den Offline-Betrieb.</p>
<p>🤗 Transformers wurde unter Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, und Flax getestet. Folgen Sie den Installationsanweisungen unten für die von Ihnen verwendete Deep-Learning-Bibliothek:</p>
<ul><li><a href="https://pytorch.org/get-started/locally/" rel="nofollow">PyTorch</a> installation instructions.</li>
<li><a href="https://www.tensorflow.org/install/pip" rel="nofollow">TensorFlow 2.0</a> installation instructions.</li>
<li><a href="https://flax.readthedocs.io/en/latest/" rel="nofollow">Flax</a> installation instructions.</li></ul>
<h2 class="relative group"><a id="installation-mit-pip" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#installation-mit-pip"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Installation mit pip
</span></h2>
<p>Sie sollten 🤗 Transformers in einer <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">virtuellen Umgebung</a> installieren. Wenn Sie mit virtuellen Python-Umgebungen nicht vertraut sind, werfen Sie einen Blick auf diese <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">Anleitung</a>. Eine virtuelle Umgebung macht es einfacher, verschiedene Projekte zu verwalten und Kompatibilitätsprobleme zwischen Abhängigkeiten zu vermeiden.</p>
<p>Beginnen wir mit der Erstellung einer virtuellen Umgebung in Ihrem Projektverzeichnis:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->python -m venv .<span class="hljs-built_in">env</span><!-- HTML_TAG_END --></pre></div>
<p>Aktivieren wir die virtuelle Umgebung. Unter Linux und MacOs:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">source</span> .<span class="hljs-built_in">env</span>/bin/activate<!-- HTML_TAG_END --></pre></div>
<p>Aktivieren wir die virtuelle Umgebung unter Windows</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->.<span class="hljs-built_in">env</span>/Scripts/activate<!-- HTML_TAG_END --></pre></div>
<p>Jetzt können wir die 🤗 Transformers mit dem folgenden Befehl installieren:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install transformers<!-- HTML_TAG_END --></pre></div>
<p>Bei reiner CPU-Unterstützung können wir 🤗 Transformers und eine Deep-Learning-Bibliothek bequem in einer Zeile installieren. Installieren wir zum Beispiel 🤗 Transformers und PyTorch mit:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install transformers[torch]<!-- HTML_TAG_END --></pre></div>
<p>🤗 Transformers und TensorFlow 2.0:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->pip install transformers[tf-cpu]<!-- HTML_TAG_END --></pre></div>
<p>🤗 Transformers und Flax:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install transformers[flax]<!-- HTML_TAG_END --></pre></div>
<p>Überprüfen wir abschließend, ob 🤗 Transformers ordnungsgemäß installiert wurde, indem wir den folgenden Befehl ausführen. Es wird ein vortrainiertes Modell heruntergeladen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->python -c <span class="hljs-string">"from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"</span><!-- HTML_TAG_END --></pre></div>
<p>Dann wird die Kategorie und die Wahrscheinlichkeit ausgegeben:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: 0.9998704791069031}]<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="installation-aus-dem-code" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#installation-aus-dem-code"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Installation aus dem Code
</span></h2>
<p>Installieren wir 🤗 Transformers aus dem Quellcode mit dem folgenden Befehl:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install git+https://github.com/huggingface/transformers<!-- HTML_TAG_END --></pre></div>
<p>Dieser Befehl installiert die aktuelle <code>main</code> Version und nicht die neueste <code>stable</code> Version. Die <code>main</code>-Version ist nützlich, um mit den neuesten Entwicklungen Schritt zu halten. Zum Beispiel, wenn ein Fehler seit der letzten offiziellen Version behoben wurde, aber eine neue Version noch nicht veröffentlicht wurde. Das bedeutet jedoch, dass die “Hauptversion” nicht immer stabil ist. Wir bemühen uns, die Hauptversion einsatzbereit zu halten, und die meisten Probleme werden normalerweise innerhalb weniger Stunden oder eines Tages behoben. Wenn Sie auf ein Problem stoßen, öffnen Sie bitte ein [Issue] (<a href="https://github.com/huggingface/transformers/issues" rel="nofollow">https://github.com/huggingface/transformers/issues</a>), damit wir es noch schneller beheben können!</p>
<p>Überprüfen wir, ob 🤗 Transformers richtig installiert wurde, indem Sie den folgenden Befehl ausführen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->python -c <span class="hljs-string">"from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"</span><!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="editierbare-installation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#editierbare-installation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Editierbare Installation
</span></h2>
<p>Sie benötigen eine bearbeitbare Installation, wenn Sie:</p>
<ul><li>die “Haupt”-Version des Quellcodes verwenden möchten.</li>
<li>Zu 🤗 Transformers beitragen und Änderungen am Code testen wollen.</li></ul>
<p>Klonen Sie das Repository und installieren 🤗 Transformers mit den folgenden Befehlen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->git <span class="hljs-built_in">clone</span> https://github.com/huggingface/transformers.git
<span class="hljs-built_in">cd</span> transformers
pip install -e .<!-- HTML_TAG_END --></pre></div>
<p>Diese Befehle verknüpfen den Ordner, in den Sie das Repository geklont haben, mit den Pfaden Ihrer Python-Bibliotheken. Python wird nun in dem Ordner suchen, in den Sie geklont haben, zusätzlich zu den normalen Bibliothekspfaden. Wenn zum Beispiel Ihre Python-Pakete normalerweise in <code>~/anaconda3/envs/main/lib/python3.7/site-packages/</code> installiert sind, wird Python auch den Ordner durchsuchen, in den Sie geklont haben: <code>~/transformers/</code>.</p>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>Sie müssen den Ordner <code>transformers</code> behalten, wenn Sie die Bibliothek weiter verwenden wollen.</p></div>
<p>Jetzt können Sie Ihren Klon mit dem folgenden Befehl ganz einfach auf die neueste Version von 🤗 Transformers aktualisieren:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-built_in">cd</span> ~/transformers/
git pull<!-- HTML_TAG_END --></pre></div>
<p>Ihre Python-Umgebung wird beim nächsten Ausführen die <code>main</code>-Version von 🤗 Transformers finden.</p>
<h2 class="relative group"><a id="installation-mit-conda" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#installation-mit-conda"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Installation mit conda
</span></h2>
<p>Installation von dem conda Kanal <code>huggingface</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->conda install -c huggingface transformers<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="cache-einrichtung" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#cache-einrichtung"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Cache Einrichtung
</span></h2>
<p>Vorgefertigte Modelle werden heruntergeladen und lokal zwischengespeichert unter: <code>~/.cache/huggingface/hub</code>. Dies ist das Standardverzeichnis, das durch die Shell-Umgebungsvariable “TRANSFORMERS_CACHE” vorgegeben ist. Unter Windows wird das Standardverzeichnis durch <code>C:\Benutzer\Benutzername\.cache\huggingface\hub</code> angegeben. Sie können die unten aufgeführten Shell-Umgebungsvariablen - in der Reihenfolge ihrer Priorität - ändern, um ein anderes Cache-Verzeichnis anzugeben:</p>
<ol><li>Shell-Umgebungsvariable (Standard): <code>HUGGINGFACE_HUB_CACHE</code> oder <code>TRANSFORMERS_CACHE</code>.</li>
<li>Shell-Umgebungsvariable: <code>HF_HOME</code>.</li>
<li>Shell-Umgebungsvariable: <code>XDG_CACHE_HOME</code> + <code>/huggingface</code>.</li></ol>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Transformers verwendet die Shell-Umgebungsvariablen <code>PYTORCH_TRANSFORMERS_CACHE</code> oder <code>PYTORCH_PRETRAINED_BERT_CACHE</code>, wenn Sie von einer früheren Iteration dieser Bibliothek kommen und diese Umgebungsvariablen gesetzt haben, sofern Sie nicht die Shell-Umgebungsvariable <code>TRANSFORMERS_CACHE</code> angeben.</p></div>
<h2 class="relative group"><a id="offline-modus" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#offline-modus"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Offline Modus
</span></h2>
<p>Transformers ist in der Lage, in einer Firewall- oder Offline-Umgebung zu laufen, indem es nur lokale Dateien verwendet. Setzen Sie die Umgebungsvariable <code>TRANSFORMERS_OFFLINE=1</code>, um dieses Verhalten zu aktivieren.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Fügen sie <a href="https://huggingface.co/docs/datasets/" rel="nofollow">🤗 Datasets</a> zu Ihrem Offline-Trainingsworkflow hinzufügen, indem Sie die Umgebungsvariable <code>HF_DATASETS_OFFLINE=1</code> setzen.</p></div>
<p>So würden Sie beispielsweise ein Programm in einem normalen Netzwerk mit einer Firewall für externe Instanzen mit dem folgenden Befehl ausführen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...<!-- HTML_TAG_END --></pre></div>
<p>Führen Sie das gleiche Programm in einer Offline-Instanz mit aus:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START -->HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...<!-- HTML_TAG_END --></pre></div>
<p>Das Skript sollte nun laufen, ohne sich aufzuhängen oder eine Zeitüberschreitung abzuwarten, da es weiß, dass es nur nach lokalen Dateien suchen soll.</p>
<h3 class="relative group"><a id="abrufen-von-modellen-und-tokenizern-zur-offlineverwendung" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#abrufen-von-modellen-und-tokenizern-zur-offlineverwendung"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Abrufen von Modellen und Tokenizern zur Offline-Verwendung
</span></h3>
<p>Eine andere Möglichkeit, 🤗 Transformers offline zu verwenden, besteht darin, die Dateien im Voraus herunterzuladen und dann auf ihren lokalen Pfad zu verweisen, wenn Sie sie offline verwenden müssen. Es gibt drei Möglichkeiten, dies zu tun:</p>
<ul><li><p>Laden Sie eine Datei über die Benutzeroberfläche des <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> herunter, indem Sie auf das ↓-Symbol klicken.</p>
<p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/download-icon.png" alt="download-icon"></p></li>
<li><p>Verwenden Sie den [PreTrainedModel.from_pretrained] und [PreTrainedModel.save_pretrained] Workflow:</p>
<ol><li><p>Laden Sie Ihre Dateien im Voraus mit <code>PreTrainedModel.from_pretrained()</code> herunter:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bigscience/T0_3B"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">"bigscience/T0_3B"</span>)<!-- HTML_TAG_END --></pre></div></li>
<li><p>Speichern Sie Ihre Dateien in einem bestimmten Verzeichnis mit <code>PreTrainedModel.save_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenizer.save_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)
<span class="hljs-meta">>>> </span>model.save_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)<!-- HTML_TAG_END --></pre></div></li>
<li><p>Wenn Sie nun offline sind, laden Sie Ihre Dateien mit <code>PreTrainedModel.from_pretrained()</code> aus dem bestimmten Verzeichnis:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)
<span class="hljs-meta">>>> </span>model = AutoModel.from_pretrained(<span class="hljs-string">"./your/path/bigscience_t0"</span>)<!-- HTML_TAG_END --></pre></div></li></ol></li>
<li><p>Programmatisches Herunterladen von Dateien mit der <a href="https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub" rel="nofollow">huggingface_hub</a> Bibliothek:</p>
<ol><li><p>Installieren Sie die “huggingface_hub”-Bibliothek in Ihrer virtuellen Umgebung:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->python -m pip install huggingface_hub<!-- HTML_TAG_END --></pre></div></li>
<li><p>Verwenden Sie die Funktion <a href="https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub" rel="nofollow"><code>hf_hub_download</code></a>, um eine Datei in einen bestimmten Pfad herunterzuladen. Der folgende Befehl lädt zum Beispiel die Datei “config.json” aus dem Modell <a href="https://huggingface.co/bigscience/T0_3B" rel="nofollow">T0</a> in den gewünschten Pfad herunter:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download
<span class="hljs-meta">>>> </span>hf_hub_download(repo_id=<span class="hljs-string">"bigscience/T0_3B"</span>, filename=<span class="hljs-string">"config.json"</span>, cache_dir=<span class="hljs-string">"./your/path/bigscience_t0"</span>)<!-- HTML_TAG_END --></pre></div></li></ol></li></ul>
<p>Sobald Ihre Datei heruntergeladen und lokal zwischengespeichert ist, geben Sie den lokalen Pfad an, um sie zu laden und zu verwenden:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig
<span class="hljs-meta">>>> </span>config = AutoConfig.from_pretrained(<span class="hljs-string">"./your/path/bigscience_t0/config.json"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Weitere Informationen zum Herunterladen von Dateien, die auf dem Hub gespeichert sind, finden Sie im Abschnitt [Wie man Dateien vom Hub herunterlädt] (<a href="https://huggingface.co/docs/hub/how-to-downstream" rel="nofollow">https://huggingface.co/docs/hub/how-to-downstream</a>).</p></div>
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| 492 |
0 | hf_public_repos/doc-build-dev/transformers/pr_18789 | hf_public_repos/doc-build-dev/transformers/pr_18789/de/_toctree.yml | - sections:
- local: index
title: 🤗 Transformers
- local: quicktour
title: Schnellstart
- local: installation
title: Installation
title: Erste Schritte
- sections:
- local: pipeline_tutorial
title: Pipelines für Inferenzen
- local: autoclass_tutorial
title: Laden von vortrainierten Instanzen mit einer AutoClass
- local: preprocessing
title: Vorverarbeiten
- local: training
title: Optimierung eines vortrainierten Modells
- local: accelerate
title: Verteiltes Training mit 🤗 Accelerate
- local: model_sharing
title: Ein Modell teilen
title: Tutorials
| 493 |
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<h1 class="relative group"><a id="vortrainierte-instanzen-mit-einer-autoclass-laden" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#vortrainierte-instanzen-mit-einer-autoclass-laden"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Vortrainierte Instanzen mit einer AutoClass laden
</span></h1>
<p>Bei so vielen verschiedenen Transformator-Architekturen kann es eine Herausforderung sein, eine für Ihren Checkpoint zu erstellen. Als Teil der 🤗 Transformers Kernphilosophie, die Bibliothek leicht, einfach und flexibel nutzbar zu machen, leitet eine <code>AutoClass</code> automatisch die richtige Architektur aus einem gegebenen Checkpoint ab und lädt sie. Mit der Methode <code>from_pretrained()</code> kann man schnell ein vortrainiertes Modell für eine beliebige Architektur laden, so dass man keine Zeit und Ressourcen aufwenden muss, um ein Modell von Grund auf zu trainieren. Die Erstellung dieser Art von Checkpoint-agnostischem Code bedeutet, dass Ihr Code, wenn er für einen Checkpoint funktioniert, auch mit einem anderen Checkpoint funktionieren wird - solange er für eine ähnliche Aufgabe trainiert wurde - selbst wenn die Architektur unterschiedlich ist.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Denken Sie daran, dass sich die Architektur auf das Skelett des Modells bezieht und die Checkpoints die Gewichte für eine bestimmte Architektur sind. Zum Beispiel ist <a href="https://huggingface.co/bert-base-uncased" rel="nofollow">BERT</a> eine Architektur, während <code>bert-base-uncased</code> ein Checkpoint ist. Modell ist ein allgemeiner Begriff, der entweder Architektur oder Prüfpunkt bedeuten kann.</p></div>
<p>In dieser Anleitung lernen Sie, wie man:</p>
<ul><li>Einen vortrainierten Tokenizer lädt.</li>
<li>Einen vortrainierten Merkmalsextraktor lädt.</li>
<li>Einen vortrainierten Prozessor lädt.</li>
<li>Ein vortrainiertes Modell lädt.</li></ul>
<h2 class="relative group"><a id="autotokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#autotokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoTokenizer
</span></h2>
<p>Nahezu jede NLP-Aufgabe beginnt mit einem Tokenizer. Ein Tokenizer wandelt Ihre Eingabe in ein Format um, das vom Modell verarbeitet werden kann.</p>
<p>Laden Sie einen Tokenizer mit <code>AutoTokenizer.from_pretrained()</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"bert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Dann tokenisieren Sie Ihre Eingabe wie unten gezeigt:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>sequence = <span class="hljs-string">"In a hole in the ground there lived a hobbit."</span>
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(tokenizer(sequence))
{<span class="hljs-string">'input_ids'</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">1999</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">4920</span>, <span class="hljs-number">1999</span>, <span class="hljs-number">1996</span>, <span class="hljs-number">2598</span>, <span class="hljs-number">2045</span>, <span class="hljs-number">2973</span>, <span class="hljs-number">1037</span>, <span class="hljs-number">7570</span>, <span class="hljs-number">10322</span>, <span class="hljs-number">4183</span>, <span class="hljs-number">1012</span>, <span class="hljs-number">102</span>],
<span class="hljs-string">'token_type_ids'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'attention_mask'</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]}<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="autofeatureextractor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#autofeatureextractor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoFeatureExtractor
</span></h2>
<p>Für Audio- und Bildverarbeitungsaufgaben verarbeitet ein Merkmalsextraktor das Audiosignal oder Bild in das richtige Eingabeformat.</p>
<p>Laden Sie einen Merkmalsextraktor mit <code>AutoFeatureExtractor.from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor
<span class="hljs-meta">>>> </span>feature_extractor = AutoFeatureExtractor.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"</span>
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="autoprocessor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#autoprocessor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoProcessor
</span></h2>
<p>Multimodale Aufgaben erfordern einen Prozessor, der zwei Arten von Vorverarbeitungswerkzeugen kombiniert. Das Modell <a href="model_doc/layoutlmv2">LayoutLMV2</a> beispielsweise benötigt einen Feature-Extraktor für Bilder und einen Tokenizer für Text; ein Prozessor kombiniert beide.</p>
<p>Laden Sie einen Prozessor mit <code>AutoProcessor.from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor
<span class="hljs-meta">>>> </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">"microsoft/layoutlmv2-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="automodel" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#automodel"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoModel
</span></h2>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>Mit den <code>AutoModelFor</code>-Klassen können Sie schließlich ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe <a href="model_doc/auto">hier</a> für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit <code>AutoModelForSequenceClassification.from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForTokenClassification
<span class="hljs-meta">>>> </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p>Für PyTorch-Modelle verwendet die Methode <code>from_pretrained()</code> <code>torch.load()</code>, die intern <code>pickle</code> verwendet und als unsicher bekannt ist. Generell sollte man niemals ein Modell laden, das aus einer nicht vertrauenswürdigen Quelle stammen könnte, oder das manipuliert worden sein könnte. Dieses Sicherheitsrisiko wird für öffentliche Modelle, die auf dem Hugging Face Hub gehostet werden, teilweise gemildert, da diese bei jeder Übertragung <a href="https://huggingface.co/docs/hub/security-malware" rel="nofollow">auf Malware</a> gescannt werden. Siehe die <a href="https://huggingface.co/docs/hub/security" rel="nofollow">Hub-Dokumentation</a> für Best Practices wie <a href="https://huggingface.co/docs/hub/security-gpg#signing-commits-with-gpg" rel="nofollow">signierte Commit-Verifizierung</a> mit GPG.</p>
<p>TensorFlow- und Flax-Checkpoints sind nicht betroffen und können in PyTorch-Architekturen mit den Kwargs <code>from_tf</code> und <code>from_flax</code> für die Methode <code>from_pretrained</code> geladen werden, um dieses Problem zu umgehen.</p></div>
<p>Im Allgemeinen empfehlen wir die Verwendung der Klasse “AutoTokenizer” und der Klasse “AutoModelFor”, um trainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.</p></div></div>
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<span>TensorFlow</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Mit den Klassen <code>TFAutoModelFor</code> schließlich können Sie ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe <a href="model_doc/auto">hier</a> für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit <code>TFAutoModelForSequenceClassification.from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForTokenClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Im Allgemeinen empfehlen wir, die Klasse “AutoTokenizer” und die Klasse “TFAutoModelFor” zu verwenden, um vortrainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.</p>
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<h1 class="relative group"><a id="pipelines-fr-inferenzen" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipelines-fr-inferenzen"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipelines für Inferenzen
</span></h1>
<p>Die <code>pipeline()</code> macht es einfach, jedes beliebige Modell aus dem <a href="https://huggingface.co/models" rel="nofollow">Hub</a> für die Inferenz auf jede Sprache, Computer Vision, Sprache und multimodale Aufgaben zu verwenden. Selbst wenn Sie keine Erfahrung mit einer bestimmten Modalität haben oder nicht mit dem zugrundeliegenden Code hinter den Modellen vertraut sind, können Sie sie mit der <code>pipeline()</code> für Inferenzen verwenden! In diesem Beispiel lernen Sie, wie:</p>
<ul><li>Eine <code>pipeline()</code> für Inferenz zu verwenden.</li>
<li>Einen bestimmten Tokenizer oder ein bestimmtes Modell zu verwenden.</li>
<li>Eine <code>pipeline()</code> für Audio-, Vision- und multimodale Aufgaben zu verwenden.</li></ul>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Eine vollständige Liste der unterstützten Aufgaben und verfügbaren Parameter finden Sie in der <code>pipeline()</code>-Dokumentation.</p></div>
<h2 class="relative group"><a id="verwendung-von-pipelines" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#verwendung-von-pipelines"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Verwendung von Pipelines
</span></h2>
<p>Obwohl jede Aufgabe eine zugehörige <code>pipeline()</code> hat, ist es einfacher, die allgemeine <code>pipeline()</code>-Abstraktion zu verwenden, die alle aufgabenspezifischen Pipelines enthält. Die <code>pipeline()</code> lädt automatisch ein Standardmodell und eine Vorverarbeitungsklasse, die für Ihre Aufgabe inferenzfähig ist.</p>
<ol><li>Beginnen Sie mit der Erstellung einer <code>pipeline()</code> und geben Sie eine Inferenzaufgabe an:</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>generator = pipeline(task=<span class="hljs-string">"text-generation"</span>)<!-- HTML_TAG_END --></pre></div>
<ol start="2"><li>Übergeben Sie Ihren Eingabetext an die <code>pipeline()</code>:</li></ol>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>
<span class="hljs-meta">... </span>) <span class="hljs-comment"># doctest: +SKIP</span>
[{<span class="hljs-string">'generated_text'</span>: <span class="hljs-string">'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Wenn Sie mehr als eine Eingabe haben, übergeben Sie die Eingabe als Liste:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> [
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>,
<span class="hljs-meta">... </span> <span class="hljs-string">"Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne"</span>,
<span class="hljs-meta">... </span> ]
<span class="hljs-meta">... </span>) <span class="hljs-comment"># doctest: +SKIP</span><!-- HTML_TAG_END --></pre></div>
<p>Alle zusätzlichen Parameter für Ihre Aufgabe können auch in die <code>pipeline()</code> aufgenommen werden. Die Aufgabe <code>Text-Generierung</code> hat eine <code>generate()</code>-Methode mit mehreren Parametern zur Steuerung der Ausgabe. Wenn Sie zum Beispiel mehr als eine Ausgabe erzeugen wollen, setzen Sie den Parameter <code>num_return_sequences</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>,
<span class="hljs-meta">... </span> num_return_sequences=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>) <span class="hljs-comment"># doctest: +SKIP</span><!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="whlen-sie-ein-modell-und-einen-tokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#whlen-sie-ein-modell-und-einen-tokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Wählen Sie ein Modell und einen Tokenizer
</span></h3>
<p>Die <code>pipeline()</code> akzeptiert jedes Modell aus dem [Hub] (<a href="https://huggingface.co/models" rel="nofollow">https://huggingface.co/models</a>). Auf dem Hub gibt es Tags, mit denen Sie nach einem Modell filtern können, das Sie für Ihre Aufgabe verwenden möchten. Sobald Sie ein passendes Modell ausgewählt haben, laden Sie es mit der entsprechenden <code>AutoModelFor</code> und <code>AutoTokenizer</code> Klasse. Laden Sie zum Beispiel die Klasse <code>AutoModelForCausalLM</code> für eine kausale Sprachmodellierungsaufgabe:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)
<span class="hljs-meta">>>> </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">"distilgpt2"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Erstellen Sie eine <code>pipeline()</code> für Ihre Aufgabe, und geben Sie das Modell und den Tokenizer an, die Sie geladen haben:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>generator = pipeline(task=<span class="hljs-string">"text-generation"</span>, model=model, tokenizer=tokenizer)<!-- HTML_TAG_END --></pre></div>
<p>Übergeben Sie Ihren Eingabetext an die <code>pipeline()</code> , um einen Text zu erzeugen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>generator(
<span class="hljs-meta">... </span> <span class="hljs-string">"Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"</span>
<span class="hljs-meta">... </span>) <span class="hljs-comment"># doctest: +SKIP</span>
[{<span class="hljs-string">'generated_text'</span>: <span class="hljs-string">'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'</span>}]<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="audiopipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#audiopipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Audio-Pipeline
</span></h2>
<p>Die <code>pipeline()</code> unterstützt auch Audioaufgaben wie Audioklassifizierung und automatische Spracherkennung.</p>
<p>Lassen Sie uns zum Beispiel die Emotion in diesem Audioclip klassifizieren:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span>torch.manual_seed(<span class="hljs-number">42</span>)
<span class="hljs-meta">>>> </span>ds = load_dataset(<span class="hljs-string">"hf-internal-testing/librispeech_asr_demo"</span>, <span class="hljs-string">"clean"</span>, split=<span class="hljs-string">"validation"</span>)
<span class="hljs-meta">>>> </span>audio_file = ds[<span class="hljs-number">0</span>][<span class="hljs-string">"audio"</span>][<span class="hljs-string">"path"</span>]<!-- HTML_TAG_END --></pre></div>
<p>Finden Sie ein <a href="https://huggingface.co/models?pipeline_tag=audio-classification" rel="nofollow">Audioklassifikation</a> Modell auf dem Model Hub für Emotionserkennung und laden Sie es in die <code>pipeline()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>audio_classifier = pipeline(
<span class="hljs-meta">... </span> task=<span class="hljs-string">"audio-classification"</span>, model=<span class="hljs-string">"ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"</span>
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Übergeben Sie die Audiodatei an die <code>pipeline()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>preds = audio_classifier(audio_file)
<span class="hljs-meta">>>> </span>preds = [{<span class="hljs-string">"score"</span>: <span class="hljs-built_in">round</span>(pred[<span class="hljs-string">"score"</span>], <span class="hljs-number">4</span>), <span class="hljs-string">"label"</span>: pred[<span class="hljs-string">"label"</span>]} <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">>>> </span>preds
[{<span class="hljs-string">'score'</span>: <span class="hljs-number">0.1315</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'calm'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.1307</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'neutral'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.1274</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'sad'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.1261</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'fearful'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.1242</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'happy'</span>}]<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="bildverarbeitungspipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#bildverarbeitungspipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Bildverarbeitungs-Pipeline
</span></h2>
<p>Die Verwendung einer <code>pipeline()</code> für Bildverarbeitungsaufgaben ist praktisch identisch.</p>
<p>Geben Sie Ihre Aufgabe an und übergeben Sie Ihr Bild an den Klassifikator. Das Bild kann ein Link oder ein lokaler Pfad zu dem Bild sein. Zum Beispiel: Welche Katzenart ist unten abgebildet?</p>
<p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" alt="pipeline-cat-chonk"></p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>vision_classifier = pipeline(task=<span class="hljs-string">"image-classification"</span>)
<span class="hljs-meta">>>> </span>preds = vision_classifier(
<span class="hljs-meta">... </span> images=<span class="hljs-string">"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">>>> </span>preds = [{<span class="hljs-string">"score"</span>: <span class="hljs-built_in">round</span>(pred[<span class="hljs-string">"score"</span>], <span class="hljs-number">4</span>), <span class="hljs-string">"label"</span>: pred[<span class="hljs-string">"label"</span>]} <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">>>> </span>preds
[{<span class="hljs-string">'score'</span>: <span class="hljs-number">0.4335</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'lynx, catamount'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.0348</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.0324</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'snow leopard, ounce, Panthera uncia'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.0239</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'Egyptian cat'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.0229</span>, <span class="hljs-string">'label'</span>: <span class="hljs-string">'tiger cat'</span>}]<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="multimodale-pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#multimodale-pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Multimodale Pipeline
</span></h2>
<p>Die <code>pipeline()</code> unterstützt mehr als eine Modalität. Eine Aufgabe zur Beantwortung visueller Fragen (VQA) kombiniert zum Beispiel Text und Bild. Verwenden Sie einen beliebigen Bildlink und eine Frage, die Sie zu dem Bild stellen möchten. Das Bild kann eine URL oder ein lokaler Pfad zu dem Bild sein.</p>
<p>Wenn Sie zum Beispiel das gleiche Bild wie in der obigen Vision-Pipeline verwenden:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>image = <span class="hljs-string">"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"</span>
<span class="hljs-meta">>>> </span>question = <span class="hljs-string">"Where is the cat?"</span><!-- HTML_TAG_END --></pre></div>
<p>Erstellen Sie eine Pipeline für “vqa” und übergeben Sie ihr das Bild und die Frage:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>vqa = pipeline(task=<span class="hljs-string">"vqa"</span>)
<span class="hljs-meta">>>> </span>preds = vqa(image=image, question=question)
<span class="hljs-meta">>>> </span>preds = [{<span class="hljs-string">"score"</span>: <span class="hljs-built_in">round</span>(pred[<span class="hljs-string">"score"</span>], <span class="hljs-number">4</span>), <span class="hljs-string">"answer"</span>: pred[<span class="hljs-string">"answer"</span>]} <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">>>> </span>preds
[{<span class="hljs-string">'score'</span>: <span class="hljs-number">0.9112</span>, <span class="hljs-string">'answer'</span>: <span class="hljs-string">'snow'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.8796</span>, <span class="hljs-string">'answer'</span>: <span class="hljs-string">'in snow'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.6717</span>, <span class="hljs-string">'answer'</span>: <span class="hljs-string">'outside'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.0291</span>, <span class="hljs-string">'answer'</span>: <span class="hljs-string">'on ground'</span>}, {<span class="hljs-string">'score'</span>: <span class="hljs-number">0.027</span>, <span class="hljs-string">'answer'</span>: <span class="hljs-string">'ground'</span>}]<!-- HTML_TAG_END --></pre></div>
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<h1 class="relative group"><a id="schnellstart" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#schnellstart"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Schnellstart
</span></h1>
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<p>Mit 🤗 Transformers können Sie sofort loslegen! Verwenden Sie die <code>pipeline()</code> für schnelle Inferenz und laden Sie schnell ein vortrainiertes Modell und einen Tokenizer mit einer <a href="./model_doc/auto">AutoClass</a>, um Ihre Text-, Bild- oder Audioaufgabe zu lösen.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Alle in der Dokumentation vorgestellten Codebeispiele haben oben links einen Umschalter für PyTorch und TensorFlow. Wenn
nicht, wird erwartet, dass der Code für beide Backends ohne Änderungen funktioniert.</p></div>
<h2 class="relative group"><a id="pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Pipeline
</span></h2>
<p><code>pipeline()</code> ist der einfachste Weg, ein vortrainiertes Modell für eine bestimmte Aufgabe zu verwenden.</p>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/tiZFewofSLM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Die <code>pipeline()</code> unterstützt viele gängige Aufgaben:</p>
<p><strong>Text</strong>:</p>
<ul><li>Stimmungsanalyse: Klassifizierung der Polarität eines gegebenen Textes.</li>
<li>Textgenerierung (auf Englisch): Generierung von Text aus einer gegebenen Eingabe.</li>
<li>Name-Entity-Recognition (NER): Kennzeichnung jedes Worts mit der Entität, die es repräsentiert (Person, Datum, Ort usw.).</li>
<li>Beantwortung von Fragen: Extrahieren der Antwort aus dem Kontext, wenn ein gewisser Kontext und eine Frage gegeben sind.</li>
<li>Fill-mask: Ausfüllen von Lücken in einem Text mit maskierten Wörtern.</li>
<li>Zusammenfassung: Erstellung einer Zusammenfassung einer langen Text- oder Dokumentensequenz.</li>
<li>Übersetzung: Übersetzen eines Textes in eine andere Sprache.</li>
<li>Merkmalsextraktion: Erstellen einer Tensordarstellung des Textes.</li></ul>
<p><strong>Bild</strong>:</p>
<ul><li>Bildklassifizierung: Klassifizierung eines Bildes.</li>
<li>Bildsegmentierung: Klassifizierung jedes Pixels in einem Bild.</li>
<li>Objekterkennung: Erkennen von Objekten innerhalb eines Bildes.</li></ul>
<p><strong>Audio</strong>:</p>
<ul><li>Audioklassifizierung: Zuweisung eines Labels zu einem bestimmten Audiosegment.</li>
<li>Automatische Spracherkennung (ASR): Transkription von Audiodaten in Text.</li></ul>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Für mehr Details über die <code>pipeline()</code> und assoziierte Aufgaben, schauen Sie in die Dokumentation <a href="./main_classes/pipelines">hier</a>.</p></div>
<h3 class="relative group"><a id="verwendung-der-pipeline" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#verwendung-der-pipeline"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Verwendung der Pipeline
</span></h3>
<p>Im folgenden Beispiel werden Sie die <code>pipeline()</code> für die Stimmungsanalyse verwenden.</p>
<p>Installieren Sie die folgenden Abhängigkeiten, falls Sie dies nicht bereits getan haben:</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
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<span>Pytorch</span></div>
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<pre><!-- HTML_TAG_START -->pip install torch<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<span>Hide TensorFlow content</span></div></div>
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<pre><!-- HTML_TAG_START -->pip install tensorflow<!-- HTML_TAG_END --></pre></div>
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</div>
<p>Importieren sie die <code>pipeline()</code> und spezifizieren sie die Aufgabe, welche sie lösen möchten:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>classifier = pipeline(<span class="hljs-string">"sentiment-analysis"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Die Pipeline lädt ein standardmäßiges [vortrainiertes Modell] (<a href="https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english" rel="nofollow">https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english</a>) und einen Tokenizer für die Stimmungs-Analyse herunter und speichert sie. Jetzt können Sie den “Klassifikator” auf Ihren Zieltext anwenden:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>classifier(<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'POSITIVE'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.9998</span>}]<!-- HTML_TAG_END --></pre></div>
<p>For more than one sentence, pass a list of sentences to the <code>pipeline()</code> which returns a list of dictionaries:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>results = classifier([<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>])
<span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> result <span class="hljs-keyword">in</span> results:
<span class="hljs-meta">... </span> <span class="hljs-built_in">print</span>(<span class="hljs-string">f"label: <span class="hljs-subst">{result[<span class="hljs-string">'label'</span>]}</span>, with score: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(result[<span class="hljs-string">'score'</span>], <span class="hljs-number">4</span>)}</span>"</span>)
label: POSITIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.9998</span>
label: NEGATIVE, <span class="hljs-keyword">with</span> score: <span class="hljs-number">0.5309</span><!-- HTML_TAG_END --></pre></div>
<p>Die <code>pipeline()</code> kann auch über einen ganzen Datensatz iterieren. Starten wir mit der Installation der <a href="https://huggingface.co/docs/datasets/" rel="nofollow">🤗 Datasets</a> Bibliothek:</p>
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<pre><!-- HTML_TAG_START -->pip install datasets <!-- HTML_TAG_END --></pre></div>
<p>Erstellen wir eine <code>pipeline()</code> mit der Aufgabe die wir lösen und dem Modell welches wir nutzen möchten.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<span class="hljs-meta">>>> </span>speech_recognizer = pipeline(<span class="hljs-string">"automatic-speech-recognition"</span>, model=<span class="hljs-string">"facebook/wav2vec2-base-960h"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Als nächstes laden wir den Datensatz (siehe 🤗 Datasets <a href="https://huggingface.co/docs/datasets/quickstart.html" rel="nofollow">Quick Start</a> für mehr Details) welches wir nutzen möchten. Zum Beispiel laden wir den <a href="https://huggingface.co/datasets/PolyAI/minds14" rel="nofollow">MInDS-14</a> Datensatz:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset, Audio
<span class="hljs-meta">>>> </span>dataset = load_dataset(<span class="hljs-string">"PolyAI/minds14"</span>, name=<span class="hljs-string">"en-US"</span>, split=<span class="hljs-string">"train"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Wir müssen sicherstellen, dass die Abtastrate des Datensatzes der Abtastrate entspricht, mit der <code>facebook/wav2vec2-base-960h</code> trainiert wurde.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>dataset = dataset.cast_column(<span class="hljs-string">"audio"</span>, Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))<!-- HTML_TAG_END --></pre></div>
<p>Audiodateien werden automatisch geladen und neu abgetastet, wenn die Spalte “audio” aufgerufen wird.
Extrahieren wir die rohen Wellenform-Arrays der ersten 4 Beispiele und übergeben wir sie als Liste an die Pipeline:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>result = speech_recognizer(dataset[:<span class="hljs-number">4</span>][<span class="hljs-string">"audio"</span>])
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>([d[<span class="hljs-string">"text"</span>] <span class="hljs-keyword">for</span> d <span class="hljs-keyword">in</span> result])
[<span class="hljs-string">'I WOULD LIKE TO SET UP A JOINT ACCOUNT WITH MY PARTNER HOW DO I PROCEED WITH DOING THAT'</span>, <span class="hljs-string">"FODING HOW I'D SET UP A JOIN TO HET WITH MY WIFE AND WHERE THE AP MIGHT BE"</span>, <span class="hljs-string">"I I'D LIKE TOY SET UP A JOINT ACCOUNT WITH MY PARTNER I'M NOT SEEING THE OPTION TO DO IT ON THE AP SO I CALLED IN TO GET SOME HELP CAN I JUST DO IT OVER THE PHONE WITH YOU AND GIVE YOU THE INFORMATION OR SHOULD I DO IT IN THE AP AND I'M MISSING SOMETHING UQUETTE HAD PREFERRED TO JUST DO IT OVER THE PHONE OF POSSIBLE THINGS"</span>, <span class="hljs-string">'HOW DO I THURN A JOIN A COUNT'</span>]<!-- HTML_TAG_END --></pre></div>
<p>Bei einem größeren Datensatz mit vielen Eingaben (wie bei Sprache oder Bildverarbeitung) sollten Sie einen Generator anstelle einer Liste übergeben, der alle Eingaben in den Speicher lädt. Weitere Informationen finden Sie in der <a href="./main_classes/pipelines">Pipeline-Dokumentation</a>.</p>
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<span>Ein anderes Modell und einen anderen Tokenizer in der Pipeline verwenden
</span></h3>
<p>Die <code>pipeline()</code> kann jedes Modell aus dem [Model Hub] (<a href="https://huggingface.co/models" rel="nofollow">https://huggingface.co/models</a>) verwenden, wodurch es einfach ist, die <code>pipeline()</code> für andere Anwendungsfälle anzupassen. Wenn Sie beispielsweise ein Modell wünschen, das französischen Text verarbeiten kann, verwenden Sie die Tags im Model Hub, um nach einem geeigneten Modell zu filtern. Das oberste gefilterte Ergebnis liefert ein mehrsprachiges <a href="https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment" rel="nofollow">BERT-Modell</a>, das auf die Stimmungsanalyse abgestimmt ist. Großartig, verwenden wir dieses Modell!</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span><!-- HTML_TAG_END --></pre></div>
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<span>Pytorch</span></div>
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<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>Use the <code>AutoModelForSequenceClassification</code> and <code>AutoTokenizer</code> to load the pretrained model and it’s associated tokenizer (more on an <code>AutoClass</code> below):</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = AutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Use the <code>TFAutoModelForSequenceClassification</code> and <code>AutoTokenizer</code> to load the pretrained model and it’s associated tokenizer (more on an <code>TFAutoClass</code> below):</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<p>Dann können Sie das Modell und den Tokenizer in der <code>pipeline()</code> angeben und den <code>Klassifikator</code> auf Ihren Zieltext anwenden:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>classifier = pipeline(<span class="hljs-string">"sentiment-analysis"</span>, model=model, tokenizer=tokenizer)
<span class="hljs-meta">>>> </span>classifier(<span class="hljs-string">"Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers."</span>)
[{<span class="hljs-string">'label'</span>: <span class="hljs-string">'5 stars'</span>, <span class="hljs-string">'score'</span>: <span class="hljs-number">0.7273</span>}]<!-- HTML_TAG_END --></pre></div>
<p>Wenn Sie kein Modell für Ihren Anwendungsfall finden können, müssen Sie ein vortrainiertes Modell auf Ihren Daten feinabstimmen. Schauen Sie sich unser <a href="./training">Feinabstimmungs-Tutorial</a> an, um zu erfahren, wie das geht. Und schließlich, nachdem Sie Ihr trainiertes Modell verfeinert haben, sollten Sie es mit der Community im Model Hub teilen (siehe Tutorial <a href="./model_sharing">hier</a>), um NLP für alle zu demokratisieren! 🤗</p>
<h2 class="relative group"><a id="autoclass" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#autoclass"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoClass
</span></h2>
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/AhChOFRegn4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Unter der Haube arbeiten die Klassen <code>AutoModelForSequenceClassification</code> und <code>AutoTokenizer</code> zusammen, um die <code>pipeline()</code> zu betreiben. Eine <a href="./model_doc/auto"><code>AutoClass</code></a> ist eine Abkürzung, die automatisch die Architektur eines trainierten Modells aus dessen Namen oder Pfad abruft. Sie müssen nur die passende <code>AutoClass</code> für Ihre Aufgabe und den zugehörigen Tokenizer mit <code>AutoTokenizer</code> auswählen. </p>
<p>Kehren wir zu unserem Beispiel zurück und sehen wir uns an, wie Sie die <code>AutoClass</code> verwenden können, um die Ergebnisse der <code>pipeline()</code> zu replizieren.</p>
<h3 class="relative group"><a id="autotokenizer" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#autotokenizer"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoTokenizer
</span></h3>
<p>Ein Tokenizer ist für die Vorverarbeitung von Text in ein für das Modell verständliches Format zuständig. Zunächst zerlegt der Tokenisierer den Text in Wörter, die <em>Token</em> genannt werden. Es gibt mehrere Regeln für den Tokenisierungsprozess, z. B. wie und auf welcher Ebene ein Wort aufgespalten wird (weitere Informationen über Tokenisierung <a href="./tokenizer_summary">hier</a>). Das Wichtigste ist jedoch, dass Sie den Tokenizer mit demselben Modellnamen instanziieren müssen, um sicherzustellen, dass Sie dieselben Tokenisierungsregeln verwenden, mit denen ein Modell zuvor trainiert wurde.
Laden sie einen Tokenizer mit <code>AutoTokenizer</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
<p>Anschließend wandelt der Tokenizer die Token in Zahlen um, um einen Tensor als Eingabe für das Modell zu konstruieren. Dieser wird als <em>Vokabular</em> des Modells bezeichnet.</p>
<p>Übergeben Sie Ihren Text an den Tokenizer:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>encoding = tokenizer(<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(encoding)
{<span class="hljs-string">'input_ids'</span>: [<span class="hljs-number">101</span>, <span class="hljs-number">11312</span>, <span class="hljs-number">10320</span>, <span class="hljs-number">12495</span>, <span class="hljs-number">19308</span>, <span class="hljs-number">10114</span>, <span class="hljs-number">11391</span>, <span class="hljs-number">10855</span>, <span class="hljs-number">10103</span>, <span class="hljs-number">100</span>, <span class="hljs-number">58263</span>, <span class="hljs-number">13299</span>, <span class="hljs-number">119</span>, <span class="hljs-number">102</span>],
<span class="hljs-string">'token_type_ids'</span>: [<span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
<span class="hljs-string">'attention_mask'</span>: [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]}<!-- HTML_TAG_END --></pre></div>
<p>Der Tokenizer gibt ein Wörterbuch zurück, das Folgendes enthält:</p>
<ul><li><a href="./glossary#input-ids">input_ids</a>: numerische Repräsentationen Ihrer Token.</li>
<li><a href=".glossary#attention-mask">atttention_mask</a>: gibt an, welche Token beachtet werden sollen.</li></ul>
<p>Genau wie die <code>pipeline()</code> akzeptiert der Tokenizer eine Liste von Eingaben. Darüber hinaus kann der Tokenizer den Text auch auffüllen und kürzen, um einen Stapel mit einheitlicher Länge zurückzugeben:</p>
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<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_batch = tokenizer(
<span class="hljs-meta">... </span> [<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>],
<span class="hljs-meta">... </span> padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"pt"</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_batch = tokenizer(
<span class="hljs-meta">... </span> [<span class="hljs-string">"We are very happy to show you the 🤗 Transformers library."</span>, <span class="hljs-string">"We hope you don't hate it."</span>],
<span class="hljs-meta">... </span> padding=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> truncation=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span> max_length=<span class="hljs-number">512</span>,
<span class="hljs-meta">... </span> return_tensors=<span class="hljs-string">"tf"</span>,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<p>Lesen Sie das Tutorial <a href="./preprocessing">preprocessing</a> für weitere Details zur Tokenisierung.</p>
<h3 class="relative group"><a id="automodel" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#automodel"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>AutoModel
</span></h3>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
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<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>🤗 Transformers bietet eine einfache und einheitliche Möglichkeit, vortrainierte Instanzen zu laden. Das bedeutet, dass Sie ein <code>AutoModel</code> laden können, wie Sie einen <code>AutoTokenizer</code> laden würden. Der einzige Unterschied ist die Auswahl des richtigen <code>AutoModel</code> für die Aufgabe. Da Sie eine Text- oder Sequenzklassifizierung vornehmen, laden Sie <code>AutoModelForSequenceClassification</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>In der <a href="./task_summary">Aufgabenzusammenfassung</a> steht, welche [AutoModel]-Klasse für welche Aufgabe zu verwenden ist.</p></div>
<p>Jetzt können Sie Ihren vorverarbeiteten Stapel von Eingaben direkt an das Modell übergeben. Sie müssen nur das Wörterbuch entpacken, indem Sie <code>**</code> hinzufügen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_outputs = pt_model(**pt_batch)<!-- HTML_TAG_END --></pre></div>
<p>Das Modell gibt die endgültigen Aktivierungen in dem Attribut “logits” aus. Wenden Sie die Softmax-Funktion auf die “logits” an, um die Wahrscheinlichkeiten zu erhalten:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> torch <span class="hljs-keyword">import</span> nn
<span class="hljs-meta">>>> </span>pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-<span class="hljs-number">1</span>)
<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(pt_predictions)
tensor([[<span class="hljs-number">0.0021</span>, <span class="hljs-number">0.0018</span>, <span class="hljs-number">0.0115</span>, <span class="hljs-number">0.2121</span>, <span class="hljs-number">0.7725</span>],
[<span class="hljs-number">0.2084</span>, <span class="hljs-number">0.1826</span>, <span class="hljs-number">0.1969</span>, <span class="hljs-number">0.1755</span>, <span class="hljs-number">0.2365</span>]], grad_fn=<SoftmaxBackward0>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>🤗 Transformers bietet eine einfache und einheitliche Methode zum Laden von vortrainierten Instanzen. Das bedeutet, dass Sie ein <code>TFAutoModel</code> genauso laden können, wie Sie einen <code>AutoTokenizer</code> laden würden. Der einzige Unterschied ist die Auswahl des richtigen <code>TFAutoModel</code> für die Aufgabe. Da Sie Text - oder Sequenz - Klassifizierung machen, laden Sie <code>TFAutoModelForSequenceClassification</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForSequenceClassification
<span class="hljs-meta">>>> </span>model_name = <span class="hljs-string">"nlptown/bert-base-multilingual-uncased-sentiment"</span>
<span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)<!-- HTML_TAG_END --></pre></div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>In der <a href="./task_summary">Aufgabenzusammenfassung</a> steht, welche [AutoModel]-Klasse für welche Aufgabe zu verwenden ist.</p></div>
<p>Jetzt können Sie Ihren vorverarbeiteten Stapel von Eingaben direkt an das Modell übergeben, indem Sie die Wörterbuchschlüssel direkt an die Tensoren übergeben:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_outputs = tf_model(tf_batch)<!-- HTML_TAG_END --></pre></div>
<p>Das Modell gibt die endgültigen Aktivierungen in dem Attribut “logits” aus. Wenden Sie die Softmax-Funktion auf die “logits” an, um die Wahrscheinlichkeiten zu erhalten:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">>>> </span>tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-<span class="hljs-number">1</span>)
<span class="hljs-meta">>>> </span>tf_predictions<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Alle 🤗 Transformers-Modelle (PyTorch oder TensorFlow) geben die Tensoren <em>vor</em> der endgültigen Aktivierungsfunktion
Funktion (wie Softmax) aus, da die endgültige Aktivierungsfunktion oft mit dem Verlusten verschmolzen ist.</p></div>
<p>Modelle sind ein standardmäßiges <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow"><code>torch.nn.Module</code></a> oder ein <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow"><code>tf.keras.Model</code></a>, sodass Sie sie in Ihrer üblichen Trainingsschleife verwenden können. Um jedoch die Dinge einfacher zu machen, bietet 🤗 Transformers eine <code>Trainer</code>-Klasse für PyTorch, die Funktionalität für verteiltes Training, gemischte Präzision und mehr bietet. Für TensorFlow können Sie die Methode <code>fit</code> aus <a href="https://keras.io/" rel="nofollow">Keras</a> verwenden. Siehe das <a href="./training">training tutorial</a> für weitere Details.</p>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Transformers-Modellausgaben sind spezielle Datenklassen, so dass ihre Attribute in einer IDE automatisch vervollständigt werden.
Die Modellausgänge verhalten sich auch wie ein Tupel oder ein Wörterbuch (z.B. können Sie mit einem Integer, einem Slice oder einem String indexieren), wobei die Attribute, die “None” sind, ignoriert werden.</p></div>
<h3 class="relative group"><a id="modell-speichern" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#modell-speichern"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Modell speichern
</span></h3>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>Sobald Ihr Modell feinabgestimmt ist, können Sie es mit seinem Tokenizer speichern, indem Sie <code>PreTrainedModel.save_pretrained()</code> verwenden:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_save_directory = <span class="hljs-string">"./pt_save_pretrained"</span>
<span class="hljs-meta">>>> </span>tokenizer.save_pretrained(pt_save_directory)
<span class="hljs-meta">>>> </span>pt_model.save_pretrained(pt_save_directory)<!-- HTML_TAG_END --></pre></div>
<p>Wenn Sie bereit sind, das Modell erneut zu verwenden, laden Sie es mit <code>PreTrainedModel.from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"./pt_save_pretrained"</span>)<!-- HTML_TAG_END --></pre></div></div></div>
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<span>TensorFlow</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Sobald Ihr Modell feinabgestimmt ist, können Sie es mit seinem Tokenizer unter Verwendung von <code>TFPreTrainedModel.save_pretrained()</code> speichern:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_save_directory = <span class="hljs-string">"./tf_save_pretrained"</span>
<span class="hljs-meta">>>> </span>tokenizer.save_pretrained(tf_save_directory)
<span class="hljs-meta">>>> </span>tf_model.save_pretrained(tf_save_directory)<!-- HTML_TAG_END --></pre></div>
<p>Wenn Sie bereit sind, das Modell wieder zu verwenden, laden Sie es mit <code>TFPreTrainedModel.from_pretrained()</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">"./tf_save_pretrained"</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<p>Ein besonders cooles 🤗 Transformers-Feature ist die Möglichkeit, ein Modell zu speichern und es entweder als PyTorch- oder TensorFlow-Modell wieder zu laden. Der Parameter “from_pt” oder “from_tf” kann das Modell von einem Framework in das andere konvertieren:</p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
<span class="hljs-meta">>>> </span>pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel
<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
<span class="hljs-meta">>>> </span>tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<h2 class="relative group"><a id="custom-model-builds" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#custom-model-builds"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Custom model builds
</span></h2>
<p>Sie können die Konfigurationsklasse des Modells ändern, um zu bestimmen, wie ein Modell aufgebaut ist. Die Konfiguration legt die Attribute eines Modells fest, z. B. die Anzahl der verborgenen Schichten oder der Aufmerksamkeitsköpfe. Wenn Sie ein Modell aus einer benutzerdefinierten Konfigurationsklasse initialisieren, beginnen Sie bei Null. Die Modellattribute werden zufällig initialisiert, und Sie müssen das Modell trainieren, bevor Sie es verwenden können, um aussagekräftige Ergebnisse zu erhalten.</p>
<p>Beginnen Sie mit dem Import von <code>AutoConfig</code> und laden Sie dann das trainierte Modell, das Sie ändern möchten. Innerhalb von <code>AutoConfig.from_pretrained()</code> können Sie das Attribut angeben, das Sie ändern möchten, z. B. die Anzahl der Aufmerksamkeitsköpfe:</p>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig
<span class="hljs-meta">>>> </span>my_config = AutoConfig.from_pretrained(<span class="hljs-string">"distilbert-base-uncased"</span>, n_heads=<span class="hljs-number">12</span>)<!-- HTML_TAG_END --></pre></div>
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<span>Pytorch</span></div>
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<p>Create a model from your custom configuration with <code>AutoModel.from_config()</code>:</p>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
<span class="hljs-meta">>>> </span>my_model = AutoModel.from_config(my_config)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
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<span>Hide TensorFlow content</span></div></div>
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<p>Create a model from your custom configuration with <code>TFAutoModel.from_config()</code>:</p>
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Copied</div></button></div>
<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModel
<span class="hljs-meta">>>> </span>my_model = TFAutoModel.from_config(my_config)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<p>Weitere Informationen zur Erstellung von benutzerdefinierten Konfigurationen finden Sie in der Anleitung <a href="./create_a_model">Erstellen einer benutzerdefinierten Architektur</a>.</p>
<h2 class="relative group"><a id="wie-geht-es-weiter" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#wie-geht-es-weiter"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Wie geht es weiter?
</span></h2>
<p>Nachdem Sie nun die 🤗 Transformers-Kurztour abgeschlossen haben, schauen Sie sich unsere Anleitungen an und erfahren Sie, wie Sie spezifischere Dinge tun können, wie das Schreiben eines benutzerdefinierten Modells, die Feinabstimmung eines Modells für eine Aufgabe und wie man ein Modell mit einem Skript trainiert. Wenn Sie mehr über die Kernkonzepte von 🤗 Transformers erfahren möchten, nehmen Sie sich eine Tasse Kaffee und werfen Sie einen Blick auf unsere konzeptionellen Leitfäden!</p>
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<h1 class="relative group"><a id="transformers" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#transformers"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>🤗 Transformers
</span></h1>
<p>Maschinelles Lernen auf dem neuesten Stand der Technik für PyTorch, TensorFlow und JAX.</p>
<p>🤗 Transformers bietet APIs zum einfachen Herunterladen und Trainieren von vortrainierten Modellen auf dem neuesten Stand der Technik. Die Verwendung von vortrainierten Modellen kann Rechenkosten sparen und den CO2-Fußabdruck reduzieren und Zeit sparen, die für das Training eines Modells von Grund auf benötigt wird. Die Modelle können für verschiedene Modalitäten verwendet werden, wie z. B.:</p>
<ul><li>📝 Text: Textklassifizierung, Informationsextrahierung, Beantwortung von Fragen, Zusammenfassung, Übersetzung und Texterstellung in über 100 Sprachen.</li>
<li>🖼️ Bilder: Bildklassifizierung, Objekterkennung und Segmentierung.</li>
<li>🗣️ Audio: Spracherkennung und Audioklassifizierung.</li>
<li>🐙 Multimodal: Beantwortung von Tabellenfragen, optische Zeichenerkennung, Informationsextraktion aus gescannten Dokumenten, Videoklassifizierung und Beantwortung visueller Fragen.</li></ul>
<p>Unsere Bibliothek unterstützt die nahtlose Integration von drei der beliebtesten Deep-Learning-Bibliotheken: <a href="https://pytorch.org/" rel="nofollow">PyTorch</a>, <a href="https://www.tensorflow.org/" rel="nofollow">TensorFlow</a> und <a href="https://jax.readthedocs.io/en/latest/" rel="nofollow">JAX</a>. Trainieren Sie Ihr Modell in drei Codezeilen in einem Framework und laden Sie es zur Inferenz mit einem anderen.</p>
<p>Jede 🤗 Transformers-Architektur ist in einem eigenständigen Python-Modul definiert, so dass sie leicht für Forschung und Experimente angepasst werden kann.</p>
<h2 class="relative group"><a id="wenn-sie-auf-der-suche-nach-individueller-untersttzung-durch-das-hugging-faceteam-sind" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#wenn-sie-auf-der-suche-nach-individueller-untersttzung-durch-das-hugging-faceteam-sind"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Wenn Sie auf der Suche nach individueller Unterstützung durch das Hugging Face-Team sind
</span></h2>
<a target="_blank" href="https://huggingface.co/support"><img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a><br>
<h2 class="relative group"><a id="inhalt" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#inhalt"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Inhalt
</span></h2>
<p>Die Dokumentation ist in fünf Teile gegliedert:</p>
<ul><li><p><strong>GET STARTED</strong> enthält eine kurze Tour und Installationsanweisungen, um mit 🤗 Transformers loszulegen.</p></li>
<li><p><strong>TUTORIALS</strong> sind ein hervorragender Ausgangspunkt, wenn Sie neu in unserer Bibliothek sind. Dieser Abschnitt hilft Ihnen, die grundlegenden Fähigkeiten zu erlangen, die Sie benötigen, um mit 🤗 Transformers zu arbeiten.</p></li>
<li><p><strong>HOW-TO GUIDES</strong> zeigen Ihnen, wie Sie ein bestimmtes Ziel erreichen können, z. B. die Feinabstimmung eines vortrainierten Modells für die Sprachmodellierung oder die Erstellung eines benutzerdefinierten Modellkopfs.</p></li>
<li><p><strong>KONZEPTUELLE ANLEITUNGEN</strong> bietet weitere Diskussionen und Erklärungen zu den zugrunde liegenden Konzepten und Ideen hinter Modellen, Aufgaben und der Designphilosophie von 🤗 Transformers. </p></li>
<li><p><strong>API</strong> beschreibt jede Klasse und Funktion, gruppiert in:</p>
<ul><li><strong>MAIN CLASSES</strong> für die Hauptklassen, die die wichtigsten APIs der Bibliothek darstellen.</li>
<li>MODELLE** für die Klassen und Funktionen, die zu jedem in der Bibliothek implementierten Modell gehören.</li>
<li><strong>INTERNAL HELPERS</strong> für die Klassen und Funktionen, die wir intern verwenden.</li></ul></li></ul>
<p>Die Bibliothek enthält derzeit JAX-, PyTorch- und TensorFlow-Implementierungen, vortrainierte Modellgewichte, Nutzungsskripte und Konvertierungsprogramme für die folgenden Modelle.</p>
<h3 class="relative group"><a id="untersttze-modelle" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#untersttze-modelle"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Unterstütze Modelle
</span></h3>
<ol><li><strong><a href="model_doc/albert">ALBERT</a></strong> (from Google Research and the Toyota Technological Institute at Chicago) released with the paper <a href="https://arxiv.org/abs/1909.11942" rel="nofollow">ALBERT: A Lite BERT for Self-supervised Learning of Language Representations</a>, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.</li>
<li><strong><a href="model_doc/bart">BART</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension</a> by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.</li>
<li><strong><a href="model_doc/barthez">BARThez</a></strong> (from École polytechnique) released with the paper <a href="https://arxiv.org/abs/2010.12321" rel="nofollow">BARThez: a Skilled Pretrained French Sequence-to-Sequence Model</a> by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.</li>
<li><strong><a href="model_doc/bartpho">BARTpho</a></strong> (from VinAI Research) released with the paper <a href="https://arxiv.org/abs/2109.09701" rel="nofollow">BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese</a> by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.</li>
<li><strong><a href="model_doc/beit">BEiT</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2106.08254" rel="nofollow">BEiT: BERT Pre-Training of Image Transformers</a> by Hangbo Bao, Li Dong, Furu Wei.</li>
<li><strong><a href="model_doc/bert">BERT</a></strong> (from Google) released with the paper <a href="https://arxiv.org/abs/1810.04805" rel="nofollow">BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding</a> by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.</li>
<li><strong><a href="model_doc/bert-generation">BERT For Sequence Generation</a></strong> (from Google) released with the paper <a href="https://arxiv.org/abs/1907.12461" rel="nofollow">Leveraging Pre-trained Checkpoints for Sequence Generation Tasks</a> by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.</li>
<li><strong><a href="model_doc/bertweet">BERTweet</a></strong> (from VinAI Research) released with the paper <a href="https://aclanthology.org/2020.emnlp-demos.2/" rel="nofollow">BERTweet: A pre-trained language model for English Tweets</a> by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.</li>
<li><strong><a href="model_doc/bigbird_pegasus">BigBird-Pegasus</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2007.14062" rel="nofollow">Big Bird: Transformers for Longer Sequences</a> by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.</li>
<li><strong><a href="model_doc/big_bird">BigBird-RoBERTa</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2007.14062" rel="nofollow">Big Bird: Transformers for Longer Sequences</a> by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.</li>
<li><strong><a href="model_doc/blenderbot">Blenderbot</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2004.13637" rel="nofollow">Recipes for building an open-domain chatbot</a> by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.</li>
<li><strong><a href="model_doc/blenderbot-small">BlenderbotSmall</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2004.13637" rel="nofollow">Recipes for building an open-domain chatbot</a> by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.</li>
<li><strong><a href="model_doc/bloom">BLOOM</a></strong> (from BigScience workshop) released by the <a href="https://bigscience.huggingface.co/" rel="nofollow">BigSicence Workshop</a>.</li>
<li><strong><a href="model_doc/bort">BORT</a></strong> (from Alexa) released with the paper <a href="https://arxiv.org/abs/2010.10499" rel="nofollow">Optimal Subarchitecture Extraction For BERT</a> by Adrian de Wynter and Daniel J. Perry.</li>
<li><strong><a href="model_doc/byt5">ByT5</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2105.13626" rel="nofollow">ByT5: Towards a token-free future with pre-trained byte-to-byte models</a> by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.</li>
<li><strong><a href="model_doc/camembert">CamemBERT</a></strong> (from Inria/Facebook/Sorbonne) released with the paper <a href="https://arxiv.org/abs/1911.03894" rel="nofollow">CamemBERT: a Tasty French Language Model</a> by Louis Martin<em>, Benjamin Muller</em>, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.</li>
<li><strong><a href="model_doc/canine">CANINE</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2103.06874" rel="nofollow">CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation</a> by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.</li>
<li><strong><a href="model_doc/clip">CLIP</a></strong> (from OpenAI) released with the paper <a href="https://arxiv.org/abs/2103.00020" rel="nofollow">Learning Transferable Visual Models From Natural Language Supervision</a> by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.</li>
<li><strong><a href="model_doc/codegen">CodeGen</a></strong> (from Salesforce) released with the paper <a href="https://arxiv.org/abs/2203.13474" rel="nofollow">A Conversational Paradigm for Program Synthesis</a> by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.</li>
<li><strong><a href="model_doc/convbert">ConvBERT</a></strong> (from YituTech) released with the paper <a href="https://arxiv.org/abs/2008.02496" rel="nofollow">ConvBERT: Improving BERT with Span-based Dynamic Convolution</a> by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.</li>
<li><strong><a href="model_doc/convnext">ConvNeXT</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2201.03545" rel="nofollow">A ConvNet for the 2020s</a> by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.</li>
<li><strong><a href="model_doc/cpm">CPM</a></strong> (from Tsinghua University) released with the paper <a href="https://arxiv.org/abs/2012.00413" rel="nofollow">CPM: A Large-scale Generative Chinese Pre-trained Language Model</a> by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.</li>
<li><strong><a href="model_doc/ctrl">CTRL</a></strong> (from Salesforce) released with the paper <a href="https://arxiv.org/abs/1909.05858" rel="nofollow">CTRL: A Conditional Transformer Language Model for Controllable Generation</a> by Nitish Shirish Keskar<em>, Bryan McCann</em>, Lav R. Varshney, Caiming Xiong and Richard Socher.</li>
<li><strong><a href="model_doc/cvt">CvT</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2103.15808" rel="nofollow">CvT: Introducing Convolutions to Vision Transformers</a> by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.</li>
<li><strong><a href="model_doc/data2vec">Data2Vec</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2202.03555" rel="nofollow">Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language</a> by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.</li>
<li><strong><a href="model_doc/deberta">DeBERTa</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2006.03654" rel="nofollow">DeBERTa: Decoding-enhanced BERT with Disentangled Attention</a> by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.</li>
<li><strong><a href="model_doc/deberta-v2">DeBERTa-v2</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2006.03654" rel="nofollow">DeBERTa: Decoding-enhanced BERT with Disentangled Attention</a> by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.</li>
<li><strong><a href="model_doc/decision_transformer">Decision Transformer</a></strong> (from Berkeley/Facebook/Google) released with the paper <a href="https://arxiv.org/abs/2106.01345" rel="nofollow">Decision Transformer: Reinforcement Learning via Sequence Modeling</a> by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.</li>
<li><strong><a href="model_doc/deit">DeiT</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2012.12877" rel="nofollow">Training data-efficient image transformers & distillation through attention</a> by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.</li>
<li><strong><a href="model_doc/detr">DETR</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2005.12872" rel="nofollow">End-to-End Object Detection with Transformers</a> by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.</li>
<li><strong><a href="model_doc/dialogpt">DialoGPT</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/1911.00536" rel="nofollow">DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation</a> by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.</li>
<li><strong><a href="model_doc/distilbert">DistilBERT</a></strong> (from HuggingFace), released together with the paper <a href="https://arxiv.org/abs/1910.01108" rel="nofollow">DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter</a> by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">DistilGPT2</a>, RoBERTa into <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">DistilRoBERTa</a>, Multilingual BERT into <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">DistilmBERT</a> and a German version of DistilBERT.</li>
<li><strong><a href="model_doc/dit">DiT</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2203.02378" rel="nofollow">DiT: Self-supervised Pre-training for Document Image Transformer</a> by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.</li>
<li><strong><a href="model_doc/dpr">DPR</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2004.04906" rel="nofollow">Dense Passage Retrieval for Open-Domain Question Answering</a> by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.</li>
<li><strong><a href="master/model_doc/dpt">DPT</a></strong> (from Intel Labs) released with the paper <a href="https://arxiv.org/abs/2103.13413" rel="nofollow">Vision Transformers for Dense Prediction</a> by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.</li>
<li><strong><a href="model_doc/electra">ELECTRA</a></strong> (from Google Research/Stanford University) released with the paper <a href="https://arxiv.org/abs/2003.10555" rel="nofollow">ELECTRA: Pre-training text encoders as discriminators rather than generators</a> by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.</li>
<li><strong><a href="model_doc/encoder-decoder">EncoderDecoder</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/1907.12461" rel="nofollow">Leveraging Pre-trained Checkpoints for Sequence Generation Tasks</a> by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.</li>
<li><strong><a href="model_doc/flaubert">FlauBERT</a></strong> (from CNRS) released with the paper <a href="https://arxiv.org/abs/1912.05372" rel="nofollow">FlauBERT: Unsupervised Language Model Pre-training for French</a> by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.</li>
<li><strong><a href="model_doc/flava">FLAVA</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2112.04482" rel="nofollow">FLAVA: A Foundational Language And Vision Alignment Model</a> by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.</li>
<li><strong><a href="model_doc/fnet">FNet</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2105.03824" rel="nofollow">FNet: Mixing Tokens with Fourier Transforms</a> by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.</li>
<li><strong><a href="model_doc/funnel">Funnel Transformer</a></strong> (from CMU/Google Brain) released with the paper <a href="https://arxiv.org/abs/2006.03236" rel="nofollow">Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing</a> by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.</li>
<li><strong><a href="model_doc/glpn">GLPN</a></strong> (from KAIST) released with the paper <a href="https://arxiv.org/abs/2201.07436" rel="nofollow">Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth</a> by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.</li>
<li><strong><a href="model_doc/openai-gpt">GPT</a></strong> (from OpenAI) released with the paper <a href="https://blog.openai.com/language-unsupervised/" rel="nofollow">Improving Language Understanding by Generative Pre-Training</a> by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.</li>
<li><strong><a href="model_doc/gpt_neo">GPT Neo</a></strong> (from EleutherAI) released in the repository <a href="https://github.com/EleutherAI/gpt-neo" rel="nofollow">EleutherAI/gpt-neo</a> by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.</li>
<li><strong><a href="model_doc/gpt_neox">GPT NeoX</a></strong> (from EleutherAI) released with the paper <a href="https://arxiv.org/abs/2204.06745" rel="nofollow">GPT-NeoX-20B: An Open-Source Autoregressive Language Model</a> by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach</li>
<li><strong><a href="model_doc/gpt2">GPT-2</a></strong> (from OpenAI) released with the paper <a href="https://blog.openai.com/better-language-models/" rel="nofollow">Language Models are Unsupervised Multitask Learners</a> by Alec Radford<em>, Jeffrey Wu</em>, Rewon Child, David Luan, Dario Amodei<strong>and Ilya Sutskever</strong>.</li>
<li><strong><a href="model_doc/gptj">GPT-J</a></strong> (from EleutherAI) released in the repository <a href="https://github.com/kingoflolz/mesh-transformer-jax/" rel="nofollow">kingoflolz/mesh-transformer-jax</a> by Ben Wang and Aran Komatsuzaki.</li>
<li><strong><a href="model_doc/groupvit">GroupViT</a></strong> (from UCSD, NVIDIA) released with the paper <a href="https://arxiv.org/abs/2202.11094" rel="nofollow">GroupViT: Semantic Segmentation Emerges from Text Supervision</a> by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.</li>
<li><strong><a href="model_doc/hubert">Hubert</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2106.07447" rel="nofollow">HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units</a> by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.</li>
<li><strong><a href="model_doc/ibert">I-BERT</a></strong> (from Berkeley) released with the paper <a href="https://arxiv.org/abs/2101.01321" rel="nofollow">I-BERT: Integer-only BERT Quantization</a> by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.</li>
<li><strong><a href="model_doc/imagegpt">ImageGPT</a></strong> (from OpenAI) released with the paper <a href="https://openai.com/blog/image-gpt/" rel="nofollow">Generative Pretraining from Pixels</a> by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.</li>
<li><strong><a href="model_doc/layoutlm">LayoutLM</a></strong> (from Microsoft Research Asia) released with the paper <a href="https://arxiv.org/abs/1912.13318" rel="nofollow">LayoutLM: Pre-training of Text and Layout for Document Image Understanding</a> by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.</li>
<li><strong><a href="model_doc/layoutlmv2">LayoutLMv2</a></strong> (from Microsoft Research Asia) released with the paper <a href="https://arxiv.org/abs/2012.14740" rel="nofollow">LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding</a> by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.</li>
<li><strong><a href="model_doc/layoutlmv3">LayoutLMv3</a></strong> (from Microsoft Research Asia) released with the paper <a href="https://arxiv.org/abs/2204.08387" rel="nofollow">LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking</a> by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.</li>
<li><strong><a href="model_doc/layoutxlm">LayoutXLM</a></strong> (from Microsoft Research Asia) released with the paper <a href="https://arxiv.org/abs/2104.08836" rel="nofollow">LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding</a> by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.</li>
<li><strong><a href="model_doc/led">LED</a></strong> (from AllenAI) released with the paper <a href="https://arxiv.org/abs/2004.05150" rel="nofollow">Longformer: The Long-Document Transformer</a> by Iz Beltagy, Matthew E. Peters, Arman Cohan.</li>
<li><strong><a href="model_doc/levit">LeViT</a></strong> (from Meta AI) released with the paper <a href="https://arxiv.org/abs/2104.01136" rel="nofollow">LeViT: A Vision Transformer in ConvNet’s Clothing for Faster Inference</a> by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.</li>
<li><strong><a href="model_doc/longformer">Longformer</a></strong> (from AllenAI) released with the paper <a href="https://arxiv.org/abs/2004.05150" rel="nofollow">Longformer: The Long-Document Transformer</a> by Iz Beltagy, Matthew E. Peters, Arman Cohan.</li>
<li><strong><a href="model_doc/longt5">LongT5</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2112.07916" rel="nofollow">LongT5: Efficient Text-To-Text Transformer for Long Sequences</a> by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.</li>
<li><strong><a href="model_doc/luke">LUKE</a></strong> (from Studio Ousia) released with the paper <a href="https://arxiv.org/abs/2010.01057" rel="nofollow">LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention</a> by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.</li>
<li><strong><a href="model_doc/lxmert">LXMERT</a></strong> (from UNC Chapel Hill) released with the paper <a href="https://arxiv.org/abs/1908.07490" rel="nofollow">LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering</a> by Hao Tan and Mohit Bansal.</li>
<li><strong><a href="model_doc/mctct">M-CTC-T</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2111.00161" rel="nofollow">Pseudo-Labeling For Massively Multilingual Speech Recognition</a> by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.</li>
<li><strong><a href="model_doc/m2m_100">M2M100</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2010.11125" rel="nofollow">Beyond English-Centric Multilingual Machine Translation</a> by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.</li>
<li><strong><a href="model_doc/marian">MarianMT</a></strong> Machine translation models trained using <a href="http://opus.nlpl.eu/" rel="nofollow">OPUS</a> data by Jörg Tiedemann. The <a href="https://marian-nmt.github.io/" rel="nofollow">Marian Framework</a> is being developed by the Microsoft Translator Team.</li>
<li><strong><a href="model_doc/maskformer">MaskFormer</a></strong> (from Meta and UIUC) released with the paper <a href="https://arxiv.org/abs/2107.06278" rel="nofollow">Per-Pixel Classification is Not All You Need for Semantic Segmentation</a> by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.</li>
<li><strong><a href="model_doc/mbart">mBART</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2001.08210" rel="nofollow">Multilingual Denoising Pre-training for Neural Machine Translation</a> by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.</li>
<li><strong><a href="model_doc/mbart">mBART-50</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2008.00401" rel="nofollow">Multilingual Translation with Extensible Multilingual Pretraining and Finetuning</a> by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.</li>
<li><strong><a href="model_doc/megatron-bert">Megatron-BERT</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/1909.08053" rel="nofollow">Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism</a> by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.</li>
<li><strong><a href="model_doc/megatron_gpt2">Megatron-GPT2</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/1909.08053" rel="nofollow">Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism</a> by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.</li>
<li><strong><a href="model_doc/mluke">mLUKE</a></strong> (from Studio Ousia) released with the paper <a href="https://arxiv.org/abs/2110.08151" rel="nofollow">mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models</a> by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.</li>
<li><strong><a href="model_doc/mobilebert">MobileBERT</a></strong> (from CMU/Google Brain) released with the paper <a href="https://arxiv.org/abs/2004.02984" rel="nofollow">MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices</a> by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.</li>
<li><strong><a href="model_doc/mobilevit">MobileViT</a></strong> (from Apple) released with the paper <a href="https://arxiv.org/abs/2110.02178" rel="nofollow">MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer</a> by Sachin Mehta and Mohammad Rastegari.</li>
<li><strong><a href="model_doc/mpnet">MPNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2004.09297" rel="nofollow">MPNet: Masked and Permuted Pre-training for Language Understanding</a> by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.</li>
<li><strong><a href="model_doc/mt5">MT5</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2010.11934" rel="nofollow">mT5: A massively multilingual pre-trained text-to-text transformer</a> by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.</li>
<li><strong><a href="model_doc/mvp">MVP</a></strong> (from RUC AI Box) released with the paper <a href="https://arxiv.org/abs/2206.12131" rel="nofollow">MVP: Multi-task Supervised Pre-training for Natural Language Generation</a> by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.</li>
<li><strong><a href="model_doc/nezha">Nezha</a></strong> (from Huawei Noah’s Ark Lab) released with the paper <a href="https://arxiv.org/abs/1909.00204" rel="nofollow">NEZHA: Neural Contextualized Representation for Chinese Language Understanding</a> by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.</li>
<li><strong><a href="model_doc/nllb">NLLB</a></strong> (from Meta) released with the paper <a href="https://arxiv.org/abs/2207.04672" rel="nofollow">No Language Left Behind: Scaling Human-Centered Machine Translation</a> by the NLLB team.</li>
<li><strong><a href="model_doc/nystromformer">Nyströmformer</a></strong> (from the University of Wisconsin - Madison) released with the paper <a href="https://arxiv.org/abs/2102.03902" rel="nofollow">Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention</a> by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.</li>
<li><strong><a href="master/model_doc/opt">OPT</a></strong> (from Meta AI) released with the paper <a href="https://arxiv.org/abs/2205.01068" rel="nofollow">OPT: Open Pre-trained Transformer Language Models</a> by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.</li>
<li><strong><a href="model_doc/owlvit">OWL-ViT</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2205.06230" rel="nofollow">Simple Open-Vocabulary Object Detection with Vision Transformers</a> by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.</li>
<li><strong><a href="model_doc/pegasus">Pegasus</a></strong> (from Google) released with the paper <a href="https://arxiv.org/abs/1912.08777" rel="nofollow">PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization</a> by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.</li>
<li><strong><a href="model_doc/perceiver">Perceiver IO</a></strong> (from Deepmind) released with the paper <a href="https://arxiv.org/abs/2107.14795" rel="nofollow">Perceiver IO: A General Architecture for Structured Inputs & Outputs</a> by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.</li>
<li><strong><a href="model_doc/phobert">PhoBERT</a></strong> (from VinAI Research) released with the paper <a href="https://www.aclweb.org/anthology/2020.findings-emnlp.92/" rel="nofollow">PhoBERT: Pre-trained language models for Vietnamese</a> by Dat Quoc Nguyen and Anh Tuan Nguyen.</li>
<li><strong><a href="model_doc/plbart">PLBart</a></strong> (from UCLA NLP) released with the paper <a href="https://arxiv.org/abs/2103.06333" rel="nofollow">Unified Pre-training for Program Understanding and Generation</a> by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.</li>
<li><strong><a href="model_doc/poolformer">PoolFormer</a></strong> (from Sea AI Labs) released with the paper <a href="https://arxiv.org/abs/2111.11418" rel="nofollow">MetaFormer is Actually What You Need for Vision</a> by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.</li>
<li><strong><a href="model_doc/prophetnet">ProphetNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2001.04063" rel="nofollow">ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training</a> by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.</li>
<li><strong><a href="model_doc/qdqbert">QDQBert</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/2004.09602" rel="nofollow">Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation</a> by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.</li>
<li><strong><a href="model_doc/rag">RAG</a></strong> (from Facebook) released with the paper <a href="https://arxiv.org/abs/2005.11401" rel="nofollow">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks</a> by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.</li>
<li><strong><a href="model_doc/realm.html">REALM</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2002.08909" rel="nofollow">REALM: Retrieval-Augmented Language Model Pre-Training</a> by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.</li>
<li><strong><a href="model_doc/reformer">Reformer</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2001.04451" rel="nofollow">Reformer: The Efficient Transformer</a> by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.</li>
<li><strong><a href="model_doc/regnet">RegNet</a></strong> (from META Platforms) released with the paper <a href="https://arxiv.org/abs/2003.13678" rel="nofollow">Designing Network Design Space</a> by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.</li>
<li><strong><a href="model_doc/rembert">RemBERT</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2010.12821" rel="nofollow">Rethinking embedding coupling in pre-trained language models</a> by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.</li>
<li><strong><a href="model_doc/resnet">ResNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/1512.03385" rel="nofollow">Deep Residual Learning for Image Recognition</a> by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.</li>
<li><strong><a href="model_doc/roberta">RoBERTa</a></strong> (from Facebook), released together with the paper <a href="https://arxiv.org/abs/1907.11692" rel="nofollow">RoBERTa: A Robustly Optimized BERT Pretraining Approach</a> by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.</li>
<li><strong><a href="model_doc/roformer">RoFormer</a></strong> (from ZhuiyiTechnology), released together with the paper <a href="https://arxiv.org/abs/2104.09864" rel="nofollow">RoFormer: Enhanced Transformer with Rotary Position Embedding</a> by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.</li>
<li><strong><a href="model_doc/segformer">SegFormer</a></strong> (from NVIDIA) released with the paper <a href="https://arxiv.org/abs/2105.15203" rel="nofollow">SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers</a> by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.</li>
<li><strong><a href="model_doc/sew">SEW</a></strong> (from ASAPP) released with the paper <a href="https://arxiv.org/abs/2109.06870" rel="nofollow">Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition</a> by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.</li>
<li><strong><a href="model_doc/sew_d">SEW-D</a></strong> (from ASAPP) released with the paper <a href="https://arxiv.org/abs/2109.06870" rel="nofollow">Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition</a> by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.</li>
<li><strong><a href="model_doc/speech_to_text">SpeechToTextTransformer</a></strong> (from Facebook), released together with the paper <a href="https://arxiv.org/abs/2010.05171" rel="nofollow">fairseq S2T: Fast Speech-to-Text Modeling with fairseq</a> by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.</li>
<li><strong><a href="model_doc/speech_to_text_2">SpeechToTextTransformer2</a></strong> (from Facebook), released together with the paper <a href="https://arxiv.org/abs/2104.06678" rel="nofollow">Large-Scale Self- and Semi-Supervised Learning for Speech Translation</a> by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.</li>
<li><strong><a href="model_doc/splinter">Splinter</a></strong> (from Tel Aviv University), released together with the paper <a href="https://arxiv.org/abs/2101.00438" rel="nofollow">Few-Shot Question Answering by Pretraining Span Selection</a> by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.</li>
<li><strong><a href="model_doc/squeezebert">SqueezeBERT</a></strong> (from Berkeley) released with the paper <a href="https://arxiv.org/abs/2006.11316" rel="nofollow">SqueezeBERT: What can computer vision teach NLP about efficient neural networks?</a> by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.</li>
<li><strong><a href="model_doc/swin">Swin Transformer</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2103.14030" rel="nofollow">Swin Transformer: Hierarchical Vision Transformer using Shifted Windows</a> by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.</li>
<li><strong><a href="model_doc/swinv2">Swin Transformer V2</a></strong> (from Microsoft) released with the paper <a href="https://arxiv.org/abs/2111.09883" rel="nofollow">Swin Transformer V2: Scaling Up Capacity and Resolution</a> by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.</li>
<li><strong><a href="model_doc/t5">T5</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/1910.10683" rel="nofollow">Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer</a> by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.</li>
<li><strong><a href="model_doc/t5v1.1">T5v1.1</a></strong> (from Google AI) released in the repository <a href="https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511" rel="nofollow">google-research/text-to-text-transfer-transformer</a> by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.</li>
<li><strong><a href="model_doc/tapas">TAPAS</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2004.02349" rel="nofollow">TAPAS: Weakly Supervised Table Parsing via Pre-training</a> by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.</li>
<li><strong><a href="model_doc/tapex">TAPEX</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2107.07653" rel="nofollow">TAPEX: Table Pre-training via Learning a Neural SQL Executor</a> by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.</li>
<li><strong><a href="model_doc/trajectory_transformers">Trajectory Transformer</a></strong> (from the University of California at Berkeley) released with the paper <a href="https://arxiv.org/abs/2106.02039" rel="nofollow">Offline Reinforcement Learning as One Big Sequence Modeling Problem</a> by Michael Janner, Qiyang Li, Sergey Levine</li>
<li><strong><a href="model_doc/transfo-xl">Transformer-XL</a></strong> (from Google/CMU) released with the paper <a href="https://arxiv.org/abs/1901.02860" rel="nofollow">Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context</a> by Zihang Dai<em>, Zhilin Yang</em>, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.</li>
<li><strong><a href="model_doc/trocr">TrOCR</a></strong> (from Microsoft), released together with the paper <a href="https://arxiv.org/abs/2109.10282" rel="nofollow">TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.</li>
<li><strong><a href="model_doc/ul2">UL2</a></strong> (from Google Research) released with the paper <a href="https://arxiv.org/abs/2205.05131v1" rel="nofollow">Unifying Language Learning Paradigms</a> by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler</li>
<li><strong><a href="model_doc/unispeech">UniSpeech</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2101.07597" rel="nofollow">UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data</a> by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.</li>
<li><strong><a href="model_doc/unispeech-sat">UniSpeechSat</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2110.05752" rel="nofollow">UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING</a> by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.</li>
<li><strong><a href="model_doc/van">VAN</a></strong> (from Tsinghua University and Nankai University) released with the paper <a href="https://arxiv.org/abs/2202.09741" rel="nofollow">Visual Attention Network</a> by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.</li>
<li><strong><a href="model_doc/videomae">VideoMAE</a></strong> (from Multimedia Computing Group, Nanjing University) released with the paper <a href="https://arxiv.org/abs/2203.12602" rel="nofollow">VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training</a> by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.</li>
<li><strong><a href="model_doc/vilt">ViLT</a></strong> (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper <a href="https://arxiv.org/abs/2102.03334" rel="nofollow">ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision</a> by Wonjae Kim, Bokyung Son, Ildoo Kim.</li>
<li><strong><a href="model_doc/vit">Vision Transformer (ViT)</a></strong> (from Google AI) released with the paper <a href="https://arxiv.org/abs/2010.11929" rel="nofollow">An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale</a> by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.</li>
<li><strong><a href="model_doc/visual_bert">VisualBERT</a></strong> (from UCLA NLP) released with the paper <a href="https://arxiv.org/pdf/1908.03557" rel="nofollow">VisualBERT: A Simple and Performant Baseline for Vision and Language</a> by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.</li>
<li><strong><a href="model_doc/vit_mae">ViTMAE</a></strong> (from Meta AI) released with the paper <a href="https://arxiv.org/abs/2111.06377" rel="nofollow">Masked Autoencoders Are Scalable Vision Learners</a> by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.</li>
<li><strong><a href="model_doc/wav2vec2">Wav2Vec2</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2006.11477" rel="nofollow">wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations</a> by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.</li>
<li><strong><a href="model_doc/wav2vec2-conformer">Wav2Vec2-Conformer</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2010.05171" rel="nofollow">FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ</a> by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.</li>
<li><strong><a href="model_doc/wav2vec2_phoneme">Wav2Vec2Phoneme</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2109.11680" rel="nofollow">Simple and Effective Zero-shot Cross-lingual Phoneme Recognition</a> by Qiantong Xu, Alexei Baevski, Michael Auli.</li>
<li><strong><a href="model_doc/wavlm">WavLM</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2110.13900" rel="nofollow">WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing</a> by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.</li>
<li><strong><a href="model_doc/xglm">XGLM</a></strong> (From Facebook AI) released with the paper <a href="https://arxiv.org/abs/2112.10668" rel="nofollow">Few-shot Learning with Multilingual Language Models</a> by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.</li>
<li><strong><a href="model_doc/xlm">XLM</a></strong> (from Facebook) released together with the paper <a href="https://arxiv.org/abs/1901.07291" rel="nofollow">Cross-lingual Language Model Pretraining</a> by Guillaume Lample and Alexis Conneau.</li>
<li><strong><a href="model_doc/xlm-prophetnet">XLM-ProphetNet</a></strong> (from Microsoft Research) released with the paper <a href="https://arxiv.org/abs/2001.04063" rel="nofollow">ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training</a> by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.</li>
<li><strong><a href="model_doc/xlm-roberta">XLM-RoBERTa</a></strong> (from Facebook AI), released together with the paper <a href="https://arxiv.org/abs/1911.02116" rel="nofollow">Unsupervised Cross-lingual Representation Learning at Scale</a> by Alexis Conneau<em>, Kartikay Khandelwal</em>, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.</li>
<li><strong><a href="model_doc/xlm-roberta-xl">XLM-RoBERTa-XL</a></strong> (from Facebook AI), released together with the paper <a href="https://arxiv.org/abs/2105.00572" rel="nofollow">Larger-Scale Transformers for Multilingual Masked Language Modeling</a> by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.</li>
<li><strong><a href="model_doc/xlnet">XLNet</a></strong> (from Google/CMU) released with the paper <a href="https://arxiv.org/abs/1906.08237" rel="nofollow">XLNet: Generalized Autoregressive Pretraining for Language Understanding</a> by Zhilin Yang<em>, Zihang Dai</em>, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.</li>
<li><strong><a href="model_doc/xls_r">XLS-R</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2111.09296" rel="nofollow">XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale</a> by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.</li>
<li><strong><a href="model_doc/xlsr_wav2vec2">XLSR-Wav2Vec2</a></strong> (from Facebook AI) released with the paper <a href="https://arxiv.org/abs/2006.13979" rel="nofollow">Unsupervised Cross-Lingual Representation Learning For Speech Recognition</a> by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.</li>
<li><strong><a href="model_doc/yolos">YOLOS</a></strong> (from Huazhong University of Science & Technology) released with the paper <a href="https://arxiv.org/abs/2106.00666" rel="nofollow">You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection</a> by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.</li>
<li><strong><a href="model_doc/yoso">YOSO</a></strong> (from the University of Wisconsin - Madison) released with the paper <a href="https://arxiv.org/abs/2111.09714" rel="nofollow">You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling</a> by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.</li></ol>
<h3 class="relative group"><a id="untersttzte-frameworks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#untersttzte-frameworks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Unterstützte Frameworks
</span></h3>
<p>Die folgende Tabelle zeigt die derzeitige Unterstützung in der Bibliothek für jedes dieser Modelle, unabhängig davon, ob sie einen Python
Tokenizer haben (als “langsam” bezeichnet), ein “schneller” Tokenizer, der von der 🤗 Tokenizers Bibliothek unterstützt wird, ob sie Unterstützung in Jax (via
Flax), PyTorch, und/oder TensorFlow haben.</p>
<table><thead><tr><th align="center">Model</th>
<th align="center">Tokenizer slow</th>
<th align="center">Tokenizer fast</th>
<th align="center">PyTorch support</th>
<th align="center">TensorFlow support</th>
<th align="center">Flax Support</th></tr></thead>
<tbody><tr><td align="center">ALBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">BART</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">BEiT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">BERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Bert Generation</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">BigBird</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">BigBird-Pegasus</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Blenderbot</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">BlenderbotSmall</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">BLOOM</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">CamemBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">CANINE</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">CLIP</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">CodeGen</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ConvBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">ConvNeXT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">CTRL</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">CvT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Data2VecAudio</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Data2VecText</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Data2VecVision</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">DeBERTa</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">DeBERTa-v2</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Decision Transformer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">DeiT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">DETR</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">DistilBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">DPR</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">DPT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ELECTRA</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Encoder decoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">FairSeq Machine-Translation</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">FlauBERT</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">FLAVA</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">FNet</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Funnel Transformer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">GLPN</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">GPT Neo</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">GPT NeoX</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">GPT-J</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">GroupViT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Hubert</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">I-BERT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ImageGPT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">LayoutLM</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">LayoutLMv2</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">LayoutLMv3</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">LED</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">LeViT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Longformer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">LongT5</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">LUKE</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">LXMERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">M-CTC-T</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">M2M100</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Marian</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">MaskFormer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">mBART</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Megatron-BERT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">MobileBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">MobileViT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">MPNet</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">MT5</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">MVP</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Nezha</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Nyströmformer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">OpenAI GPT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">OpenAI GPT-2</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">OPT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">OWL-ViT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Pegasus</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Perceiver</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">PLBart</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">PoolFormer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ProphetNet</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">QDQBert</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RAG</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">REALM</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Reformer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RegNet</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">RemBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">ResNet</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">RetriBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">RoBERTa</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">RoFormer</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">SegFormer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">SEW</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">SEW-D</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Speech Encoder decoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">Speech2Text</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Speech2Text2</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Splinter</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">SqueezeBERT</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Swin Transformer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Swin Transformer V2</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">T5</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">TAPAS</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Trajectory Transformer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Transformer-XL</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">TrOCR</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">UniSpeech</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">UniSpeechSat</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">VAN</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">VideoMAE</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ViLT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">Vision Encoder decoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">VisionTextDualEncoder</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">VisualBERT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">ViT</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">ViTMAE</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">Wav2Vec2</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">Wav2Vec2-Conformer</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">WavLM</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">XGLM</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td></tr>
<tr><td align="center">XLM</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">XLM-ProphetNet</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">XLM-RoBERTa</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td></tr>
<tr><td align="center">XLM-RoBERTa-XL</td>
<td align="center">❌</td>
<td align="center">❌</td>
<td align="center">✅</td>
<td align="center">❌</td>
<td align="center">❌</td></tr>
<tr><td align="center">XLNet</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">✅</td>
<td align="center">❌</td></tr>
<tr><td align="center">YOLOS</td>
<td align="center">❌</td>
<td align="center">❌</td>
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<h1 class="relative group"><a id="ein-modell-teilen" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#ein-modell-teilen"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Ein Modell teilen
</span></h1>
<p>Die letzten beiden Tutorials haben gezeigt, wie man ein Modell mit PyTorch, Keras und 🤗 Accelerate für verteilte Setups feinabstimmen kann. Der nächste Schritt besteht darin, Ihr Modell mit der Community zu teilen! Bei Hugging Face glauben wir an den offenen Austausch von Wissen und Ressourcen, um künstliche Intelligenz für alle zu demokratisieren. Wir ermutigen Sie, Ihr Modell mit der Community zu teilen, um anderen zu helfen, Zeit und Ressourcen zu sparen.</p>
<p>In diesem Tutorial lernen Sie zwei Methoden kennen, wie Sie ein trainiertes oder verfeinertes Modell auf dem <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> teilen können:</p>
<ul><li>Programmgesteuertes Übertragen Ihrer Dateien auf den Hub.</li>
<li>Ziehen Sie Ihre Dateien per Drag-and-Drop über die Weboberfläche in den Hub.</li></ul>
<iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
<div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p>Um ein Modell mit der Öffentlichkeit zu teilen, benötigen Sie ein Konto auf <a href="https://huggingface.co/join" rel="nofollow">huggingface.co</a>. Sie können auch einer bestehenden Organisation beitreten oder eine neue Organisation gründen.</p></div>
<h2 class="relative group"><a id="repositoryfunktionen" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#repositoryfunktionen"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Repository-Funktionen
</span></h2>
<p>Jedes Repository im Model Hub verhält sich wie ein typisches GitHub-Repository. Unsere Repositorys bieten Versionierung, Commit-Historie und die Möglichkeit, Unterschiede zu visualisieren.</p>
<p>Die integrierte Versionierung des Model Hub basiert auf Git und <a href="https://git-lfs.github.com/" rel="nofollow">git-lfs</a>. Mit anderen Worten: Sie können ein Modell als ein Repository behandeln, was eine bessere Zugriffskontrolle und Skalierbarkeit ermöglicht. Die Versionskontrolle ermöglicht <em>Revisionen</em>, eine Methode zum Anheften einer bestimmten Version eines Modells mit einem Commit-Hash, Tag oder Branch.</p>
<p>Folglich können Sie eine bestimmte Modellversion mit dem Parameter “Revision” laden:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model = AutoModel.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"julien-c/EsperBERTo-small"</span>, revision=<span class="hljs-string">"v2.0.1"</span> <span class="hljs-comment"># tag name, or branch name, or commit hash</span>
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können die Commit-Historie sowie die Unterschiede einsehen:</p>
<p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vis_diff.png" alt="vis_diff"></p>
<h2 class="relative group"><a id="einrichtung" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#einrichtung"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Einrichtung
</span></h2>
<p>Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig <code>~/.cache/</code>) gespeichert:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->huggingface-cli login<!-- HTML_TAG_END --></pre></div>
<p>Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die <a href="https://huggingface.co/docs/hub/adding-a-library" rel="nofollow"><code>huggingface_hub</code></a> Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub.</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install huggingface_hub<!-- HTML_TAG_END --></pre></div>
<p>Verwenden Sie dann <code>notebook_login</code>, um sich beim Hub anzumelden, und folgen Sie dem Link <a href="https://huggingface.co/settings/token" rel="nofollow">hier</a>, um ein Token für die Anmeldung zu generieren:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login
<span class="hljs-meta">>>> </span>notebook_login()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="ein-modell-fr-alle-frameworks-konvertieren" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#ein-modell-fr-alle-frameworks-konvertieren"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Ein Modell für alle Frameworks konvertieren
</span></h2>
<p>Um sicherzustellen, dass Ihr Modell von jemandem verwendet werden kann, der mit einem anderen Framework arbeitet, empfehlen wir Ihnen, Ihr Modell sowohl mit PyTorch- als auch mit TensorFlow-Checkpoints zu konvertieren und hochzuladen. Während Benutzer immer noch in der Lage sind, Ihr Modell von einem anderen Framework zu laden, wenn Sie diesen Schritt überspringen, wird es langsamer sein, weil 🤗 Transformers den Checkpoint on-the-fly konvertieren müssen.</p>
<p>Die Konvertierung eines Checkpoints für ein anderes Framework ist einfach. Stellen Sie sicher, dass Sie PyTorch und TensorFlow installiert haben (siehe <a href="installation">hier</a> für Installationsanweisungen), und finden Sie dann das spezifische Modell für Ihre Aufgabe in dem anderen Framework. </p>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<p>Geben Sie <code>from_tf=True</code> an, um einen Prüfpunkt von TensorFlow nach PyTorch zu konvertieren:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">"path/to/awesome-name-you-picked"</span>, from_tf=<span class="hljs-literal">True</span>)
<span class="hljs-meta">>>> </span>pt_model.save_pretrained(<span class="hljs-string">"path/to/awesome-name-you-picked"</span>)<!-- HTML_TAG_END --></pre></div></div></div>
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<span>TensorFlow</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Geben Sie <code>from_pt=True</code> an, um einen Prüfpunkt von PyTorch nach TensorFlow zu konvertieren:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">"path/to/awesome-name-you-picked"</span>, from_pt=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Dann können Sie Ihr neues TensorFlow-Modell mit seinem neuen Checkpoint speichern:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_model.save_pretrained(<span class="hljs-string">"path/to/awesome-name-you-picked"</span>)<!-- HTML_TAG_END --></pre></div>
</div></div>
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stroke: #dce0df;
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<span>JAX</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide JAX content</span></div></div>
<div class="framework-content">
<p>Wenn ein Modell in Flax verfügbar ist, können Sie auch einen Kontrollpunkt von PyTorch nach Flax konvertieren:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span> <span class="hljs-string">"path/to/awesome-name-you-picked"</span>, from_pt=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
</div></div></div>
<h2 class="relative group"><a id="ein-modell-whrend-des-trainings-hochladen" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#ein-modell-whrend-des-trainings-hochladen"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Ein Modell während des Trainings hochladen
</span></h2>
<div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4">
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg>
<span>Pytorch</span></div>
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<span>Hide Pytorch content</span></div></div>
<div class="framework-content">
<iframe class="w-full xl:w-4/6 h-80" src="https://www.youtube-nocookie.com/embed/Z1-XMy-GNLQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p>Die Weitergabe eines Modells an den Hub ist so einfach wie das Hinzufügen eines zusätzlichen Parameters oder Rückrufs. Erinnern Sie sich an das <a href="training">Feinabstimmungs-Tutorial</a>, in der Klasse <code>TrainingArguments</code> geben Sie Hyperparameter und zusätzliche Trainingsoptionen an. Eine dieser Trainingsoptionen beinhaltet die Möglichkeit, ein Modell direkt an den Hub zu pushen. Setzen Sie <code>push_to_hub=True</code> in Ihrer <code>TrainingArguments</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>training_args = TrainingArguments(output_dir=<span class="hljs-string">"my-awesome-model"</span>, push_to_hub=<span class="hljs-literal">True</span>)<!-- HTML_TAG_END --></pre></div>
<p>Übergeben Sie Ihre Trainingsargumente wie gewohnt an <code>Trainer</code>:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>trainer = Trainer(
<span class="hljs-meta">... </span> model=model,
<span class="hljs-meta">... </span> args=training_args,
<span class="hljs-meta">... </span> train_dataset=small_train_dataset,
<span class="hljs-meta">... </span> eval_dataset=small_eval_dataset,
<span class="hljs-meta">... </span> compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Nach der Feinabstimmung Ihres Modells rufen Sie <code>push_to_hub()</code> auf <code>Trainer</code> auf, um das trainierte Modell an den Hub zu übertragen. Transformers fügt sogar automatisch Trainings-Hyperparameter, Trainingsergebnisse und Framework-Versionen zu Ihrer Modellkarte hinzu!</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>trainer.push_to_hub()<!-- HTML_TAG_END --></pre></div></div></div>
<div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg>
<span>TensorFlow</span></div>
<div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg>
<span>Hide TensorFlow content</span></div></div>
<div class="framework-content">
<p>Geben Sie ein Modell mit <code>PushToHubCallback</code> an den Hub weiter. In der <code>PushToHubCallback</code> Funktion, fügen Sie hinzu:</p>
<ul><li>Ein Ausgabeverzeichnis für Ihr Modell.</li>
<li>Einen Tokenizer.</li>
<li>Die <code>hub_model_id</code>, die Ihr Hub-Benutzername und Modellname ist.</li></ul>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.keras.callbacks <span class="hljs-keyword">import</span> PushToHubCallback
<span class="hljs-meta">>>> </span>push_to_hub_callback = PushToHubCallback(
<span class="hljs-meta">... </span> output_dir=<span class="hljs-string">"./your_model_save_path"</span>, tokenizer=tokenizer, hub_model_id=<span class="hljs-string">"your-username/my-awesome-model"</span>
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<p>Fügen Sie den Callback zu <a href="https://keras.io/api/models/model_training_apis/" rel="nofollow"><code>fit</code></a> hinzu, und 🤗 Transformers wird das trainierte Modell an den Hub weiterleiten:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=<span class="hljs-number">3</span>, callbacks=push_to_hub_callback)<!-- HTML_TAG_END --></pre></div>
</div></div>
</div>
<h2 class="relative group"><a id="verwenden-sie-die-funktion-pushtohub" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#verwenden-sie-die-funktion-pushtohub"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Verwenden Sie die Funktion <code>push_to_hub</code>.
</span></h2>
<p>Sie können <code>push_to_hub</code> auch direkt für Ihr Modell aufrufen, um es in den Hub hochzuladen.</p>
<p>Geben Sie den Namen Ihres Modells in “push_to_hub” an:</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_model.push_to_hub(<span class="hljs-string">"my-awesome-model"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Dadurch wird ein Repository unter Ihrem Benutzernamen mit dem Modellnamen <code>my-awesome-model</code> erstellt. Benutzer können nun Ihr Modell mit der Funktion <code>from_pretrained</code> laden:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel
<span class="hljs-meta">>>> </span>model = AutoModel.from_pretrained(<span class="hljs-string">"your_username/my-awesome-model"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Wenn Sie zu einer Organisation gehören und Ihr Modell stattdessen unter dem Namen der Organisation pushen wollen, fügen Sie diesen einfach zur <code>repo_id</code> hinzu:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>pt_model.push_to_hub(<span class="hljs-string">"my-awesome-org/my-awesome-model"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Die Funktion “push_to_hub” kann auch verwendet werden, um andere Dateien zu einem Modell-Repository hinzuzufügen. Zum Beispiel kann man einen Tokenizer zu einem Modell-Repository hinzufügen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tokenizer.push_to_hub(<span class="hljs-string">"my-awesome-model"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Oder vielleicht möchten Sie die TensorFlow-Version Ihres fein abgestimmten PyTorch-Modells hinzufügen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
<div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>tf_model.push_to_hub(<span class="hljs-string">"my-awesome-model"</span>)<!-- HTML_TAG_END --></pre></div>
<p>Wenn Sie nun zu Ihrem Hugging Face-Profil navigieren, sollten Sie Ihr neu erstelltes Modell-Repository sehen. Wenn Sie auf die Registerkarte <strong>Dateien</strong> klicken, werden alle Dateien angezeigt, die Sie in das Repository hochgeladen haben.</p>
<p>Weitere Einzelheiten zum Erstellen und Hochladen von Dateien in ein Repository finden Sie in der Hub-Dokumentation <a href="https://huggingface.co/docs/hub/how-to-upstream" rel="nofollow">hier</a>.</p>
<h2 class="relative group"><a id="hochladen-mit-der-weboberflche" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#hochladen-mit-der-weboberflche"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Hochladen mit der Weboberfläche
</span></h2>
<p>Benutzer, die einen no-code Ansatz bevorzugen, können ein Modell über das Webinterface des Hubs hochladen. Besuchen Sie <a href="https://huggingface.co/new" rel="nofollow">huggingface.co/new</a> um ein neues Repository zu erstellen:</p>
<p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_model_repo.png" alt="new_model_repo"></p>
<p>Fügen Sie von hier aus einige Informationen über Ihr Modell hinzu:</p>
<ul><li>Wählen Sie den <strong>Besitzer</strong> des Repositorys. Dies können Sie selbst oder eine der Organisationen sein, denen Sie angehören.</li>
<li>Wählen Sie einen Namen für Ihr Modell, der auch der Name des Repositorys sein wird.</li>
<li>Wählen Sie, ob Ihr Modell öffentlich oder privat ist.</li>
<li>Geben Sie die Lizenzverwendung für Ihr Modell an.</li></ul>
<p>Klicken Sie nun auf die Registerkarte <strong>Dateien</strong> und klicken Sie auf die Schaltfläche <strong>Datei hinzufügen</strong>, um eine neue Datei in Ihr Repository hochzuladen. Ziehen Sie dann eine Datei per Drag-and-Drop hoch und fügen Sie eine Übergabemeldung hinzu.</p>
<p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/upload_file.png" alt="upload_file"></p>
<h2 class="relative group"><a id="hinzufgen-einer-modellkarte" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#hinzufgen-einer-modellkarte"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Hinzufügen einer Modellkarte
</span></h2>
<p>Um sicherzustellen, dass die Benutzer die Fähigkeiten, Grenzen, möglichen Verzerrungen und ethischen Aspekte Ihres Modells verstehen, fügen Sie bitte eine Modellkarte zu Ihrem Repository hinzu. Die Modellkarte wird in der Datei <code>README.md</code> definiert. Sie können eine Modellkarte hinzufügen, indem Sie:</p>
<ul><li>Manuelles Erstellen und Hochladen einer “README.md”-Datei.</li>
<li>Klicken Sie auf die Schaltfläche <strong>Modellkarte bearbeiten</strong> in Ihrem Modell-Repository.</li></ul>
<p>Werfen Sie einen Blick auf die DistilBert <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">model card</a> als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei “README.md” einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation <a href="https://huggingface.co/docs/hub/models-cards" rel="nofollow">hier</a>.</p>
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<h1 class="relative group"><a id="verteiltes-training-mit-accelerate" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#verteiltes-training-mit-accelerate"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Verteiltes Training mit 🤗 Accelerate
</span></h1>
<p>Da die Modelle immer größer werden, hat sich die Parallelität als Strategie zum Trainieren größerer Modelle auf begrenzter Hardware und zur Beschleunigung der Trainingsgeschwindigkeit um mehrere Größenordnungen erwiesen. Bei Hugging Face haben wir die Bibliothek <a href="https://huggingface.co/docs/accelerate" rel="nofollow">🤗 Accelerate</a> entwickelt, um Nutzern zu helfen, ein 🤗 Transformers-Modell auf jeder Art von verteiltem Setup zu trainieren, egal ob es sich um mehrere GPUs auf einer Maschine oder mehrere GPUs auf mehreren Maschinen handelt. In diesem Tutorial lernen Sie, wie Sie Ihre native PyTorch-Trainingsschleife anpassen, um das Training in einer verteilten Umgebung zu ermöglichen.</p>
<h2 class="relative group"><a id="einrichtung" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#einrichtung"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Einrichtung
</span></h2>
<p>Beginnen Sie mit der Installation von 🤗 Accelerate:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->pip install accelerate<!-- HTML_TAG_END --></pre></div>
<p>Dann importieren und erstellen Sie ein <code>Accelerator</code>-Objekt. Der <code>Accelerator</code> wird automatisch Ihre Art der verteilten Einrichtung erkennen und alle notwendigen Komponenten für das Training initialisieren. Sie müssen Ihr Modell nicht explizit auf einem Gerät platzieren.</p>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> Accelerator
<span class="hljs-meta">>>> </span>accelerator = Accelerator()<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="vorbereiten-auf-die-beschleunigung" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#vorbereiten-auf-die-beschleunigung"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Vorbereiten auf die Beschleunigung
</span></h2>
<p>Der nächste Schritt ist die Übergabe aller relevanten Trainingsobjekte an die Methode <code>prepare</code>. Dazu gehören Ihre Trainings- und Evaluierungs-DataLoader, ein Modell und ein Optimierer:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
<span class="hljs-meta">... </span> train_dataloader, eval_dataloader, model, optimizer
<span class="hljs-meta">... </span>)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="rckwrts" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#rckwrts"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Rückwärts
</span></h2>
<p>Die letzte Ergänzung besteht darin, das typische <code>loss.backward()</code> in der Trainingsschleife durch die 🤗 Accelerate-Methode <code>backward</code> zu ersetzen:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">for</span> epoch <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_epochs):
<span class="hljs-meta">... </span> <span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> train_dataloader:
<span class="hljs-meta">... </span> outputs = model(**batch)
<span class="hljs-meta">... </span> loss = outputs.loss
<span class="hljs-meta">... </span> accelerator.backward(loss)
<span class="hljs-meta">... </span> optimizer.step()
<span class="hljs-meta">... </span> lr_scheduler.step()
<span class="hljs-meta">... </span> optimizer.zero_grad()
<span class="hljs-meta">... </span> progress_bar.update(<span class="hljs-number">1</span>)<!-- HTML_TAG_END --></pre></div>
<p>Wie Sie im folgenden Code sehen können, müssen Sie nur vier zusätzliche Codezeilen zu Ihrer Trainingsschleife hinzufügen, um verteiltes Training zu ermöglichen!</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-addition">+ from accelerate import Accelerator</span>
from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
<span class="hljs-addition">+ accelerator = Accelerator()</span>
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
optimizer = AdamW(model.parameters(), lr=3e-5)
<span class="hljs-deletion">- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")</span>
<span class="hljs-deletion">- model.to(device)</span>
<span class="hljs-addition">+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(</span>
<span class="hljs-addition">+ train_dataloader, eval_dataloader, model, optimizer</span>
<span class="hljs-addition">+ )</span>
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
<span class="hljs-deletion">- batch = {k: v.to(device) for k, v in batch.items()}</span>
outputs = model(**batch)
loss = outputs.loss
<span class="hljs-deletion">- loss.backward()</span>
<span class="hljs-addition">+ accelerator.backward(loss)</span>
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)<!-- HTML_TAG_END --></pre></div>
<h2 class="relative group"><a id="trainieren" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainieren"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainieren
</span></h2>
<p>Sobald Sie die entsprechenden Codezeilen hinzugefügt haben, starten Sie Ihr Training in einem Skript oder einem Notebook wie Colaboratory.</p>
<h3 class="relative group"><a id="trainieren-mit-einem-skript" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainieren-mit-einem-skript"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainieren mit einem Skript
</span></h3>
<p>Wenn Sie Ihr Training mit einem Skript durchführen, führen Sie den folgenden Befehl aus, um eine Konfigurationsdatei zu erstellen und zu speichern:</p>
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<pre><!-- HTML_TAG_START -->accelerate config<!-- HTML_TAG_END --></pre></div>
<p>Dann starten Sie Ihr Training mit:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START -->accelerate launch train.py<!-- HTML_TAG_END --></pre></div>
<h3 class="relative group"><a id="trainieren-mit-einem-notebook" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#trainieren-mit-einem-notebook"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
<span>Trainieren mit einem Notebook
</span></h3>
<p>🤗 Accelerate kann auch in einem Notebook laufen, wenn Sie planen, die TPUs von Colaboratory zu verwenden. Verpacken Sie den gesamten Code, der für das Training verantwortlich ist, in eine Funktion und übergeben Sie diese an <code>notebook_launcher</code>:</p>
<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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<pre><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> accelerate <span class="hljs-keyword">import</span> notebook_launcher
<span class="hljs-meta">>>> </span>notebook_launcher(training_function)<!-- HTML_TAG_END --></pre></div>
<p>Weitere Informationen über 🤗 Accelerate und seine umfangreichen Funktionen finden Sie in der <a href="https://huggingface.co/docs/accelerate" rel="nofollow">Dokumentation</a>.</p>
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